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Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. + diff --git a/NOTICE.md b/NOTICE.md new file mode 100644 index 0000000000000000000000000000000000000000..a2e72e872a2c1a5be9f339ee4696e69227fe50f0 --- /dev/null +++ b/NOTICE.md @@ -0,0 +1,15 @@ +# License and attribution + +Author: **Christoph Schuhmann**. Organization: **LAION**. Release: 7 October 2026. +LAION's new fine-tuned weights, code and documentation: **CC BY 4.0**, see `LICENSE`. +Please credit Christoph Schuhmann and LAION, link this repository and indicate changes. + +OpenAI Whisper's original code and weights retain their MIT notice: +https://github.com/openai/whisper/blob/main/LICENSE +Hugging Face Transformers is an external Apache-2.0 dependency: +https://github.com/huggingface/transformers/blob/main/LICENSE +Teacher models and source audio retain their own terms; they are not redistributed here. +Orange's identity teacher card declares CC BY-SA 3.0: +https://huggingface.co/Orange/Speaker-wavLM-id +Orange timbre: https://huggingface.co/Orange/Speaker-wavLM-tbr +The release license is not a grant of rights over upstream datasets or packages. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4cec5a8c07b05fae2d0acf176674d1dc1b4672f9 --- /dev/null +++ b/README.md @@ -0,0 +1,731 @@ +--- +license: cc-by-4.0 +library_name: pytorch +pipeline_tag: audio-classification +base_model: +- openai/whisper-base +- openai/whisper-small +- laion/whisper-base-small-layered-audio-scores +base_model_relation: finetune +datasets: +- laion/tts-scaling-ladder-de-en +- laion/laions_got_talent_raw +- mitermix/balanced_audio_snippets_40x3k +- mitermix/voice-annotation-poc +language: +- multilingual +tags: +- whisper +- encoder-only +- emotion-recognition +- vocal-burst-localization +- speaker-embeddings +- audio-quality +- multitask-regression +- gemini-distillation +--- + +# Gemini-tuned Whisper Base and Small for emotion, voice, quality and vocal bursts + +**By Christoph Schuhmann · LAION · 7 October 2026 · CC BY 4.0** + +This repository releases the **best completed two-epoch Gemini Flash 3.8 fine-tunes** +of LAION's encoder-only Whisper Base and Small multitask models. Given a recording, +one forward pass predicts emotions, speaking style, sound quality, speaker-characteristic +vectors and the location and type of non-speech vocal events such as laughter or crying. +Both models are included: `base/model.safetensors` and `small/model.safetensors`. + +**These are audio encoders with task heads. There is no Whisper text decoder, ASR +transcript, generated caption or language model.** Gemini transcripts and captions helped +prepare annotations; captions were not model inputs or an autoregressive training objective. +CPS is a scalar prediction rather than a transcript. The requested separate MOSS caption +LoRAs are outside this release and were not trained as part of these two runs. + +## Results and related releases + +- **[Comprehensive English benchmark report](https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html)**: + all 36 frozen embedding probes and four original/tuned Whisper configurations; + human-label emotion benchmarks, complete target regression, validation, burst timing and limitations. +- [Which model is best for each task?](https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html#study-overview) + includes genuineness and vocal-burst blend. +- [Every target and validation result](https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html#study-targets). +- [Original layered Whisper weights](https://huggingface.co/laion/whisper-base-small-layered-audio-scores): + the Base **S4** and Small **S3** checkpoints are the actual starting states for this release. + +**Small** has better Orange speaker-vector cosine and lower AudioBox/DNSMOS target error +than Base, and a higher EmoNet intensity correlation. **Base** has slightly better CPS error, +Flash frame F1 and VoiceNet-Emo mean rank correlation. A frozen probe can outperform both +on other tasks: see the comprehensive report for all winners. This release does not claim +one universal best model or statistical significance from small numerical differences. + +## What the models output + +| Output | Shape / scale | Meaning | +| --- | --- | --- | +| EmoNet emotions | 40 scalars, reference 0–4 | Fine-grained emotion intensity; simultaneous regression, not a mutually exclusive class. | +| VoiceNet dimensions | 57 scalars, mostly 0–6 | Delivery, register, timbre and style; Flash BKGN is 0–4 and EXPL 0–2. | +| Genuineness | 1 scalar, 0–6 | The annotation rubric's perceived genuineness. | +| Vocal-burst blend | 1 scalar, 0–10 | How a vocal event blends with surrounding speech; meaningful only when a burst is present. | +| R_quality | 1 legacy scalar | Its distinct original teacher target and units; not overwritten with another quality construct. | +| Empathic Insight Plus extras | 19 scalars | Voice traits, arousal/valence, recording/background quality and content enjoyment. | +| AudioBox Aesthetics | 4 scalars | CE: content enjoyment; CU: content usefulness; PC: production complexity; PQ: production quality. | +| DNSMOS | 7 scalars | SIG, BAK, OVRL, their raw versions and P808 MOS. | +| Burst count | 1 scalar trained in log1p space | Number of annotated events; inference additionally reports expm1 of the nonnegative prediction. | +| VoiceCLAP attributes | 61 scalars | Additional emotion, voice and quality targets; some emotion targets overlap the first 40. | +| Characters per second (CPS) | 1 separate scalar | Unicode transcript characters, including spaces/punctuation, divided by total recording duration; bracketed nonlexical placeholders removed. | +| Orange timbre | 128D unit vector | Regression to the Orange timbre teacher embedding. | +| Orange identity | 250D unit vector | Regression to the Orange identity teacher embedding; not a person's name or an identification guarantee. | +| Burst occupancy | 20 ms frame probabilities | Whether each encoder frame belongs to at least one vocal burst. | +| Independent burst events | Start **and** end seconds | Onset plus duration proposals can overlap; maximum 32 at the default evaluation setting. | +| Burst type | 53-way event-local class probabilities | The top three labels and uncalibrated softmax probabilities for each predicted interval. | + +There are **192 jointly predicted scalar outputs plus the separate CPS head**. A complete, +ordered list of exact output keys and all their held-out results appears at the end of this card. +The models return raw units, training-standardized values and display z-scores. Regression +outputs are continuous and are not automatically clipped to the annotation's ordinal range. + +## Quick start: local CPU or GPU inference + +Use Python 3.10 or newer. The measured production environment used Python 3.13.5, +PyTorch 2.9.1, Transformers 5.14.1, SoundFile 0.14 and Safetensors. Install the appropriate +PyTorch CPU/CUDA wheel for your machine; see `requirements.txt` for the other dependencies. +Inference uses a custom `model.py`, not `pipeline('automatic-speech-recognition')` or +`WhisperForConditionalGeneration.from_pretrained`. + +Download inference assets without the large optimizer checkpoints: + +```python +from huggingface_hub import snapshot_download + +snapshot_download( + repo_id="laion/whisper-base-small-gemini-audio-scores", + local_dir="whisper-gemini", + allow_patterns=["*.py", "requirements.txt", "*.json", "base/*.json", + "small/*.json", "base/model.safetensors", "small/model.safetensors"], +) +``` + +```bash +cd whisper-gemini +pip install -r requirements.txt +python inference.py clip.wav --model small --device cpu --threads 4 > small_prediction.json +python inference.py clip.wav --model base --device cpu --threads 1 > base_prediction.json +python inference.py clip.wav --model small --device cuda:0 --threads 4 \ + --include-frame-probabilities > gpu_prediction.json +``` + +Input recordings must be **0.1–30 seconds**. SoundFile decodes the supplied audio, stereo is +averaged to mono, and SciPy resamples to 16 kHz. The bundled Whisper frontend creates +80-bin log-mel features. Longer recordings need an explicit external chunking policy. +No original OpenAI weights, teacher checkpoints or Gemini API access are required at inference. + +Reuse the model when processing multiple files: + +```python +from inference import Predictor + +predictor = Predictor(".", model_size="small", device="cpu", threads=4) +result = predictor.predict("clip.wav", include_frames=True) +print(result["scores_raw"]["genuineness_0_6"]) +print(result["scores_z"]["emo_Amusement"]) +print(result["burst_event_proposals"]) # start_s, end_s, top3_classes +``` + +`--frame-threshold` and `--event-threshold` default to 0.5; `--max-events` defaults to 32. +This matches the new report's proposal operating point. Lowering the event threshold can +increase recall and false positives. Frame regions merge overlapping events, whereas the +independent proposal head can represent them separately. Neither class softmax values nor +sigmoid values have been calibrated as real-world confidence estimates. + +## Architecture + +Base uses **six encoder layers, hidden width 512**, and **26,441,735 total parameters**. +Small uses **12 encoder layers, hidden width 768**, and **97,102,031 total parameters**. +These counts include our heads and exclude the absent Whisper decoder. The encoder alone +contains 20,590,592 / 88,154,112 parameters respectively. + +From the final encoder sequence we concatenate masked mean, minimum, maximum and standard +deviation. Separately, each transformer layer contributes its masked mean vector. Shared +per-family projection MLPs reduce these means to 32 or 64 dimensions, and a sample-specific +softmax gate combines layer information. These features add residuals to the 192-score head. +Speaker heads use 128D layer-mixture routes; CPS uses 32D. The frame head predicts occupancy; +the proposal head predicts independent onset logits and log durations. A 64D layer-mixture +route for event classification averages **only frames inside that event** from every layer, +then combines them with the final-layer local mean and a 53-way classifier. + +`model.py` is the exact architecture used in training. Exported weights preserve every saved +tensor without quantization or conversion. The inherited Whisper configuration still contains +some unused decoder fields; `architectures` and `is_encoder_decoder` are set for this encoder-only +release. No decoder tensors are present. The custom inference code must be used. + +## Training data, annotation priority and splits + +The selected pool contains **66,199 exact-audio unique clips / 197.36 hours** after deduplication +across the four selections. Valid Flash annotations exist for **65,163 clips / 194.17 hours**. +The 1,036 invalid or provider-blocked responses are excluded from this tuning stage. They were +not converted into zero-emotion or no-burst targets. + +| Split actually used | Clips | Purpose | +| --- | ---: | --- | +| Training | 58,380 | Gradient updates for both full fine-tunes. | +| Validation | 3,239 | Full validation after each epoch; selects best checkpoint. | +| Test | 3,544 | Matched before/after evaluation; not used for gradient updates or epoch selection. | + +Sources are the **balanced S1 100-hour, 400-bucket selection** from +[TTS Scaling Ladder DE/EN](https://huggingface.co/datasets/laion/tts-scaling-ladder-de-en), +[LAION's Got Talent raw](https://huggingface.co/datasets/laion/laions_got_talent_raw), +[balanced audio snippets 40×3k](https://huggingface.co/datasets/mitermix/balanced_audio_snippets_40x3k) +and [voice annotation POC](https://huggingface.co/datasets/mitermix/voice-annotation-poc). +Final selections preserve all source/task memberships while annotating each exact compressed-audio +SHA256 once. Source membership counts are nonexclusive. Deterministic 90/5/5 hashing groups +explicit speaker IDs, otherwise known synthetic audio families, otherwise exact audio hashes; +observed counts differ from exact percentages because groups are kept intact. + +`training/provenance/valid_split_summary.json` records valid counts, hours, language strings, +memberships and the hash of the exact prepared target snapshot. The older `TRAINING_READY.json` +also contains split counts including invalid responses; those are not the training counts above. +Source revisions and original pool details are in `training/provenance/selected_pool.json`. +The data, Gemini responses and teacher TARs are not redistributed by this model repository. + +The recorded annotation model identifier is **`gemini-3.8-flash`**. Schema-valid Flash outputs +take priority for 40 emotions, 57 VoiceNet dimensions, genuineness, blend, event counts, +event start/end times and transcript-derived CPS. Corresponding VoiceCLAP emotion attributes +also take the Flash value. Distinct constructs such as DNSMOS BAK or the original authenticity +axis keep their own teachers. A null blend on a no-burst clip is masked, not replaced by an old +blend score. Missing values remain masked. Speech-only objectives use domain masks; Orange +speaker losses require valid known single-speaker clips. Teacher vectors are preserved even +when their training loss is masked. + +The prompt permits 78 vocal fine labels. Explicit aliases map compatible labels into the +existing 53-class checkpoint vocabulary; unrepresented labels map to `Other / rare burst`. +See `gemini_burst_mapping.json` for every mapping. Original fine labels and event captions remain +in the annotation sidecars. End times may be clamped to the decoded duration within the accepted +0.3-second annotation tolerance, with original timing and the clamp flag retained in the data. + +## Exact fine-tuning procedure + +Starting models were the lowest-validation-loss **Base S4** and **Small S3** snapshots from +the original S1→S10 layered curriculum campaign. Each snapshot contains only the progress up +to its own stage, rather than the final S10 weights: Base's original update was 266,238; +Small's was 237,434. The original campaign ran one epoch per nested stage with 10% P3 synthetic +mixture exposures per stage and a continuous cosine schedule. Its full code and recorded run +configs are included under `training/` and `training/original_curriculum/`. + +Gemini tuning **strictly restored all encoder and head weights**, started a fresh AdamW +optimizer and cosine schedule, and unfroze the entire encoder, including its positional +embedding table. This is full fine-tuning, not LoRA or a frozen-encoder probe. + +| Setting | Base | Small | +| --- | ---: | ---: | +| Epochs | 2 | 2 | +| Actual optimizer updates | 914 | 914 | +| Best checkpoint | Epoch 2 | Epoch 2 | +| Peak encoder learning rate | 1e-5 | 5e-6 | +| Peak head learning rate | 1e-4 | 1e-4 | +| Warmup | 5% (46 updates) | 5% (46 updates) | +| Cosine minimum / peak | 10% | 10% | +| Weight decay | 0.01 | 0.01 | +| Global effective batch | 128 | 128 | +| Microbatch / GPU | 32 | 16 | +| Accumulation | 1 | 2 | +| Data workers / GPU | 4 | 4 | +| Hardware | 1 node, 4 GH200 GPUs | 1 node, 4 GH200 GPUs | +| Precision | BF16 autocast; FP32 saved weights | BF16 autocast; FP32 saved weights | +| Gradient clipping | Global norm 1.0 | Global norm 1.0 | +| Seed | 20261005 | 20261005 | +| Selected class-weighted validation loss | 1.7683769 | 1.6989245 | +| Training Slurm job | 2199189 | 2199190 | +| Recorded allocation elapsed time | 10m17s | 10m31s | + +There is **no P3 replay in this extra two-epoch stage**. A DDP sampler may pad a few examples +to distribute batches equally; validation is evaluated completely on rank zero without +duplicating its samples. The full config, class counts and epoch loss logs are under `training/`. + +### Losses + +The implementation is `training/train_layered_curriculum.py::loss_terms`, reused unchanged +by `training/gemini_finetune/train_whisper.py`: + +- Masked Huber regression, delta 1, on standardized scalar targets; per-family weights + are the exact `GROUPS` constants in `training/multitask_p3_smoke.py`. +- CPS: standardized Huber delta 1, weight 0.2. +- Speaker vectors: 0.2 × (cosine distance + 0.1 × Huber delta 0.1), only valid single speakers. +- Frames: weighted BCE (positive weight 5), weight 0.5, plus soft Dice weight 0.25. +- Onsets: weighted BCE (positive weight 100), weight 0.2. +- Event log duration: Huber delta 0.5, weight 0.1, evaluated at labeled starts. +- Event class: categorical cross entropy, weight 0.2, label smoothing 0.02; + training-only class weights sqrt(median event count / class count), clipped to 0.25–4. + +Every epoch saves latest and, when improved, best states. Published `base/checkpoint.pt` +and `small/checkpoint.pt` are the exact best files: model, AdamW, scheduler, four-rank RNG, +completed epoch, update, normalization, taxonomy and original config. They support epoch-boundary +continuation with the same DDP world size. Both runs already finished epoch 2; extending training +requires an explicit new phase/schedule, not blindly resuming the completed two-epoch plan. +Inference uses only Safetensors and never loads the `.pt` training pickle. + +## Normalization + +```text +raw_score = standardized_network_output × training_std + training_mean +display_z = (raw_score − original_training_teacher_median) / original_training_teacher_std +CPS_raw = CPS_network_output × CPS_training_std + CPS_training_mean +``` + +`training_normalization.json` is the original frozen checkpoint normalization, preserved +through Gemini tuning. It is required for correct raw units. `display_normalization.json` +is the train-only median/std reference from the original release, retained for compatible +visualization and rankings. `gemini_train_statistics.json` separately stores train-only Flash +mean/std/counts; its inherited “provisional” description refers to the incremental parser, +but the file here is the completed final export. It was not substituted into network decoding. +Display z-scores are relative context, not class probabilities or confidence intervals. + +## How the data were mounted and how to reproduce + +Training ran on **JUPITER Booster**. `/e/home`, `/e/project1`, `/e/scratch` and `/e/software` +were existing shared HPC filesystems visible to every rank; no Docker bind mount or network +download occurred inside the GPU training. Source audio and same-audio teacher outputs stayed +in WebDataset TARs. `TarReader` caches a bounded set of open archives and reads named members; +it does not extract millions of individual files. Teachers are cached annotations, not called +inside the gradient loop. + +| Original absolute path | Contents / use | +| --- | --- | +| `/e/home/jusers/schuhmann1/jupiter/whisper_score_regression` | Production training and inference source. | +| `/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model` | Original Base S4 / Small S3 states, frontend, normalization and classes. | +| `/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_multisource_100h_20261004` | Combined exact-audio selection and Gemini response sidecars. | +| `/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/prepared` | `targets.jsonl`, final training gate, normalization and burst mapping. | +| Same preparation root, `teacher_sidecars/` | SHA-bound TAR companions with Orange vectors and all additional teacher outputs. | +| Same preparation root, `training/whisper_{base,small}` | Exact best/latest states and per-epoch metrics. | +| `/e/scratch/reformo/schuhmann1_moss/code/fastgen.sh` | Python/software paths for the original offline HPC environment. | + +`targets.jsonl` contains `audio_tar` / `audio_member` and embedding `tar` / `member` references. +On another machine, obtain the same complete prepared data, copy TARs and rewrite the absolute +TAR prefixes to your local mounts while preserving member names and audio hashes. Provide +`checkpoint_normalization.json`, `gemini_burst_mapping.json` and `TRAINING_READY.json` beside it. +The repository includes the parser, schema prompt and teacher backfill code; recreating its +data requires the upstream datasets, teacher weights and your own annotation service access. +There is no credential embedded here. Source availability and terms still apply. + +To reproduce the **two-epoch experiment**, download the original Base S4 or Small S3 `.pt` +initializer from the previous repository, not this release's already-tuned state. Then: + +```bash +pip install -r requirements-training.txt +python training/make_finetune_config.py --model base \ + --data-root /your/prepared --initial-checkpoint /your/original/base/S4/checkpoint.pt \ + --output-dir /your/new-base-run --config /your/base-run.json +python -m torch.distributed.run --standalone --nproc_per_node=4 \ + training/gemini_finetune/train_whisper.py --config /your/base-run.json +``` + +For Small use `--model small` and the original `small/S3/checkpoint.pt`. The config helper +changes paths while preserving recorded hyperparameters. `training/gemini_finetune/train_whisper.sbatch` +is the original submitted Slurm launcher, with original mount paths for reference. Evaluator +scripts likewise preserve original study paths; adapt `study_paths.py` and benchmark data paths +if reproducing the full comparison. Safe-weight export tools and verification receipts are included. + +## Held-out Flash target agreement + +Both models are tested on the **same 3,544 valid Flash test clips**. These targets are machine +annotations, not a fresh human panel. Speaker cosine uses 1,718 valid single-speaker clips; +blend uses 2,391 clips with non-null blend. Family MAE is the unweighted mean over valid axes. + +| Flash test target / metric | Base | Small | +| --- | ---: | ---: | +| Burst frame F1 ↑ | 0.6929 | 0.6892 | +| Burst event F1, IoU ≥0.5 ↑ | 0.1945 | 0.1949 | +| Burst start MAE on IoU ≥0.1 matches, seconds ↓ | 0.1201 | 0.1150 | +| Burst end MAE on IoU ≥0.1 matches, seconds ↓ | 0.1973 | 0.2006 | +| 53-class accuracy with reference spans ↑ | 0.4280 | 0.4483 | +| 53-class accuracy on matched predicted spans ↑ | 0.4189 | 0.4370 | +| Orange timbre cosine ↑ (1,718 valid single-speaker clips) | 0.9038 | 0.9223 | +| Orange identity cosine ↑ (1,718 valid single-speaker clips) | 0.8541 | 0.8741 | +| CPS raw MAE, characters/second ↓ | 0.9012 | 0.9309 | +| Genuineness raw MAE on 0–6 scale ↓ (3,544 clips) | 0.4774 | 0.4704 | +| Genuineness normalized MAE ↓ | 0.2971 | 0.2928 | +| Blend raw MAE on 0–10 scale ↓ (2,391 valid clips) | 0.8773 | 0.8417 | +| Blend normalized MAE ↓ | 0.3228 | 0.3097 | +| 40 emotions, mean normalized MAE ↓ | 0.5873 | 0.5605 | +| 57 VoiceNet dimensions, mean normalized MAE ↓ | 0.4967 | 0.4813 | +| AudioBox four axes, mean normalized MAE ↓ | 0.1877 | 0.1739 | +| DNSMOS seven outputs, mean normalized MAE ↓ | 0.2047 | 0.1975 | + +Normalized MAE is measured in frozen training standard deviations. Burst interval metrics use +Hungarian matching of independent onset/duration proposals, not binary contiguous-frame regions. +Boundary errors and predicted-span class accuracy are conditional on matches at IoU ≥0.1. +Reference-span class accuracy gives the classifier the true interval; it is not end-to-end +localization accuracy. Maximum proposals is 32, with a fixed 0.5 onset threshold. Event precision +is low despite high recall; the report includes all IoU cutoffs and precision/recall. + +The validation tables in `evaluation/{base,small}/validation_metrics.json` use the existing +3,239 checkpoint-selection clips, not an untouched test. Supplementary evaluator loss uses +unit class weights, while the training selection loss above used the configured class weights. +Different loss values under those two policies are expected. + +## Public human-label benchmarks + +| Human-label benchmark / metric | Base | Small | +| --- | ---: | ---: | +| EmoNet-Voice intensity, Pearson r ↑ | 0.4340 | 0.4512 | +| EmoNet-Voice intensity, Spearman ρ ↑ | 0.4577 | 0.4679 | +| VoiceNet-Emo, mean prompt Spearman ρ ↑ | 0.4288 | 0.4282 | +| VoiceNet-Ext / emolia-dim, mean prompt Spearman ρ ↑ | 0.1566 | 0.1502 | +| CREMA-D matched actor-CV score-MLP accuracy ↑ | 0.6310 | 0.6423 | +| RAVDESS matched actor-CV score-MLP accuracy ↑ | 0.6597 | 0.6639 | + +EmoNet uses 12,000 mapped clips and fixed 0–4→0–10 endpoint scaling; two unsupported labels +from the published 12,600-clip benchmark are omitted. VoiceNet-Emo uses 7,986 current-repository +questions with at least two raters; VoiceNet-Ext uses 13,917. **EXT and DIM refer to the same +benchmark**. Current snapshot coverage differs from paper coverage. The report includes unflagged, +untruncated and alternate agreement cuts and explains optimistic oracle threshold numbers. + +CREMA-D and RAVDESS figures are **supervised matched nested actor-cross-validation score adapters**, +not zero-shot paper numbers. Each adapter sees the model's 192 raw predictions, a train-fold +StandardScaler and a 64-unit GELU MLP: 12,742 / 12,872 parameters respectively. Five outer actor +folds and three inner folds choose learning rate 0.001/0.003 and 20/50 epochs. Outer test actors +never fit that fold's scaler or adapter. These benchmark-only adapters are not the model's burst +classifier and are not automatically applied by `inference.py`. Full fold records, metrics and +confusions are included; the report compares the same adapter budget across all backbones. +Upstream training/source overlap with the public benchmarks was not exhaustively audited. + +## P3 retention and limitations + +On the identical fixed **2,000-clip P3 test audit**, frame F1 changes from **0.9622 to 0.8051** +for Base and **0.9693 to 0.8392** for Small after Gemini tuning. Flash test frame F1 rises +from 0.3072/0.3171 to 0.6929/0.6892 respectively. This is a target/domain tradeoff: +there was no P3 replay in this phase. All matched pre/post P3 validation/test JSONs are included. +It is not valid to claim universal burst improvement from Flash agreement alone. + +The selected pool is not a representative random sample of all natural audio. Quality scores +imitate AudioBox/DNSMOS/Empathic teachers rather than measuring new human MOS. Speaker cosine +measures regression agreement with Orange, not verification accuracy or human identity. +The burst classifier groups unsupported fine labels, event proposals have many false positives, +and emotional or expressive clips can differ from the synthetic P3 construction labels. +Recordings longer than 30 seconds and multi-speaker embeddings require additional policies. + +## Annotation models, taxonomies and packages + +| Supervision / package | Primary reference | +| --- | --- | +| Original Whisper encoders | [OpenAI Base](https://huggingface.co/openai/whisper-base), [OpenAI Small](https://huggingface.co/openai/whisper-small), [Whisper code](https://github.com/openai/whisper). | +| Gemini annotation schema | The exact 108 KB master prompt is `training/gemini_s10_smoke/gemini_voice_annotation_master_prompt_detailed.txt`; parser and validation code are included. | +| 40 emotions | [EmoNet taxonomy](https://github.com/LAION-AI/voicenet/blob/main/taxonomy/emonet_taxonomy.md), [emotion annotations toolkit](https://github.com/LAION-AI/emotion-annotations). | +| 57 voice dimensions | [VoiceNet taxonomy](https://github.com/LAION-AI/voicenet/blob/main/taxonomy/voicenet_taxonomy.md), [commercial dimension predictors](https://huggingface.co/laion/voicenet-dimension-predictors-commercial). | +| Original genuineness teacher | [VoiceCLAP commercial genuineness](https://huggingface.co/laion/voiceclap-commercial-genuineness); matching targets superseded by valid Flash labels. | +| Original blend teacher | [VoiceCLAP commercial vocalburst blend](https://huggingface.co/laion/voiceclap-commercial-vocalburst-blend); matching targets superseded by valid Flash labels. | +| VoiceCLAP attributes / intermediate embedding | [Commercial VoiceCLAP](https://huggingface.co/laion/voiceclap-commercial), [attribute heads](https://huggingface.co/laion/voiceclap-commercial-attribute-heads); 768D intermediate embeddings are cached in data, not an output head. | +| Empathic extras / original emotion teacher | [Empathic Insight Voice Plus](https://huggingface.co/laion/Empathic-Insight-Voice-Plus), [BUD-E Whisper](https://huggingface.co/laion/BUD-E-Whisper); 3072D teacher features are cached, not an output head. | +| AudioBox | [Meta AudioBox Aesthetics](https://huggingface.co/facebook/audiobox-aesthetics). | +| DNSMOS | [Microsoft DNS Challenge / DNSMOS](https://github.com/microsoft/DNS-Challenge/tree/master/DNSMOS). | +| Orange timbre, 128D | [Orange Speaker-wavLM-tbr](https://huggingface.co/Orange/Speaker-wavLM-tbr), pinned annotation revision `b8d2608d56f18e2b6e27ba566cf69132e2c2ad6c`. | +| Orange identity, 250D | [Orange Speaker-wavLM-id](https://huggingface.co/Orange/Speaker-wavLM-id), pinned revision `abbb3c7b8d220ceebc33d9bd3bb5aedb580342c7`. | +| Timbre generation implementation | [LAION generation script](https://github.com/LAION-AI/emotion-annotations/blob/main/generate_timbre_embeddings.py). | +| Vocal event taxonomy / locator | [LAION voice taxonomies](https://github.com/LAION-AI/voice-taxonomies), [vocalburst locator](https://huggingface.co/laion/vocalburst-locator), canonical classes and Flash mapping bundled here. | +| Human emotion/style benchmark | [LAION emolia-bench](https://github.com/LAION-AI/emolia-bench), [EmoNet-Voice Bench](https://huggingface.co/datasets/t1a5anu-anon/emonet-voice-bench). | + +Teacher inference runs on the exact selected waveform. New scores and float16 target vectors +are persisted in SHA-bound companion TARs; the gradient loop reuses those sidecars. Their +model revisions and target-level provenance remain in the prepared annotations. + +## Repository contents and export verification + +| Path | Contents | +| --- | --- | +| `base/model.safetensors`, `small/model.safetensors` | Complete best Gemini-tuned encoder and head weights; no decoder. | +| `base/checkpoint.pt`, `small/checkpoint.pt` | Exact best resumable training states including optimizer, scheduler and per-rank RNG. | +| `{base,small}/config.json`, `preprocessor_config.json` | Corresponding encoder architecture and Whisper log-mel frontend. | +| `model.py`, `inference.py`, `requirements.txt` | Complete standalone inference, cached Predictor API and CLI. | +| `training/`, `vocal_burst_pool/p3/` | Actual training source and local dependency closure, target parser, prompt, backfill and evaluation code, configs, logs and reproduction helper. | +| `training_normalization.json`, `display_normalization.json`, `gemini_train_statistics.json` | Frozen decoding, contextual display and separate final Flash train-only statistics. | +| `classes.json`, `gemini_burst_mapping.json` | 53 canonical class names and all Flash aliases/fallbacks. | +| `evaluation/{base,small}/` | Flash validation/test, pre/post P3 retention, public benchmarks and actor-adapter folds. | +| `verification/{base,small}.json` | Bitwise safe-weight and model-output parity; real held-out audio API checks, dimensions and timestamp validity. | +| `release_manifest.json` | File sizes and SHA256 hashes, original checkpoint hashes and provenance. | +| `release_tools/` | Export, verification and publication source. | +| `LICENSE`, `NOTICE.md` | CC BY 4.0 and upstream attribution. | + +No audio, source transcripts, raw Gemini responses or service credentials are published by +this model repository. Model verification uses a real held-out waveform internally and saves +only its hash and output-contract evidence. Evaluation JSONs contain aggregate benchmark +results, prompts/classes and fold protocols; no listening audio is copied into this release. + +## License and attribution + +The new fine-tuned weights, new code and documentation are released by **LAION** under +**[Creative Commons Attribution 4.0 International](LICENSE)**. Credit **Christoph Schuhmann +and LAION**, link this repository and indicate changes. This license does not relicense +upstream source audio, teacher models or external packages. The original OpenAI Whisper +components remain MIT; Orange's identity teacher retains its upstream CC BY-SA 3.0 terms. +See `NOTICE.md` and upstream model/data cards for their notices. + +```bibtex +@misc{schuhmann_whisper_gemini_multitask_2026, + title = {Gemini-Tuned Encoder-Only Whisper Base and Small for Emotion, Voice, Quality and Vocal Bursts}, + author = {Schuhmann, Christoph and {LAION}}, + year = {2026}, + url = {https://huggingface.co/laion/whisper-base-small-gemini-audio-scores} +} +``` + +## All 192 scalar targets: exact held-out results + +These are Flash test targets, with the remaining AudioBox/DNSMOS/Orange/Empathic families +provided by their own teachers. Raw MAE uses each target's units. Norm. MAE uses its frozen +training standard deviation. `r` is Pearson and `ρ` is Spearman. Missing metrics remain +unavailable. The source JSON has separate counts for every model/target. + +| Index | Exact target key | Valid N | Base raw MAE | Base norm. MAE | Base r | Base ρ | Small raw MAE | Small norm. MAE | Small r | Small ρ | +| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 0 | `emo_Affection` | 3544 | 0.4034 | 0.7418 | 0.7163 | 0.5685 | 0.3911 | 0.7192 | 0.7345 | 0.5839 | +| 1 | `emo_Amusement` | 3544 | 0.5187 | 0.5978 | 0.7413 | 0.6742 | 0.4879 | 0.5623 | 0.7606 | 0.6992 | +| 2 | `emo_Anger` | 3544 | 0.3462 | 0.5968 | 0.6770 | 0.4799 | 0.3376 | 0.5819 | 0.6729 | 0.5020 | +| 3 | `emo_Astonishment_Surprise` | 3544 | 0.4007 | 0.7257 | 0.6906 | 0.5951 | 0.3838 | 0.6950 | 0.7075 | 0.6081 | +| 4 | `emo_Awe` | 3544 | 0.3259 | 0.7764 | 0.7652 | 0.5528 | 0.3002 | 0.7152 | 0.7946 | 0.5699 | +| 5 | `emo_Bitterness` | 3544 | 0.3718 | 0.9136 | 0.6692 | 0.5244 | 0.3488 | 0.8571 | 0.6932 | 0.5548 | +| 6 | `emo_Concentration` | 3544 | 0.4721 | 0.6463 | 0.6937 | 0.6930 | 0.4634 | 0.6343 | 0.7024 | 0.7005 | +| 7 | `emo_Confusion` | 3544 | 0.3659 | 0.6860 | 0.6370 | 0.5124 | 0.3462 | 0.6490 | 0.6617 | 0.5392 | +| 8 | `emo_Contemplation` | 3544 | 0.5538 | 0.9403 | 0.7380 | 0.7293 | 0.5367 | 0.9113 | 0.7552 | 0.7449 | +| 9 | `emo_Contempt` | 3544 | 0.3103 | 0.6878 | 0.6823 | 0.4370 | 0.2999 | 0.6648 | 0.6994 | 0.4587 | +| 10 | `emo_Contentment` | 3544 | 0.5967 | 1.1843 | 0.6973 | 0.6890 | 0.5675 | 1.1264 | 0.7270 | 0.7214 | +| 11 | `emo_Disappointment` | 3544 | 0.4548 | 0.6485 | 0.7058 | 0.5985 | 0.4328 | 0.6171 | 0.7290 | 0.6164 | +| 12 | `emo_Disgust` | 3544 | 0.2244 | 0.4576 | 0.6700 | 0.4164 | 0.2140 | 0.4365 | 0.7002 | 0.4420 | +| 13 | `emo_Distress` | 3544 | 0.4677 | 0.5722 | 0.7701 | 0.6569 | 0.4445 | 0.5438 | 0.7917 | 0.6834 | +| 14 | `emo_Doubt` | 3544 | 0.4587 | 0.9491 | 0.6336 | 0.6068 | 0.4360 | 0.9020 | 0.6652 | 0.6390 | +| 15 | `emo_Elation` | 3544 | 0.3643 | 0.5071 | 0.7254 | 0.5958 | 0.3513 | 0.4890 | 0.7449 | 0.6097 | +| 16 | `emo_Embarrassment` | 3544 | 0.1472 | 0.3367 | 0.5970 | 0.2711 | 0.1434 | 0.3282 | 0.6350 | 0.2784 | +| 17 | `emo_Emotional_Numbness` | 3544 | 0.2516 | 0.2617 | 0.5849 | 0.4138 | 0.2434 | 0.2532 | 0.6081 | 0.4254 | +| 18 | `emo_Fatigue_Exhaustion` | 3544 | 0.4472 | 0.8269 | 0.7700 | 0.6846 | 0.4385 | 0.8108 | 0.7746 | 0.6890 | +| 19 | `emo_Fear` | 3544 | 0.2303 | 0.3764 | 0.7266 | 0.4491 | 0.2201 | 0.3597 | 0.7331 | 0.4586 | +| 20 | `emo_Helplessness` | 3544 | 0.4093 | 0.5781 | 0.7839 | 0.6445 | 0.3889 | 0.5492 | 0.8065 | 0.6608 | +| 21 | `emo_Hope_Enthusiasm_Optimism` | 3544 | 0.5715 | 0.7535 | 0.6933 | 0.6714 | 0.5412 | 0.7135 | 0.7238 | 0.7046 | +| 22 | `emo_Impatience_and_Irritability` | 3544 | 0.5024 | 0.7343 | 0.6582 | 0.5313 | 0.4835 | 0.7068 | 0.6653 | 0.5517 | +| 23 | `emo_Infatuation` | 3544 | 0.1289 | 0.2382 | 0.7034 | 0.3314 | 0.1205 | 0.2226 | 0.7270 | 0.3399 | +| 24 | `emo_Interest` | 3544 | 0.5271 | 0.5766 | 0.7397 | 0.7085 | 0.5056 | 0.5531 | 0.7626 | 0.7317 | +| 25 | `emo_Intoxication_Altered_States_of_Consciousness` | 3544 | 0.1044 | 0.1877 | 0.4826 | 0.2741 | 0.1046 | 0.1881 | 0.4474 | 0.2786 | +| 26 | `emo_Jealousy_and_Envy` | 3544 | 0.0624 | 0.1400 | 0.6992 | 0.1091 | 0.0559 | 0.1254 | 0.7301 | 0.1062 | +| 27 | `emo_Longing` | 3544 | 0.3539 | 0.5566 | 0.7124 | 0.5817 | 0.3355 | 0.5277 | 0.7392 | 0.5894 | +| 28 | `emo_Malevolence_Malice` | 3544 | 0.1676 | 0.3200 | 0.6364 | 0.3145 | 0.1636 | 0.3124 | 0.6531 | 0.3299 | +| 29 | `emo_Pain` | 3544 | 0.2795 | 0.4859 | 0.7490 | 0.5796 | 0.2580 | 0.4485 | 0.7785 | 0.5889 | +| 30 | `emo_Pleasure_Ecstasy` | 3544 | 0.2679 | 0.4388 | 0.7381 | 0.5662 | 0.2471 | 0.4048 | 0.7743 | 0.5881 | +| 31 | `emo_Pride` | 3544 | 0.5015 | 0.9645 | 0.6547 | 0.5704 | 0.4816 | 0.9263 | 0.6812 | 0.5928 | +| 32 | `emo_Relief` | 3544 | 0.4468 | 0.6403 | 0.6572 | 0.5138 | 0.4189 | 0.6002 | 0.6932 | 0.5388 | +| 33 | `emo_Sadness` | 3544 | 0.3507 | 0.4589 | 0.7617 | 0.5775 | 0.3287 | 0.4301 | 0.7960 | 0.5914 | +| 34 | `emo_Sexual_Lust` | 3544 | 0.0958 | 0.1924 | 0.6895 | 0.2417 | 0.0930 | 0.1868 | 0.6770 | 0.2413 | +| 35 | `emo_Shame` | 3544 | 0.0955 | 0.1822 | 0.6960 | 0.2302 | 0.0896 | 0.1710 | 0.7326 | 0.2385 | +| 36 | `emo_Sourness` | 3544 | 0.3766 | 0.8710 | 0.6533 | 0.5006 | 0.3576 | 0.8271 | 0.6761 | 0.5236 | +| 37 | `emo_Teasing` | 3544 | 0.4089 | 0.7599 | 0.6783 | 0.5625 | 0.4021 | 0.7473 | 0.6848 | 0.5687 | +| 38 | `emo_Thankfulness_Gratitude` | 3544 | 0.2490 | 0.3602 | 0.7406 | 0.4788 | 0.2260 | 0.3269 | 0.7811 | 0.5085 | +| 39 | `emo_Triumph` | 3544 | 0.3263 | 0.6180 | 0.6941 | 0.5031 | 0.3151 | 0.5967 | 0.7145 | 0.5123 | +| 40 | `vn_AGEV_reg` | 3544 | 0.3185 | 0.2527 | 0.4460 | 0.4312 | 0.3093 | 0.2455 | 0.4414 | 0.4270 | +| 41 | `vn_AROU_reg` | 3544 | 0.5437 | 0.4826 | 0.7243 | 0.6929 | 0.5284 | 0.4690 | 0.7376 | 0.7127 | +| 42 | `vn_ARSH_reg` | 3544 | 0.4911 | 0.5604 | 0.3468 | 0.3323 | 0.4776 | 0.5450 | 0.3800 | 0.3731 | +| 43 | `vn_ATCK_reg` | 3544 | 0.4661 | 0.3540 | 0.6870 | 0.6544 | 0.4635 | 0.3520 | 0.6932 | 0.6690 | +| 44 | `vn_BKGN_reg` | 3544 | 0.3384 | 0.4472 | 0.6167 | 0.6001 | 0.3359 | 0.4439 | 0.6118 | 0.5989 | +| 45 | `vn_BRGT_reg` | 3544 | 0.3844 | 0.4743 | 0.6861 | 0.6711 | 0.3833 | 0.4729 | 0.6862 | 0.6676 | +| 46 | `vn_CHNK_reg` | 3544 | 0.4343 | 0.3484 | 0.7160 | 0.7076 | 0.4312 | 0.3459 | 0.7241 | 0.7166 | +| 47 | `vn_CLRT_reg` | 3544 | 0.4416 | 0.4595 | 0.8017 | 0.6867 | 0.4334 | 0.4510 | 0.8035 | 0.6948 | +| 48 | `vn_COGL_reg` | 3544 | 0.5413 | 0.5135 | 0.7345 | 0.7335 | 0.5232 | 0.4963 | 0.7542 | 0.7556 | +| 49 | `vn_DARC_reg` | 3544 | 0.4769 | 0.3501 | 0.4287 | 0.3938 | 0.4663 | 0.3423 | 0.4556 | 0.4118 | +| 50 | `vn_DFLU_reg` | 3544 | 0.5694 | 0.4820 | 0.8008 | 0.7930 | 0.5619 | 0.4757 | 0.8075 | 0.8025 | +| 51 | `vn_EMPH_reg` | 3544 | 0.4545 | 0.2893 | 0.6617 | 0.6401 | 0.4437 | 0.2824 | 0.6713 | 0.6575 | +| 52 | `vn_ESTH_reg` | 3544 | 0.4608 | 0.5917 | 0.6939 | 0.6557 | 0.4423 | 0.5680 | 0.7172 | 0.6910 | +| 53 | `vn_EXPL_reg` | 3544 | 0.1151 | 0.3516 | 0.2977 | 0.1599 | 0.1088 | 0.3323 | 0.2825 | 0.1475 | +| 54 | `vn_FOCS_reg` | 3544 | 0.4337 | 0.3907 | 0.6086 | 0.4914 | 0.4098 | 0.3692 | 0.6395 | 0.5212 | +| 55 | `vn_FULL_reg` | 3544 | 0.3593 | 0.4066 | 0.5248 | 0.5048 | 0.3466 | 0.3922 | 0.5457 | 0.5310 | +| 56 | `vn_GEND_reg` | 3544 | 0.5459 | 0.3428 | 0.8868 | 0.8254 | 0.5735 | 0.3601 | 0.8836 | 0.8256 | +| 57 | `vn_HARM_reg` | 3544 | 0.4697 | 0.5674 | 0.6904 | 0.6492 | 0.4512 | 0.5450 | 0.7242 | 0.6856 | +| 58 | `vn_METL_reg` | 3544 | 0.4922 | 0.5140 | 0.6026 | 0.5499 | 0.4832 | 0.5046 | 0.6160 | 0.5692 | +| 59 | `vn_RANG_reg` | 3544 | 0.5298 | 0.5943 | 0.7133 | 0.7109 | 0.5125 | 0.5749 | 0.7316 | 0.7340 | +| 60 | `vn_RCQL_reg` | 3544 | 0.4049 | 0.4498 | 0.6185 | 0.6144 | 0.4084 | 0.4536 | 0.6119 | 0.6139 | +| 61 | `vn_REGS_reg` | 3544 | 0.4636 | 0.3690 | 0.8743 | 0.8353 | 0.4724 | 0.3760 | 0.8706 | 0.8324 | +| 62 | `vn_RESP_reg` | 3544 | 0.4666 | 0.4362 | 0.8088 | 0.7550 | 0.4444 | 0.4155 | 0.8261 | 0.7708 | +| 63 | `vn_ROUG_reg` | 3544 | 0.5316 | 0.6029 | 0.7408 | 0.7184 | 0.5216 | 0.5916 | 0.7509 | 0.7339 | +| 64 | `vn_R_CHST_reg` | 3544 | 0.4628 | 0.5450 | 0.7755 | 0.7775 | 0.4618 | 0.5439 | 0.7789 | 0.7825 | +| 65 | `vn_R_HEAD_reg` | 3544 | 0.5097 | 0.6189 | 0.7728 | 0.7756 | 0.5088 | 0.6178 | 0.7719 | 0.7709 | +| 66 | `vn_R_MASK_reg` | 3544 | 0.4401 | 0.4301 | 0.7060 | 0.6774 | 0.4369 | 0.4269 | 0.7118 | 0.6882 | +| 67 | `vn_R_MIXD_reg` | 3544 | 0.4149 | 0.4957 | 0.6177 | 0.5642 | 0.4046 | 0.4835 | 0.6333 | 0.5872 | +| 68 | `vn_R_NASL_reg` | 3544 | 0.4205 | 0.3982 | 0.4078 | 0.3681 | 0.4063 | 0.3847 | 0.4375 | 0.4148 | +| 69 | `vn_R_ORAL_reg` | 3544 | 0.3597 | 0.4834 | 0.6342 | 0.6177 | 0.3516 | 0.4725 | 0.6439 | 0.6331 | +| 70 | `vn_R_THRT_reg` | 3544 | 0.4400 | 0.5464 | 0.6952 | 0.6673 | 0.4269 | 0.5301 | 0.7085 | 0.6869 | +| 71 | `vn_SMTH_reg` | 3544 | 0.4650 | 0.4184 | 0.7178 | 0.7130 | 0.4497 | 0.4046 | 0.7341 | 0.7314 | +| 72 | `vn_STNC_reg` | 3544 | 0.6337 | 0.5565 | 0.6729 | 0.6393 | 0.6053 | 0.5316 | 0.7046 | 0.6692 | +| 73 | `vn_STRU_reg` | 3544 | 0.4699 | 0.4766 | 0.7898 | 0.7386 | 0.4659 | 0.4724 | 0.7923 | 0.7465 | +| 74 | `vn_S_ASMR_reg` | 3544 | 0.5717 | 0.4147 | 0.7268 | 0.6562 | 0.5479 | 0.3974 | 0.7467 | 0.6773 | +| 75 | `vn_S_AUTH_reg` | 3544 | 0.7103 | 0.6837 | 0.7108 | 0.6953 | 0.6791 | 0.6537 | 0.7404 | 0.7287 | +| 76 | `vn_S_CART_reg` | 3544 | 0.6246 | 0.4562 | 0.6659 | 0.5904 | 0.6244 | 0.4560 | 0.6549 | 0.5974 | +| 77 | `vn_S_CASU_reg` | 3544 | 0.7998 | 0.6269 | 0.6985 | 0.7130 | 0.7619 | 0.5971 | 0.7284 | 0.7413 | +| 78 | `vn_S_CONV_reg` | 3544 | 0.6987 | 0.5353 | 0.7534 | 0.7393 | 0.6797 | 0.5207 | 0.7696 | 0.7600 | +| 79 | `vn_S_DRAM_reg` | 3544 | 0.6893 | 0.6050 | 0.7871 | 0.7833 | 0.6613 | 0.5804 | 0.8062 | 0.8037 | +| 80 | `vn_S_FORM_reg` | 3544 | 0.6598 | 0.6543 | 0.7273 | 0.7316 | 0.6426 | 0.6373 | 0.7447 | 0.7507 | +| 81 | `vn_S_MONO_reg` | 3544 | 0.8582 | 0.6745 | 0.6484 | 0.6408 | 0.8287 | 0.6514 | 0.6732 | 0.6646 | +| 82 | `vn_S_NARR_reg` | 3544 | 0.7113 | 0.4956 | 0.7613 | 0.7441 | 0.6963 | 0.4851 | 0.7702 | 0.7539 | +| 83 | `vn_S_NEWS_reg` | 3544 | 0.4990 | 0.9056 | 0.6904 | 0.6742 | 0.4881 | 0.8858 | 0.7103 | 0.6925 | +| 84 | `vn_S_PLAY_reg` | 3544 | 0.8496 | 0.6535 | 0.6578 | 0.6327 | 0.7954 | 0.6119 | 0.7052 | 0.6929 | +| 85 | `vn_S_RANT_reg` | 3544 | 0.6595 | 0.5168 | 0.5771 | 0.4963 | 0.6214 | 0.4870 | 0.6483 | 0.5575 | +| 86 | `vn_S_STRY_reg` | 3544 | 0.7550 | 0.5417 | 0.6284 | 0.6181 | 0.7309 | 0.5245 | 0.6553 | 0.6423 | +| 87 | `vn_S_TECH_reg` | 3544 | 0.7180 | 0.5895 | 0.7318 | 0.7560 | 0.6733 | 0.5529 | 0.7636 | 0.7858 | +| 88 | `vn_S_WHIS_reg` | 3544 | 0.5282 | 0.3304 | 0.6921 | 0.6367 | 0.4893 | 0.3061 | 0.7177 | 0.6580 | +| 89 | `vn_TEMP_reg` | 3544 | 0.4112 | 0.3424 | 0.7711 | 0.7410 | 0.4034 | 0.3360 | 0.7789 | 0.7479 | +| 90 | `vn_TENS_reg` | 3544 | 0.5110 | 0.6140 | 0.7012 | 0.6084 | 0.4922 | 0.5914 | 0.7223 | 0.6347 | +| 91 | `vn_VALN_reg` | 3544 | 0.7516 | 0.6652 | 0.6443 | 0.6258 | 0.6572 | 0.5816 | 0.7361 | 0.7195 | +| 92 | `vn_VALS_reg` | 3544 | 0.4307 | 0.3921 | 0.4358 | 0.3966 | 0.4074 | 0.3709 | 0.5246 | 0.4904 | +| 93 | `vn_VFLX_reg` | 3544 | 0.3329 | 0.2758 | 0.1541 | 0.1562 | 0.3150 | 0.2611 | 0.1945 | 0.1881 | +| 94 | `vn_VOLT_reg` | 3544 | 0.4732 | 0.5448 | 0.7000 | 0.6775 | 0.4696 | 0.5408 | 0.7030 | 0.6810 | +| 95 | `vn_VULN_reg` | 3544 | 0.6739 | 0.6803 | 0.7484 | 0.7045 | 0.6464 | 0.6526 | 0.7704 | 0.7329 | +| 96 | `vn_WARM_reg` | 3544 | 0.4646 | 0.5134 | 0.5795 | 0.5277 | 0.4362 | 0.4820 | 0.6354 | 0.5966 | +| 97 | `genuineness_0_6` | 3544 | 0.4774 | 0.2971 | 0.5561 | 0.5957 | 0.4704 | 0.2928 | 0.5616 | 0.5979 | +| 98 | `blend_0_10` | 2391 | 0.8773 | 0.3228 | 0.2822 | 0.3074 | 0.8417 | 0.3097 | 0.3151 | 0.3216 | +| 99 | `R_quality` | 2413 | 0.1025 | 0.3095 | 0.9180 | 0.8908 | 0.0972 | 0.2937 | 0.9246 | 0.8994 | +| 100 | `eiv_extra:Age` | 3368 | 0.1391 | 0.4716 | 0.8225 | 0.7135 | 0.1344 | 0.4557 | 0.8399 | 0.7298 | +| 101 | `eiv_extra:Arousal` | 3368 | 0.2575 | 0.5906 | 0.8606 | 0.8790 | 0.2399 | 0.5503 | 0.8760 | 0.8948 | +| 102 | `eiv_extra:Authenticity` | 3368 | 0.0930 | 0.3718 | 0.9065 | 0.9116 | 0.0872 | 0.3487 | 0.9187 | 0.9240 | +| 103 | `eiv_extra:Background_Noise` | 3368 | 0.1414 | 0.5358 | 0.8755 | 0.8651 | 0.1324 | 0.5018 | 0.8925 | 0.8775 | +| 104 | `eiv_extra:Confident_vs._Hesitant` | 3368 | 0.1701 | 0.5593 | 0.9002 | 0.9047 | 0.1545 | 0.5081 | 0.9178 | 0.9235 | +| 105 | `eiv_extra:Gender` | 3368 | 0.2693 | 0.2700 | 0.9372 | 0.9178 | 0.2534 | 0.2540 | 0.9454 | 0.9256 | +| 106 | `eiv_extra:High-Pitched_vs._Low-Pitched` | 3368 | 0.0894 | 0.3577 | 0.9407 | 0.9421 | 0.0802 | 0.3206 | 0.9523 | 0.9529 | +| 107 | `eiv_extra:Monotone_vs._Expressive` | 3368 | 0.1635 | 0.3478 | 0.9344 | 0.9346 | 0.1480 | 0.3147 | 0.9471 | 0.9475 | +| 108 | `eiv_extra:Recording_Quality` | 3368 | 0.1351 | 0.4062 | 0.9468 | 0.9465 | 0.1256 | 0.3775 | 0.9545 | 0.9551 | +| 109 | `eiv_extra:Serious_vs._Humorous` | 3368 | 0.1966 | 0.5766 | 0.8678 | 0.8451 | 0.1877 | 0.5506 | 0.8799 | 0.8581 | +| 110 | `eiv_extra:Soft_vs._Harsh` | 3368 | 0.1796 | 0.5623 | 0.7959 | 0.8006 | 0.1717 | 0.5377 | 0.8127 | 0.8101 | +| 111 | `eiv_extra:Submissive_vs._Dominant` | 3368 | 0.1440 | 0.4455 | 0.8502 | 0.8475 | 0.1348 | 0.4172 | 0.8704 | 0.8672 | +| 112 | `eiv_extra:Valence` | 3368 | 0.3711 | 0.9724 | 0.8634 | 0.8222 | 0.3430 | 0.8989 | 0.8808 | 0.8320 | +| 113 | `eiv_extra:Vulnerable_vs._Emotionally_Detached` | 3368 | 0.1589 | 0.3646 | 0.9240 | 0.9231 | 0.1509 | 0.3462 | 0.9328 | 0.9323 | +| 114 | `eiv_extra:Warm_vs._Cold` | 3368 | 0.1916 | 0.6387 | 0.8206 | 0.7840 | 0.1808 | 0.6030 | 0.8421 | 0.8045 | +| 115 | `eiv_extra:score_background_quality` | 3368 | 0.1026 | 0.1479 | 0.9482 | 0.8992 | 0.0996 | 0.1436 | 0.9518 | 0.9062 | +| 116 | `eiv_extra:score_content_enjoyment` | 3368 | 0.0765 | 0.2518 | 0.9534 | 0.9410 | 0.0728 | 0.2398 | 0.9577 | 0.9455 | +| 117 | `eiv_extra:score_overall_quality` | 3368 | 0.0819 | 0.1613 | 0.9615 | 0.9363 | 0.0786 | 0.1549 | 0.9645 | 0.9391 | +| 118 | `eiv_extra:score_speech_quality` | 3368 | 0.0479 | 0.1915 | 0.8332 | 0.7329 | 0.0445 | 0.1779 | 0.8574 | 0.7713 | +| 119 | `audiobox:CE` | 3544 | 0.2039 | 0.2006 | 0.9459 | 0.9448 | 0.1902 | 0.1871 | 0.9546 | 0.9518 | +| 120 | `audiobox:CU` | 3544 | 0.2151 | 0.2153 | 0.9356 | 0.9426 | 0.1998 | 0.2000 | 0.9454 | 0.9494 | +| 121 | `audiobox:PC` | 3544 | 0.1312 | 0.0812 | 0.9535 | 0.8055 | 0.1204 | 0.0745 | 0.9619 | 0.8117 | +| 122 | `audiobox:PQ` | 3544 | 0.2230 | 0.2537 | 0.9322 | 0.9349 | 0.2057 | 0.2340 | 0.9429 | 0.9408 | +| 123 | `dnsmos:SIG_raw` | 3368 | 0.1495 | 0.2234 | 0.9061 | 0.8503 | 0.1441 | 0.2153 | 0.9133 | 0.8592 | +| 124 | `dnsmos:BAK_raw` | 3368 | 0.2130 | 0.1893 | 0.9205 | 0.8742 | 0.2055 | 0.1826 | 0.9258 | 0.8810 | +| 125 | `dnsmos:OVRL_raw` | 3368 | 0.1801 | 0.2215 | 0.9296 | 0.8908 | 0.1743 | 0.2144 | 0.9339 | 0.8969 | +| 126 | `dnsmos:SIG` | 3368 | 0.0938 | 0.1872 | 0.9033 | 0.8464 | 0.0901 | 0.1797 | 0.9108 | 0.8563 | +| 127 | `dnsmos:BAK` | 3368 | 0.1440 | 0.1551 | 0.9250 | 0.8659 | 0.1395 | 0.1502 | 0.9301 | 0.8699 | +| 128 | `dnsmos:OVRL` | 3368 | 0.1200 | 0.1978 | 0.9327 | 0.8902 | 0.1161 | 0.1913 | 0.9371 | 0.8965 | +| 129 | `dnsmos:P808_MOS` | 3368 | 0.1283 | 0.2585 | 0.9176 | 0.8696 | 0.1235 | 0.2487 | 0.9230 | 0.8819 | +| 130 | `burst_count_log1p` | 3544 | 0.2474 | 0.3815 | 0.8305 | 0.8198 | 0.2492 | 0.3843 | 0.8273 | 0.8164 | +| 131 | `voiceclap_attribute:Affection` | 3544 | 0.5078 | 0.9158 | 0.5838 | 0.4973 | 0.4877 | 0.8795 | 0.6392 | 0.5332 | +| 132 | `voiceclap_attribute:Age` | 3368 | 0.1578 | 0.2902 | 0.8188 | 0.7483 | 0.1504 | 0.2766 | 0.8313 | 0.7624 | +| 133 | `voiceclap_attribute:Amusement` | 3544 | 0.5768 | 1.0050 | 0.7031 | 0.6284 | 0.5497 | 0.9579 | 0.7273 | 0.6620 | +| 134 | `voiceclap_attribute:Anger` | 3544 | 0.4138 | 0.8156 | 0.6069 | 0.4560 | 0.3912 | 0.7711 | 0.6250 | 0.4527 | +| 135 | `voiceclap_attribute:Arousal` | 3368 | 0.2821 | 0.4532 | 0.8416 | 0.8537 | 0.2863 | 0.4599 | 0.8385 | 0.8489 | +| 136 | `voiceclap_attribute:Astonishment_Surprise` | 3544 | 0.4791 | 1.0005 | 0.6094 | 0.5371 | 0.4772 | 0.9966 | 0.6296 | 0.5575 | +| 137 | `voiceclap_attribute:Authenticity` | 3368 | 0.0910 | 0.3639 | 0.8870 | 0.8965 | 0.0938 | 0.3751 | 0.8810 | 0.8852 | +| 138 | `voiceclap_attribute:Awe` | 3544 | 0.4582 | 1.7662 | 0.6179 | 0.4764 | 0.4115 | 1.5863 | 0.7134 | 0.5330 | +| 139 | `voiceclap_attribute:Background_Noise` | 3368 | 0.0962 | 0.3644 | 0.8873 | 0.8881 | 0.1001 | 0.3789 | 0.8754 | 0.8735 | +| 140 | `voiceclap_attribute:Bitterness` | 3544 | 0.4419 | 1.7677 | 0.5745 | 0.4753 | 0.4032 | 1.6130 | 0.6572 | 0.5235 | +| 141 | `voiceclap_attribute:Concentration` | 3544 | 0.5051 | 0.9521 | 0.6333 | 0.6247 | 0.5024 | 0.9471 | 0.6427 | 0.6434 | +| 142 | `voiceclap_attribute:Confident_vs._Hesitant` | 3368 | 0.2211 | 0.4716 | 0.8456 | 0.8455 | 0.2246 | 0.4791 | 0.8385 | 0.8364 | +| 143 | `voiceclap_attribute:Confusion` | 3544 | 0.4366 | 1.3024 | 0.4817 | 0.4449 | 0.4303 | 1.2836 | 0.5233 | 0.4768 | +| 144 | `voiceclap_attribute:Contemplation` | 3544 | 0.6185 | 1.5260 | 0.6700 | 0.6556 | 0.6125 | 1.5110 | 0.6794 | 0.6710 | +| 145 | `voiceclap_attribute:Contempt` | 3544 | 0.3968 | 1.1680 | 0.5738 | 0.3881 | 0.3705 | 1.0907 | 0.6316 | 0.4085 | +| 146 | `voiceclap_attribute:Contentment` | 3544 | 0.6851 | 2.2319 | 0.6315 | 0.6411 | 0.6264 | 2.0406 | 0.6953 | 0.6942 | +| 147 | `voiceclap_attribute:Disappointment` | 3544 | 0.5357 | 1.8439 | 0.6072 | 0.5267 | 0.4829 | 1.6620 | 0.6753 | 0.5702 | +| 148 | `voiceclap_attribute:Disgust` | 3544 | 0.2720 | 1.0882 | 0.5145 | 0.3850 | 0.2585 | 1.0342 | 0.6078 | 0.4214 | +| 149 | `voiceclap_attribute:Distress` | 3544 | 0.5497 | 1.5707 | 0.7163 | 0.6190 | 0.4861 | 1.3889 | 0.7655 | 0.6572 | +| 150 | `voiceclap_attribute:Doubt` | 3544 | 0.5253 | 2.1012 | 0.5091 | 0.5297 | 0.5016 | 2.0064 | 0.5578 | 0.5718 | +| 151 | `voiceclap_attribute:Elation` | 3544 | 0.4601 | 1.1538 | 0.6368 | 0.5550 | 0.4292 | 1.0765 | 0.6666 | 0.5830 | +| 152 | `voiceclap_attribute:Embarrassment` | 3544 | 0.1896 | 0.6023 | 0.3048 | 0.2565 | 0.1799 | 0.5715 | 0.2576 | 0.2234 | +| 153 | `voiceclap_attribute:Emotional_Numbness` | 3544 | 0.2522 | 0.9473 | 0.3121 | 0.2899 | 0.2507 | 0.9419 | 0.3167 | 0.2929 | +| 154 | `voiceclap_attribute:Fatigue_Exhaustion` | 3544 | 0.5180 | 1.9897 | 0.7102 | 0.6597 | 0.4850 | 1.8632 | 0.7385 | 0.6754 | +| 155 | `voiceclap_attribute:Fear` | 3544 | 0.2930 | 1.1722 | 0.5463 | 0.4014 | 0.2913 | 1.1654 | 0.5694 | 0.4252 | +| 156 | `voiceclap_attribute:Gender` | 3368 | 0.2379 | 0.2368 | 0.9505 | 0.9383 | 0.2396 | 0.2385 | 0.9482 | 0.9338 | +| 157 | `voiceclap_attribute:Helplessness` | 3544 | 0.5094 | 1.8990 | 0.7148 | 0.5998 | 0.4535 | 1.6905 | 0.7714 | 0.6370 | +| 158 | `voiceclap_attribute:High-Pitched_vs._Low-Pitched` | 3368 | 0.1280 | 0.3427 | 0.8771 | 0.8826 | 0.1272 | 0.3405 | 0.8782 | 0.8872 | +| 159 | `voiceclap_attribute:Hope_Enthusiasm_Optimism` | 3544 | 0.6346 | 1.5974 | 0.6166 | 0.6083 | 0.5817 | 1.4642 | 0.6760 | 0.6699 | +| 160 | `voiceclap_attribute:Impatience_and_Irritability` | 3544 | 0.5706 | 1.0528 | 0.5800 | 0.4786 | 0.5425 | 1.0009 | 0.6083 | 0.5011 | +| 161 | `voiceclap_attribute:Infatuation` | 3544 | 0.1870 | 0.5623 | 0.3724 | 0.2935 | 0.1843 | 0.5543 | 0.4069 | 0.3121 | +| 162 | `voiceclap_attribute:Interest` | 3544 | 0.5735 | 1.3054 | 0.6784 | 0.6338 | 0.5473 | 1.2458 | 0.7189 | 0.6805 | +| 163 | `voiceclap_attribute:Intoxication_Altered_States_of_Consciousness` | 3544 | 0.1527 | 0.3292 | 0.2480 | 0.1951 | 0.1551 | 0.3344 | 0.2136 | 0.1762 | +| 164 | `voiceclap_attribute:Jealousy_&_Envy` | 3544 | 0.1094 | 0.4377 | 0.1123 | 0.1094 | 0.0991 | 0.3965 | 0.1322 | 0.0996 | +| 165 | `voiceclap_attribute:Longing` | 3544 | 0.4261 | 1.7046 | 0.5481 | 0.5207 | 0.4193 | 1.6770 | 0.5829 | 0.5493 | +| 166 | `voiceclap_attribute:Malevolence_Malice` | 3544 | 0.2214 | 0.6399 | 0.3811 | 0.2645 | 0.2264 | 0.6543 | 0.4078 | 0.3097 | +| 167 | `voiceclap_attribute:Monotone_vs._Expressive` | 3368 | 0.2390 | 0.4006 | 0.8655 | 0.8564 | 0.2346 | 0.3934 | 0.8682 | 0.8571 | +| 168 | `voiceclap_attribute:Pain` | 3544 | 0.3618 | 1.4471 | 0.6585 | 0.5520 | 0.3395 | 1.3579 | 0.6994 | 0.5805 | +| 169 | `voiceclap_attribute:Pleasure_Ecstasy` | 3544 | 0.3606 | 1.1972 | 0.6002 | 0.4927 | 0.3280 | 1.0889 | 0.6716 | 0.5413 | +| 170 | `voiceclap_attribute:Pride` | 3544 | 0.5591 | 1.6019 | 0.5473 | 0.5229 | 0.5450 | 1.5617 | 0.5676 | 0.5412 | +| 171 | `voiceclap_attribute:Recording_Quality` | 3368 | 0.1340 | 0.4083 | 0.9180 | 0.9200 | 0.1342 | 0.4090 | 0.9179 | 0.9209 | +| 172 | `voiceclap_attribute:Relief` | 3544 | 0.5277 | 1.2686 | 0.5372 | 0.4394 | 0.5009 | 1.2041 | 0.5875 | 0.4669 | +| 173 | `voiceclap_attribute:Sadness` | 3544 | 0.4434 | 1.4169 | 0.6306 | 0.5121 | 0.3948 | 1.2618 | 0.7219 | 0.5584 | +| 174 | `voiceclap_attribute:Serious_vs._Humorous` | 3368 | 0.2265 | 0.3971 | 0.8688 | 0.8286 | 0.2183 | 0.3827 | 0.8774 | 0.8265 | +| 175 | `voiceclap_attribute:Sexual_Lust` | 3544 | 0.1490 | 0.3565 | 0.2604 | 0.2024 | 0.1441 | 0.3448 | 0.2510 | 0.2158 | +| 176 | `voiceclap_attribute:Shame` | 3544 | 0.1468 | 0.5211 | 0.2995 | 0.1960 | 0.1480 | 0.5254 | 0.2968 | 0.1973 | +| 177 | `voiceclap_attribute:Soft_vs._Harsh` | 3368 | 0.1702 | 0.4727 | 0.8318 | 0.8489 | 0.1759 | 0.4883 | 0.8189 | 0.8327 | +| 178 | `voiceclap_attribute:Sourness` | 3544 | 0.4401 | 1.5150 | 0.5642 | 0.4561 | 0.4040 | 1.3909 | 0.6341 | 0.5007 | +| 179 | `voiceclap_attribute:Submissive_vs._Dominant` | 3368 | 0.1595 | 0.4444 | 0.8189 | 0.8267 | 0.1583 | 0.4408 | 0.8211 | 0.8262 | +| 180 | `voiceclap_attribute:Teasing` | 3544 | 0.4834 | 1.2742 | 0.6079 | 0.5211 | 0.4720 | 1.2443 | 0.6292 | 0.5347 | +| 181 | `voiceclap_attribute:Thankfulness_Gratitude` | 3544 | 0.3695 | 0.6980 | 0.5228 | 0.4199 | 0.3478 | 0.6570 | 0.6076 | 0.4657 | +| 182 | `voiceclap_attribute:Triumph` | 3544 | 0.4139 | 1.1535 | 0.5471 | 0.4353 | 0.4051 | 1.1293 | 0.5561 | 0.4452 | +| 183 | `voiceclap_attribute:Valence` | 3368 | 0.4702 | 0.7416 | 0.7155 | 0.7111 | 0.4603 | 0.7259 | 0.7213 | 0.7128 | +| 184 | `voiceclap_attribute:Vulnerable_vs._Emotionally_Detached` | 3368 | 0.2181 | 0.3924 | 0.8508 | 0.8581 | 0.2110 | 0.3796 | 0.8608 | 0.8564 | +| 185 | `voiceclap_attribute:Warm_vs._Cold` | 3368 | 0.2497 | 0.6254 | 0.7692 | 0.7600 | 0.2392 | 0.5989 | 0.7818 | 0.7665 | +| 186 | `voiceclap_attribute:duration` | 3368 | 0.9696 | 0.1531 | 0.9725 | 0.9677 | 1.0755 | 0.1698 | 0.9654 | 0.9629 | +| 187 | `voiceclap_attribute:score_background_quality` | 3368 | 0.1229 | 0.2464 | 0.8736 | 0.8523 | 0.1219 | 0.2446 | 0.8726 | 0.8543 | +| 188 | `voiceclap_attribute:score_content_enjoyment` | 3368 | 0.0622 | 0.2489 | 0.9033 | 0.9136 | 0.0648 | 0.2593 | 0.8934 | 0.9053 | +| 189 | `voiceclap_attribute:score_overall_quality` | 3368 | 0.0981 | 0.2378 | 0.9158 | 0.9063 | 0.0979 | 0.2374 | 0.9155 | 0.9048 | +| 190 | `voiceclap_attribute:score_speech_quality` | 3368 | 0.0272 | 0.1087 | 0.8570 | 0.8576 | 0.0273 | 0.1091 | 0.8536 | 0.8539 | +| 191 | `voiceclap_attribute:talking_speed` | 3368 | 1.4987 | 0.3994 | 0.9193 | 0.9197 | 1.3978 | 0.3725 | 0.9310 | 0.9277 | + +## Canonical 53-class vocal-burst vocabulary + +The classifier predicts these categories. Fine labels outside this vocabulary use the explicit +aliases/fallbacks in `gemini_burst_mapping.json`; the fine-to-group23 map is also preserved. + +| ID | Canonical vocal-burst class | +| ---: | --- | +| 0 | Other / rare burst | +| 1 | Affirmative Grunt | +| 2 | Ahem | +| 3 | Breath | +| 4 | Breathy Giggle | +| 5 | Cackle | +| 6 | Childlike Giggle | +| 7 | Chuckle | +| 8 | Contented Sigh | +| 9 | Cough | +| 10 | Crying | +| 11 | Displeased Grunt | +| 12 | Effort Grunt | +| 13 | Exasperated Sigh | +| 14 | Exhausted Groan | +| 15 | Fearful Gasp | +| 16 | Frustrated Groan | +| 17 | Growl | +| 18 | Guffaw | +| 19 | Gulp | +| 20 | Hiccup | +| 21 | Hum | +| 22 | Kissing Sound | +| 23 | Laughter | +| 24 | Lip Smack | +| 25 | Low Mumble | +| 26 | Mournful Wail | +| 27 | Nervous Giggle | +| 28 | Nervous Gulp | +| 29 | Purr | +| 30 | Quiet Sob | +| 31 | Relief Sigh | +| 32 | Scream | +| 33 | Sharp Inhale | +| 34 | Sharp Whistle | +| 35 | Shriek | +| 36 | Sigh | +| 37 | Sneeze | +| 38 | Snicker | +| 39 | Sniff | +| 40 | Snort | +| 41 | Snorting Giggle | +| 42 | Soft Whistle | +| 43 | Spitting | +| 44 | Surprised Gasp | +| 45 | Swallows | +| 46 | Throat Clearing | +| 47 | Tongue Click | +| 48 | Trembling Whimper | +| 49 | Tsk | +| 50 | Whispered Mumble | +| 51 | Wistful Sigh | +| 52 | Yawn | diff --git a/base/checkpoint.pt b/base/checkpoint.pt new file mode 100644 index 0000000000000000000000000000000000000000..ec1cdfdbb987194b97ffb35814b55bca71d3ea7e --- /dev/null +++ b/base/checkpoint.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:02ea9f6203a1d12590c36d34ba2f4e1bfa9034cae7018fcf1489bf1a6934ba0b +size 317713823 diff --git a/base/config.json b/base/config.json new file mode 100644 index 0000000000000000000000000000000000000000..3b11162fdbc29cca0db854b3d642fefd43111c2d --- /dev/null +++ b/base/config.json @@ -0,0 +1,144 @@ +{ + "_name_or_path": "laion/whisper-base-small-gemini-audio-scores/base", + "activation_dropout": 0.0, + "activation_function": 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Boundaries use fixed 0.5 onset threshold and native-feature interpolation; IoU matching uses Hungarian assignment.", + "split": "validation", + "phase": "gemini", + "domain": "Flash", + "split_scope": "Same Flash validation audio before/after; used to select tuned checkpoint" +} diff --git a/gemini_burst_mapping.json b/gemini_burst_mapping.json new file mode 100644 index 0000000000000000000000000000000000000000..a7b2cc89fd37867dfe124cfa3cef18d193f3fa16 --- /dev/null +++ b/gemini_burst_mapping.json @@ -0,0 +1,237 @@ +{ + "fine_to_group23": { + "Breathy Giggle": "laugh_soft", + "Cackle": "laugh_loud", + "Childlike Giggle": "laugh_soft", + "Chuckle": "laugh_soft", + "Guffaw": "laugh_loud", + "Nervous Giggle": "laugh_soft", + "Snicker": "laugh_soft", + "Snorting Giggle": "laugh_soft", + "Convulsive Sob": "sob", + "Mournful Wail": "wail", + "Quiet Sob": "sob", + "Sobs": "sob", + "Trembling Whimper": "sob", + "Deep Breath": "breath_calm", + "Deep Breathing": "breath_calm", + "Fast Breathing": "breath_fast", + "Heavy Breathing": "breath_fast", + "Normal Breathing": "breath_calm", + "Panting": "breath_fast", + "Slow Breathing": "breath_calm", + "Contented Sigh": "sigh_pos", + "Exasperated Sigh": "sigh_neg", + "Relief Sigh": "sigh_pos", + "Wistful Sigh": "sigh_pos", + "Fearful Gasp": "gasp", + "Sharp Inhale": "gasp", + "Surprised Gasp": "gasp", + "Exhausted Groan": "sigh_neg", + "Frustrated Groan": "sigh_neg", + "Pain Moan": "moan", + "Pleasure Moan": "moan", + "Affirmative Grunt": "grunt", + "Displeased Grunt": "sigh_neg", + "Effort Grunt": "grunt", + "Ahem": "throat", + "Clears Throat": "throat", + "Growl": "growl", + "Gurgling": "misc_body", + "Hiss": "hiss", + "Low Mumble": "hum", + "Whispered Mumble": "hum", + "Humming": "hum", + "Purr": "hum", + "Resonant Hum": "hum", + "Soft Hum": "hum", + "Cough": "throat", + "Coughing": "throat", + "Hiccup": "hiccup", + "Hiccups": "hiccup", + "Sniff": "sniff", + "Snort": "sniff", + "Yawn": "yawn", + "Person Whistling Playfully": "whistle", + "Person Whistling to Get Attention": "whistle", + "Sharp Whistle": "whistle", + "Soft Whistle": "whistle", + "Wolf Whistle": "whistle", + "Blowing a Kiss": "mouth", + "Kissing Noises": "mouth", + "Kissing Sounds": "mouth", + "Lip Smack": "mouth", + "Smack One's Lips": "mouth", + "Smacks Lips": "mouth", + "Licking Sound": "mouth", + "Spitting": "mouth", + "Sucking Noise": "mouth", + "Click One's Tongue": "mouth", + "Clicks Tongue": "mouth", + "Tongue Click": "mouth", + "Tsk": "mouth", + "Chewing Noises": "mouth", + "Drinking Noises": "swallow", + "Gulps": "swallow", + "Nervous Gulp": "swallow", + "Slurping Noises": "swallow", + "Swallows": "swallow", + "Scream": "scream", + "Shriek": "scream" + }, + "fine_to_checkpoint_class": { + "Breathy Giggle": 4, + "Cackle": 5, + "Childlike Giggle": 6, + "Chuckle": 7, + "Guffaw": 18, + "Nervous Giggle": 27, + "Snicker": 38, + "Snorting Giggle": 41, + "Convulsive Sob": 0, + "Mournful Wail": 26, + "Quiet Sob": 30, + "Sobs": 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Gemini-tuned encoder-only Whisper Base/Small models.""" +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from safetensors.torch import load_file +from scipy.signal import resample_poly +from transformers import WhisperFeatureExtractor + +from model import LayeredMultiTaskWhisper + + +def read_audio(path): + wave, rate = sf.read(path, dtype='float32', always_2d=True) + wave = wave.mean(axis=1) + duration = len(wave) / rate + if not .1 <= duration <= 30.: + raise ValueError(f'Audio must last 0.1–30 seconds; received {duration:.3f}s') + if not np.isfinite(wave).all(): + raise ValueError('Audio contains nonfinite values') + if rate != 16000: + divisor = math.gcd(rate, 16000) + wave = resample_poly(wave, 16000 // divisor, rate // divisor).astype(np.float32) + return wave[:480000], duration + + +class Predictor: + """Load once and reuse. Local safe weights; no teachers or ASR decoder.""" + def __init__(self, repo_dir, model_size='small', device='cpu', threads=4): + if model_size not in ('base', 'small') or threads < 1: + raise ValueError('Use base/small and at least one CPU thread') + torch.set_num_threads(threads) + self.repo = Path(repo_dir) + self.model_size, self.device = model_size, device + self.norm = json.loads((self.repo / 'training_normalization.json').read_text()) + self.display = json.loads((self.repo / 'display_normalization.json').read_text()) + self.class_names = json.loads((self.repo / 'classes.json').read_text())['names'] + self.names = self.norm['score_names'] + if self.names != [r['name'] for r in self.display['targets']]: + raise ValueError('Display and training target orders differ') + directory = self.repo / model_size + self.model = LayeredMultiTaskWhisper(directory, len(self.class_names), initialize_pretrained=False) + self.model.load_state_dict(load_file(directory / 'model.safetensors', device='cpu'), strict=True) + self.model.to(device).eval() + self.extractor = WhisperFeatureExtractor.from_pretrained(directory, local_files_only=True) + + def predict(self, audio, *, frame_threshold=.5, event_threshold=.5, max_events=32, include_frames=False): + if not 0 < frame_threshold < 1 or not 0 < event_threshold < 1 or max_events < 1: + raise ValueError('Thresholds must be between zero and one, max_events positive') + wave, duration = read_audio(audio) + features = self.extractor([wave], sampling_rate=16000, padding='longest', truncation=True, + max_length=480000, return_attention_mask=True, return_tensors='np') + mel = features['input_features'].astype(np.float32) + mask = features['attention_mask'].astype(np.int64) + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + mask = np.pad(mask, ((0, 0), (0, 1))) + from contextlib import nullcontext + autocast = torch.autocast('cuda', dtype=torch.bfloat16) if self.device.startswith('cuda') else nullcontext() + with torch.inference_mode(), autocast: + out = self.model(torch.from_numpy(mel).to(self.device), torch.from_numpy(mask).to(self.device), + predict_events=True, event_threshold=event_threshold, max_pred_events=max_events) + standardized = out['scores'][0].float().cpu().numpy() + raw = standardized * np.asarray(self.norm['score_std']) + np.asarray(self.norm['score_mean']) + z = {name: ((float(value) - row['median']) / row['std'] + if row['median'] is not None and row['std'] not in (None, 0) else None) + for name, value, row in zip(self.names, raw, self.display['targets'])} + count = min(int((mask.sum() + 1) // 2), out['frame'].shape[1]) + frame = out['frame'][0, :count].float().sigmoid().cpu().numpy() + edges = np.diff(np.pad((frame >= frame_threshold).astype(np.int8), (1, 1))) + regions = [{'start_s': round(float(a) * .02, 6), 'end_s': round(min(float(b) * .02, duration), 6)} + for a, b in zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1)) + if float(a) * .02 < duration] + probabilities = out['event_class'][0].float().softmax(-1).cpu().numpy() + starts = out['predicted_event_starts'][0].cpu().numpy() + ends = out['predicted_event_ends'][0].cpu().numpy() + valid = out['predicted_event_valid'][0].cpu().numpy() + onset = out['onset'][0].float().sigmoid().cpu().numpy() + proposals = [] + for j in np.flatnonzero(valid): + start, end = float(starts[j]) * .02, min(float(ends[j]) * .02, duration) + if start >= end: + continue + top = np.argsort(probabilities[j])[-3:][::-1] + proposals.append({'start_s': round(start, 6), 'end_s': round(end, 6), + 'onset_probability': float(onset[int(starts[j])]), + 'top3_classes': [{'label': self.class_names[int(k)], + 'softmax_probability': float(probabilities[j, k])} for k in top]}) + cps_z = float(out['cps'][0].float().cpu()) + result = { + 'model': self.model_size, 'checkpoint': 'best Gemini Flash 3.8 fine-tune; epoch 2', + 'duration_s': duration, + 'scores_raw': dict(zip(self.names, map(float, raw))), + 'scores_training_z': dict(zip(self.names, map(float, standardized))), + 'scores_z': z, + 'characters_per_second': cps_z * self.norm['cps_std'] + self.norm['cps_mean'], + 'characters_per_second_training_z': cps_z, + 'estimated_burst_count': float(np.expm1(np.clip(raw[130], 0, 8))), + 'orange_timbre_128d': out['timbre'][0].float().cpu().tolist(), + 'orange_identity_250d': out['identity'][0].float().cpu().tolist(), + 'binary_burst_regions': regions, 'burst_event_proposals': proposals, + 'decoding': {'frame_threshold': frame_threshold, 'event_threshold': event_threshold, 'max_events': max_events}, + 'notes': 'Encoder only: no transcript or captions. Raw regression scores are not clipped to ordinal scales. ' + 'Display z-scores use the original train-only reference median/std; they are not probabilities. ' + 'Class probabilities are uncalibrated. Blend is only meaningful when vocal bursts are present.', + } + if include_frames: + result['burst_probability_per_20ms_frame'] = frame.tolist() + return result + + +def predict(audio, repo_dir, *, model_size='small', device='cpu', threads=4, **options): + """Convenience call; reuse Predictor for many clips to avoid reloading weights.""" + return Predictor(repo_dir, model_size, device, threads).predict(audio, **options) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('audio', type=Path) + parser.add_argument('--repo-dir', type=Path, default=Path(__file__).resolve().parent) + parser.add_argument('--model', choices=('base', 'small'), default='small') + parser.add_argument('--device', default='cpu') + parser.add_argument('--threads', type=int, default=4) + parser.add_argument('--frame-threshold', type=float, default=.5) + parser.add_argument('--event-threshold', type=float, default=.5) + parser.add_argument('--max-events', type=int, default=32) + parser.add_argument('--include-frame-probabilities', action='store_true') + args = parser.parse_args() + result = predict(args.audio, args.repo_dir, model_size=args.model, device=args.device, threads=args.threads, + frame_threshold=args.frame_threshold, event_threshold=args.event_threshold, + max_events=args.max_events, include_frames=args.include_frame_probabilities) + print(json.dumps(result, indent=2, ensure_ascii=False, allow_nan=False)) + + +if __name__ == '__main__': + main() diff --git a/model.py b/model.py new file mode 100644 index 0000000000000000000000000000000000000000..9080bdb80574da8aa1a071f7dba4b7c3268c06db --- /dev/null +++ b/model.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +"""Encoder-only Whisper with layer-mean residual experts and event-local classes.""" +from __future__ import annotations + +from pathlib import Path + +import torch +from safetensors import safe_open +from torch import nn +from torch.nn import functional as F +from transformers import WhisperConfig +from transformers.models.whisper.modeling_whisper import WhisperEncoder + + +SCORE_FAMILIES={ + 'emotion':(0,40,64), + 'voicenet':(40,97,32), + 'genuineness_blend_quality':(97,100,32), + 'empathic_plus':(100,119,64), + 'audiobox':(119,123,32), + 'dnsmos':(123,130,32), + 'burst_count':(130,131,32), + 'voiceclap_attributes':(131,192,64), +} + + +class LayerMixture(nn.Module): + """Project each layer's masked mean, then learn a sample-specific layer mix.""" + def __init__(self, dim: int, width: int, layers: int): + super().__init__() + self.project=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,width), + nn.GELU(),nn.Linear(width,width)) + self.gate=nn.Linear(width,1) + self.layer_bias=nn.Parameter(torch.zeros(layers)) + self.out_norm=nn.LayerNorm(width) + + def forward(self, means: torch.Tensor) -> torch.Tensor: + # means: [batch, encoder layers, hidden width] + z=self.project(means) + weights=(self.gate(z).squeeze(-1)+self.layer_bias).softmax(dim=1) + return self.out_norm((z*weights.unsqueeze(-1)).sum(dim=1)) + + +class LayeredMultiTaskWhisper(nn.Module): + def __init__(self, model_path: str | Path, n_event_classes: int, + *, initialize_pretrained: bool = True): + super().__init__() + model_path=Path(model_path) + config=WhisperConfig.from_pretrained(model_path,local_files_only=True) + self.encoder=WhisperEncoder(config) + if initialize_pretrained: + with safe_open(model_path/'model.safetensors',framework='pt',device='cpu') as file: + prefix='model.encoder.' + pretrained={name[len(prefix):]:file.get_tensor(name) for name in file.keys() + if name.startswith(prefix)} + self.encoder.load_state_dict(pretrained,strict=True) + self.hidden_dim=config.d_model + self.num_layers=config.encoder_layers + self.n_event_classes=n_event_classes + pooled_dim=config.d_model*4 + + def clip_head(inputs: int, outputs: int): + return nn.Sequential(nn.LayerNorm(inputs),nn.Linear(inputs,512), + nn.GELU(),nn.Linear(512,outputs)) + + # A direct final-layer route plus learned residuals from every layer. + self.score_head=clip_head(pooled_dim,192) + self.score_mix=nn.ModuleDict() + self.score_delta=nn.ModuleDict() + for family,(start,stop,width) in SCORE_FAMILIES.items(): + self.score_mix[family]=LayerMixture(config.d_model,width,self.num_layers) + self.score_delta[family]=nn.Linear(width,stop-start) + nn.init.zeros_(self.score_delta[family].weight) + nn.init.zeros_(self.score_delta[family].bias) + + self.timbre_mix=LayerMixture(config.d_model,128,self.num_layers) + self.identity_mix=LayerMixture(config.d_model,128,self.num_layers) + self.timbre_head=clip_head(pooled_dim+128,128) + self.identity_head=clip_head(pooled_dim+128,250) + self.cps_mix=LayerMixture(config.d_model,32,self.num_layers) + self.cps_head=clip_head(pooled_dim+32,1) + + self.frame_head=nn.Sequential(nn.LayerNorm(config.d_model), + nn.Linear(config.d_model,config.d_model//2), + nn.GELU(), + nn.Conv1d(config.d_model//2,config.d_model//2,7,padding=3), + nn.GELU(),nn.Conv1d(config.d_model//2,1,1)) + # Independent onset/duration proposals keep overlapping source events separate. + self.proposal_head=nn.Conv1d(config.d_model//2,2,3,padding=1) + self.event_mix=LayerMixture(config.d_model,64,self.num_layers) + self.event_head=nn.Sequential(nn.LayerNorm(config.d_model+64), + nn.Linear(config.d_model+64,256),nn.GELU(), + nn.Linear(256,n_event_classes)) + + @staticmethod + def _event_means(h: torch.Tensor, starts: torch.Tensor, + ends: torch.Tensor) -> torch.Tensor: + n=h.shape[1] + starts=starts.clamp(0,n) + ends=ends.clamp(0,n) + prefix=F.pad(h.float().cumsum(dim=1),(0,0,1,0)) + width=h.shape[-1] + left=prefix.gather(1,starts.unsqueeze(-1).expand(-1,-1,width)) + right=prefix.gather(1,ends.unsqueeze(-1).expand(-1,-1,width)) + return (right-left)/(ends-starts).clamp_min(1).unsqueeze(-1) + + def forward(self, mel: torch.Tensor, mel_mask: torch.Tensor, + event_starts: torch.Tensor | None = None, + event_ends: torch.Tensor | None = None, + *, predict_events: bool = False, + event_threshold: float = .5, + max_pred_events: int = 10) -> dict[str,torch.Tensor]: + enc=self.encoder + h=F.gelu(enc.conv1(mel)) + h=F.gelu(enc.conv2(h)).transpose(1,2) + n=h.shape[1] + h=h+enc.embed_positions(torch.arange(n,device=h.device)) + valid=torch.arange(n,device=h.device)[None,:]<((mel_mask.sum(1)+1)//2)[:,None] + attention=torch.zeros((len(h),1,1,n),device=h.device,dtype=h.dtype) + attention.masked_fill_(~valid[:,None,None,:],torch.finfo(h.dtype).min) + mask=valid.unsqueeze(-1) + count=mask.sum(1).clamp_min(1) + layer_means=[] + event_layers=[] + layer_frames=[] + if (event_starts is None)!=(event_ends is None): + raise ValueError('event starts and ends must be provided together') + if predict_events and event_starts is not None: + raise ValueError('Use ground-truth event spans or predicted spans, not both') + for layer in enc.layers: + h=layer(h,attention) + layer_means.append((h.float()*mask).sum(1)/count) + if event_starts is not None: + event_layers.append(self._event_means(h,event_starts,event_ends)) + elif predict_events: + layer_frames.append(h) + h=enc.layer_norm(h).float() + mean=(h*mask).sum(1)/count + var=((h-mean[:,None,:]).square()*mask).sum(1)/count + minimum=h.masked_fill(~mask,torch.inf).amin(1) + maximum=h.masked_fill(~mask,-torch.inf).amax(1) + pooled=torch.cat((mean,minimum,maximum,var.clamp_min(1e-8).sqrt()),dim=-1) + means=torch.stack(layer_means,dim=1) + + deltas=torch.cat([self.score_delta[family](self.score_mix[family](means)) + for family in SCORE_FAMILIES],dim=-1) + score=self.score_head(pooled)+deltas + timbre=F.normalize(self.timbre_head(torch.cat((pooled,self.timbre_mix(means)),dim=-1)).float(),dim=-1) + identity=F.normalize(self.identity_head(torch.cat((pooled,self.identity_mix(means)),dim=-1)).float(),dim=-1) + cps=self.cps_head(torch.cat((pooled,self.cps_mix(means)),dim=-1)).squeeze(-1) + x=self.frame_head[0](h) + x=self.frame_head[1](x) + x=self.frame_head[2](x).transpose(1,2) + frame=self.frame_head[3:](x).squeeze(1) + proposal=self.proposal_head(x) + onset=proposal[:,0,:] + log_duration=proposal[:,1,:] + output={'scores':score,'frame':frame,'timbre':timbre, + 'identity':identity,'cps':cps, + 'onset':onset,'log_duration':log_duration} + if predict_events: + if not 0=event_threshold)&valid[i] + padded=F.pad(probabilities[i],(1,1),value=-1.) + peaks=active&(probabilities[i]>=padded[:-2])&( + probabilities[i]>padded[2:]) + ranked=torch.nonzero(peaks).flatten().tolist() + ranked.sort(key=lambda start:float(probabilities[i,start]),reverse=True) + chosen=[] + for start in ranked: + if any(abs(start-old)<3 for old in chosen): + continue + chosen.append(start) + if len(chosen)>=max_pred_events: + break + row=[] + for start in chosen: + duration=int(round(float(log_duration[i,start].float().clamp(0,8).expm1()))) + end=min(int(valid[i].sum()),start+max(1,duration)) + row.append((start,end)) + spans.append(sorted(row)) + width=max(1,max(len(row) for row in spans)) + event_starts=torch.zeros((len(h),width),device=h.device,dtype=torch.int64) + event_ends=torch.zeros_like(event_starts) + event_valid=torch.zeros_like(event_starts,dtype=torch.bool) + for i,row in enumerate(spans): + for j,(start,end) in enumerate(row): + event_starts[i,j]=start + event_ends[i,j]=end + event_valid[i,j]=True + event_layers=[self._event_means(layer,event_starts,event_ends) + for layer in layer_frames] + output.update(predicted_event_starts=event_starts, + predicted_event_ends=event_ends, + predicted_event_valid=event_valid) + if event_starts is not None: + local=self._event_means(h,event_starts,event_ends) + per_layer=torch.stack(event_layers,dim=2) + b,e,l,d=per_layer.shape + mixed=self.event_mix(per_layer.reshape(b*e,l,d)).reshape(b,e,-1) + output['event_class']=self.event_head(torch.cat((local,mixed),dim=-1)) + return output diff --git a/release_manifest.json b/release_manifest.json new file mode 100644 index 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:= stream.read(8 * 1024 * 1024): + h.update(part) + return h.hexdigest() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--model', choices=('base', 'small'), required=True) + args = parser.parse_args() + size = args.model + torch.set_num_threads(2) + source = GEMINI / 'training' / ('whisper_' + size) / 'best.pt' + checkpoint = torch.load(source, map_location='cpu', weights_only=False) + assert checkpoint['epoch_completed'] == 2 and checkpoint['update'] == 914 + norm = json.loads((RELEASE / 'training_normalization.json').read_text()) + for key in ['score_names', 'score_mean', 'score_std', 'cps_mean', 'cps_std']: + assert checkpoint['normalization'][key] == norm[key], key + state = {k: v.detach().contiguous() for k, v in checkpoint['model'].items()} + assert all(not k.startswith(('decoder.', 'model.decoder.')) for k in state) + output = RELEASE / size / 'model.safetensors' + save_file(state, output, metadata={'format': 'pt', 'author': 'Christoph Schuhmann', + 'license': 'cc-by-4.0', 'checkpoint_sha256': digest(source), + 'epochs': '2', 'update': '914', 'teacher': 'gemini-3.8-flash'}) + restored = load_file(output) + assert set(restored) == set(state) + assert all(torch.equal(state[k], restored[k]) for k in state) + sys.path.insert(0, str(RELEASE)) + from inference import Predictor + from model import LayeredMultiTaskWhisper + device = 'cuda:' + os.environ.get('LOCAL_RANK', '0') + predictor = Predictor(RELEASE, size, device, threads=2) + original = LayeredMultiTaskWhisper(RELEASE / size, 53, initialize_pretrained=False).to(device).eval() + original.load_state_dict(checkpoint['model'], strict=True) + assert sum(p.numel() for p in original.parameters()) == json.loads( + (RELEASE / 'training/configs' / (size + '_recorded_run.json')).read_text())['trainable_total_parameters'] + # Exact saved-weights parity, including layer-mixture and event heads. + generator = torch.Generator().manual_seed(20261007) + mel = torch.randn((1, 80, 100), generator=generator).to(device) + mask = torch.ones((1, 100), dtype=torch.long, device=device) + with torch.inference_mode(): + a = original(mel, mask, predict_events=True, max_pred_events=32) + b = predictor.model(mel, mask, predict_events=True, max_pred_events=32) + assert set(a) == set(b) + assert all(torch.equal(a[k], b[k]) for k in a), 'Export altered inference outputs' + del original, checkpoint, state, restored, a, b + # End-to-end new CLI/API on an actual held-out waveform, including resampling. + row = None + with (GEMINI / 'prepared/targets.jsonl').open() as stream: + for line in stream: + candidate = json.loads(line) + if candidate['split'] == 'test' and candidate['ready_for_whisper_training']: + row = candidate + break + sys.path.insert(0, str(RELEASE / 'training/gemini_finetune')) + from common import TarReader + wave = TarReader().audio(row, verify=True) + with tempfile.TemporaryDirectory(prefix='whisper-release-') as directory: + path = Path(directory) / 'heldout.wav' + sf.write(path, wave, 16000, subtype='FLOAT') + result = predictor.predict(path, include_frames=True) + assert len(result['scores_raw']) == len(result['scores_z']) == len(result['scores_training_z']) == 192 + assert len(result['orange_timbre_128d']) == 128 and len(result['orange_identity_250d']) == 250 + assert len(result['burst_probability_per_20ms_frame']) > 0 + json.dumps(result, allow_nan=False) + for event in result['binary_burst_regions'] + result['burst_event_proposals']: + assert 0 <= event['start_s'] < event['end_s'] <= result['duration_s'] + 1e-6 + for event in result['burst_event_proposals']: + assert len(event['top3_classes']) == 3 + # The advertised one-core CPU entry point also runs on real audio. + cpu = Predictor(RELEASE, size, 'cpu', threads=1) + short = Path(directory) / 'heldout-short.wav' + sf.write(short, wave[:16000], 16000, subtype='FLOAT') + cpu_result = cpu.predict(short) + json.dumps(cpu_result, allow_nan=False) + assert len(cpu_result['scores_raw']) == 192 + evidence = {'model': size, 'checkpoint_epoch': 2, 'updates': 914, + 'source_checkpoint_sha256': digest(source), 'safe_weights_sha256': digest(output), + 'safe_weights_bytes': output.stat().st_size, + 'all_tensors_bitwise_equal': True, 'all_model_outputs_bitwise_equal': True, + 'heldout_audio_sha256': row['sha256_mp3'], 'heldout_duration_s': result['duration_s'], + 'targets': 192, 'speaker_dimensions': [128, 250], 'finite_outputs': True, + 'timestamps_have_start_and_end': True, 'device': device, + 'one_thread_cpu_inference_passed': True, + 'torch': torch.__version__, 'slurm_job': os.environ.get('SLURM_JOB_ID')} + path = RELEASE / 'verification' / (size + '.json') + path.parent.mkdir(exist_ok=True) + path.write_text(json.dumps(evidence, indent=2) + '\n') + print(json.dumps(evidence), flush=True) + + +if __name__ == '__main__': + main() diff --git a/release_tools/export_rank.py b/release_tools/export_rank.py new file mode 100644 index 0000000000000000000000000000000000000000..d10f3fb038f05a42fc65e028479fd45510fc7183 --- /dev/null +++ b/release_tools/export_rank.py @@ -0,0 +1,7 @@ +import os +import subprocess +import sys +from pathlib import Path + +model = ('base', 'small')[int(os.environ['LOCAL_RANK'])] +subprocess.run([sys.executable, str(Path(__file__).with_name('export_and_verify.py')), '--model', model], check=True) diff --git a/release_tools/package_release.py b/release_tools/package_release.py new file mode 100644 index 0000000000000000000000000000000000000000..30cbabeb929bfb6e3a0d065c85bd3f488861027a --- /dev/null +++ b/release_tools/package_release.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +"""Build a credential-free, reproducible release from completed training artifacts.""" +import ast +import hashlib +import json +import shutil +from collections import Counter, defaultdict +from pathlib import Path + +HERE = Path(__file__).resolve().parent +CODE = HERE.parent +SCRATCH = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression') +GEMINI = SCRATCH / 'gemini_finetune_preparation_20261005' +OLD = SCRATCH / 'hf_layered_release/model' +STUDY = SCRATCH / 'embedding_probe_study_20261006' +OUT = SCRATCH / 'hf_gemini_release_20261007/model' +REPO = 'laion/whisper-base-small-gemini-audio-scores' +BENCHMARK = 'https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html' + + +def read(path): + return json.loads(Path(path).read_text()) + + +def write(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False) + '\n') + + +def copy(source, relative): + destination = OUT / relative + destination.parent.mkdir(parents=True, exist_ok=True) + if destination.suffix != '.pt' or not destination.exists() or destination.stat().st_size != Path(source).stat().st_size: + shutil.copy2(source, destination) + + +def dependency_closure(names): + pending, seen = list(names), set() + while pending: + name = pending.pop() + path = CODE / (name + '.py') + if name in seen or not path.exists(): + continue + seen.add(name) + for node in ast.walk(ast.parse(path.read_text())): + if isinstance(node, ast.Import): + modules = [n.name.split('.')[0] for n in node.names] + elif isinstance(node, ast.ImportFrom): + modules = [(node.module or '').split('.')[0]] + else: + continue + pending.extend(m for m in modules if (CODE / (m + '.py')).exists()) + return seen + + +def stage(): + OUT.mkdir(parents=True, exist_ok=True) + for filename in ('LICENSE', 'classes.json', 'display_normalization.json'): + copy(OLD / filename, filename) + copy(GEMINI / 'prepared/checkpoint_normalization.json', 'training_normalization.json') + copy(GEMINI / 'prepared/gemini_train_statistics.json', 'gemini_train_statistics.json') + copy(GEMINI / 'prepared/gemini_burst_mapping.json', 'gemini_burst_mapping.json') + copy(CODE / 'layered_multitask_model.py', 'model.py') + shutil.copy2(HERE / 'inference.py', OUT / 'inference.py') + copy(OLD / 'requirements.txt', 'requirements.txt') + (OUT / 'requirements-training.txt').write_text('-r requirements.txt\npyarrow>=20\n') + for size in ('base', 'small'): + configuration = read(OLD / size / 'config.json') + configuration.update(architectures=['LayeredMultiTaskWhisper'], is_encoder_decoder=False, + use_cache=False, _name_or_path=REPO + '/' + size) + write(OUT / size / 'config.json', configuration) + copy(OLD / size / 'preprocessor_config.json', size + '/preprocessor_config.json') + train = GEMINI / 'training' / ('whisper_' + size) + copy(train / 'best.pt', size + '/checkpoint.pt') + copy(train / 'config.json', 'training/configs/' + size + '_recorded_run.json') + copy(train / 'metrics.jsonl', 'training/logs/' + size + '_epochs.jsonl') + copy(train / 'COMPLETE.json', 'training/logs/' + size + '_COMPLETE.json') + for kind in ['gemini_test_metrics', 'validation_metrics', 'public_metrics', + 'legacy_on_flash_test_metrics', 'legacy_p3_test_metrics', 'legacy_p3_validation_metrics', + 'gemini_p3_test_metrics', 'gemini_p3_validation_metrics', + 'crema_matched_adapter', 'ravdess_matched_adapter']: + copy(STUDY / 'whisper/gemini' / size / (kind + '.json'), 'evaluation/' + size + '/' + kind + '.json') + copy(OLD / 'training' / (size + '_run_config.json'), 'training/original_curriculum/' + size + '_run_config.json') + names = dependency_closure(['train_layered_curriculum', 'full_ladder_dataset', 'layered_multitask_model']) + for name in sorted(names): + copy(CODE / (name + '.py'), 'training/' + name + '.py') + for name in ['common.py', 'data.py', 'train_whisper.py', 'train_whisper.sbatch', 'prepare.py', 'backfill.py', 'configure.py']: + copy(CODE / 'gemini_finetune' / name, 'training/gemini_finetune/' + name) + for name in ['annotate.py', 'gemini_voice_annotation_master_prompt_detailed.txt']: + copy(CODE / 'gemini_s10_smoke' / name, 'training/gemini_s10_smoke/' + name) + teachers = CODE.parent / 'vocal_burst_pool/p3' + for name in ['annotate_dnsmos.py', 'annotate_audiobox.py', 'annotate_voiceclap.py', 'annotate_empathic.py']: + copy(teachers / name, 'vocal_burst_pool/p3/' + name) + for name in dependency_closure(['evaluate_public_benchmarks']): + copy(CODE / (name + '.py'), 'training/' + name + '.py') + for path in sorted((CODE / 'embedding_probe_study').glob('*.py')): + copy(path, 'training/embedding_probe_study/' + path.name) + helper = Path('/e/scratch/reformo/schuhmann1_moss/out/m2_600m_ladder/code_v4_highlr_20260920/tar_member_map.py') + if helper.exists(): + copy(helper, 'training/tar_member_map.py') + copy(SCRATCH.parent / 'code/fastgen.sh', 'training/original_curriculum/fastgen.sh') + copy(HERE / 'make_finetune_config.py', 'training/make_finetune_config.py') + for name in ['export_and_verify.py', 'export_rank.py', 'verify.sbatch', 'package_release.py', 'publish_release.py']: + copy(HERE / name, 'release_tools/' + name) + copy(SCRATCH / 'gemini_multisource_100h_20261004/combined_summary.json', 'training/provenance/selected_pool.json') + copy(GEMINI / 'prepared/TRAINING_READY.json', 'training/provenance/TRAINING_READY.json') + (OUT / 'NOTICE.md').write_text('''# License and attribution + +Author: **Christoph Schuhmann**. Organization: **LAION**. Release: 7 October 2026. +LAION's new fine-tuned weights, code and documentation: **CC BY 4.0**, see `LICENSE`. +Please credit Christoph Schuhmann and LAION, link this repository and indicate changes. + +OpenAI Whisper's original code and weights retain their MIT notice: +https://github.com/openai/whisper/blob/main/LICENSE +Hugging Face Transformers is an external Apache-2.0 dependency: +https://github.com/huggingface/transformers/blob/main/LICENSE +Teacher models and source audio retain their own terms; they are not redistributed here. +Orange's identity teacher card declares CC BY-SA 3.0: +https://huggingface.co/Orange/Speaker-wavLM-id +Orange timbre: https://huggingface.co/Orange/Speaker-wavLM-tbr +The release license is not a grant of rights over upstream datasets or packages. +''') + (OUT / '.gitattributes').write_text('*.safetensors filter=lfs diff=lfs merge=lfs -text\n*.pt filter=lfs diff=lfs merge=lfs -text\n') + print('STAGED', str(OUT), 'training dependency modules', len(names), flush=True) + + +def data_summary(): + summary = OUT / 'training/provenance/valid_split_summary.json' + if summary.exists(): + return read(summary) + counts, hours, languages, sources = Counter(), defaultdict(float), Counter(), Counter() + fingerprint = hashlib.sha256() + with (GEMINI / 'prepared/targets.jsonl').open('rb') as stream: + for raw in stream: + fingerprint.update(raw) + row = json.loads(raw) + if not row['ready_for_whisper_training']: + continue + counts[row['split']] += 1 + hours[row['split']] += row['duration_s'] / 3600 + languages[row['annotation']['language']] += 1 + for source in {m['source'] for m in row['source_memberships']}: + sources[source] += 1 + assert counts == {'train': 58380, 'validation': 3239, 'test': 3544}, counts + result = {'valid_clips': sum(counts.values()), 'split_clips': dict(counts), 'split_hours': dict(hours), + 'valid_total_hours': sum(hours.values()), 'language_strings': dict(languages), + 'source_membership_counts_nonexclusive': dict(sources), + 'prepared_targets_sha256': fingerprint.hexdigest(), + 'deduplication': 'Exact selected compressed audio SHA256 across all selected sources/tasks', + 'split': '90/5/5 deterministic group hash; explicit speaker or synthetic family before exact audio SHA', + 'excluded_clips': 1036, 'captions_used_as_model_inputs': False} + write(summary, result) + return result + + +def card(): + data = data_summary() + template = (HERE / 'README_template.md').read_text() + base = read(OUT / 'evaluation/base/gemini_test_metrics.json') + small = read(OUT / 'evaluation/small/gemini_test_metrics.json') + def num(v): + return 'Unavailable' if v is None else f'{v:.4f}' + def score(m, name, key): + return next(x[key] for x in m['per_score'] if x['score'] == name) + def family(m, prefix): + values = [x['normalized_mae'] for x in m['per_score'] if x['score'].startswith(prefix) and x['normalized_mae'] is not None] + return sum(values) / len(values) + rows = [] + for task, function in [ + ('Burst frame F1 ↑', lambda m: m['frame_f1']), + ('Burst event F1, IoU ≥0.5 ↑', lambda m: m['burst_localization']['0.5']['f1']), + ('Burst start MAE on IoU ≥0.1 matches, seconds ↓', lambda m: m['burst_start_mae_s']), + ('Burst end MAE on IoU ≥0.1 matches, seconds ↓', lambda m: m['burst_end_mae_s']), + ('53-class accuracy with reference spans ↑', lambda m: m['burst_class_oracle_span_accuracy']), + ('53-class accuracy on matched predicted spans ↑', lambda m: m['burst_matched_event_class_accuracy']), + ('Orange timbre cosine ↑ (1,718 valid single-speaker clips)', lambda m: m['speaker_cosine']['timbre']['mean']), + ('Orange identity cosine ↑ (1,718 valid single-speaker clips)', lambda m: m['speaker_cosine']['identity']['mean']), + ('CPS raw MAE, characters/second ↓', lambda m: m['cps_raw_mae']), + ('Genuineness raw MAE on 0–6 scale ↓ (3,544 clips)', lambda m: score(m, 'genuineness_0_6', 'raw_mae')), + ('Genuineness normalized MAE ↓', lambda m: score(m, 'genuineness_0_6', 'normalized_mae')), + ('Blend raw MAE on 0–10 scale ↓ (2,391 valid clips)', lambda m: score(m, 'blend_0_10', 'raw_mae')), + ('Blend normalized MAE ↓', lambda m: score(m, 'blend_0_10', 'normalized_mae')), + ('40 emotions, mean normalized MAE ↓', lambda m: family(m, 'emo_')), + ('57 VoiceNet dimensions, mean normalized MAE ↓', lambda m: family(m, 'vn_')), + ('AudioBox four axes, mean normalized MAE ↓', lambda m: family(m, 'audiobox:')), + ('DNSMOS seven outputs, mean normalized MAE ↓', lambda m: family(m, 'dnsmos:'))]: + rows.append('| ' + task + ' | ' + num(function(base)) + ' | ' + num(function(small)) + ' |') + holdout_table = '| Flash test target / metric | Base | Small |\n| --- | ---: | ---: |\n' + '\n'.join(rows) + public_rows = [] + for task, key, metric in [('EmoNet-Voice intensity, Pearson r ↑', 'emonet', 'pearson'), + ('EmoNet-Voice intensity, Spearman ρ ↑', 'emonet', 'spearman'), + ('VoiceNet-Emo, mean prompt Spearman ρ ↑', 'emolia-emo', 'mean_prompt_spearman'), + ('VoiceNet-Ext / emolia-dim, mean prompt Spearman ρ ↑', 'emolia-dim', 'mean_prompt_spearman')]: + values = [] + for size in ['base', 'small']: + result = read(OUT / 'evaluation' / size / 'public_metrics.json')[key] + values.append(result['all_mapped_40' if key == 'emonet' else 'repo_min_2_raters'][metric]) + public_rows.append('| ' + task + ' | ' + num(values[0]) + ' | ' + num(values[1]) + ' |') + for key in ['crema', 'ravdess']: + values = [read(OUT / 'evaluation' / s / (key + '_matched_adapter.json'))['metrics']['accuracy'] for s in ['base', 'small']] + public_rows.append('| ' + ('CREMA-D' if key == 'crema' else 'RAVDESS') + ' matched actor-CV score-MLP accuracy ↑ | ' + num(values[0]) + ' | ' + num(values[1]) + ' |') + public_table = '| Human-label benchmark / metric | Base | Small |\n| --- | ---: | ---: |\n' + '\n'.join(public_rows) + target_rows = [] + for index, (b, s) in enumerate(zip(base['per_score'], small['per_score'])): + assert b['score'] == s['score'] + target_rows.append('| ' + str(index) + ' | `' + b['score'] + '` | ' + str(b['n']) + ' | ' + ' | '.join( + num(m[k]) for m in [b, s] for k in ['raw_mae', 'normalized_mae', 'pearson', 'spearman']) + ' |') + target_table = '| Index | Exact target key | Valid N | Base raw MAE | Base norm. MAE | Base r | Base ρ | Small raw MAE | Small norm. MAE | Small r | Small ρ |\n| ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |\n' + '\n'.join(target_rows) + names = read(OUT / 'classes.json')['names'] + classes = '| ID | Canonical vocal-burst class |\n| ---: | --- |\n' + '\n'.join('| ' + str(i) + ' | ' + name + ' |' for i, name in enumerate(names)) + card = template.replace('{{HOLDOUT_TABLE}}', holdout_table).replace('{{PUBLIC_TABLE}}', public_table).replace('{{TARGET_TABLE}}', target_table).replace('{{CLASS_TABLE}}', classes).replace('{{VALID_HOURS}}', f"{data['valid_total_hours']:.2f}") + assert '{{' not in card.replace('author = {Schuhmann, Christoph and {LAION}}', ''), 'Unexpanded placeholder' + (OUT / 'README.md').write_text(card) + print('WROTE model card', len(card.encode()), flush=True) + + +if __name__ == '__main__': + stage() + card() diff --git a/release_tools/publish_release.py b/release_tools/publish_release.py new file mode 100644 index 0000000000000000000000000000000000000000..bf42c4c618dd429e2dd00edf2c20fb4a74a9358a --- /dev/null +++ b/release_tools/publish_release.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 +"""Publish the verified Gemini model release using the existing Hub login.""" +import hashlib +import json +import os +import re +from pathlib import Path + +os.environ.pop('HF_HUB_OFFLINE', None) +os.environ.pop('TRANSFORMERS_OFFLINE', None) +os.environ['HF_HUB_DISABLE_PROGRESS_BARS'] = '1' +from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download + +HERE = Path(__file__).resolve().parent +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_gemini_release_20261007') +FOLDER = ROOT / 'model' +REPO = 'laion/whisper-base-small-gemini-audio-scores' + + +def digest(path): + h = hashlib.sha256() + with path.open('rb') as stream: + while chunk := stream.read(8 * 1024 * 1024): + h.update(chunk) + return h.hexdigest() + + +def main(): + verification = {size: json.loads((FOLDER / 'verification' / (size + '.json')).read_text()) + for size in ('base', 'small')} + for size, evidence in verification.items(): + assert evidence['all_tensors_bitwise_equal'] and evidence['all_model_outputs_bitwise_equal'] + assert evidence['targets'] == 192 and evidence['speaker_dimensions'] == [128, 250] + assert digest(FOLDER / size / 'model.safetensors') == evidence['safe_weights_sha256'] + assert digest(FOLDER / size / 'checkpoint.pt') == evidence['source_checkpoint_sha256'] + paths = sorted(p for p in FOLDER.rglob('*') if p.is_file() and '__pycache__' not in p.parts + and '.cache' not in p.parts and p.name != 'release_manifest.json') + # Fail without printing matched values. Only source/config/docs are inspected. + patterns = [r'hf_[A-Za-z0-9]{20,}', r'AIza[A-Za-z0-9_-]{25,}', + r'(?i)(?:api_key|access_token|password)\s*[=:]\s*[\"\'][A-Za-z0-9_-]{20,}[\"\']'] + for path in paths: + if path.suffix in ('.py', '.json', '.md', '.txt', '.sbatch', '.sh', '.jsonl'): + content = path.read_text() + if any(re.search(pattern, content) for pattern in patterns): + raise RuntimeError('Potential credential in ' + str(path.relative_to(FOLDER))) + manifest = {'repo_id': REPO, 'author': 'Christoph Schuhmann', 'organization': 'LAION', + 'license': 'cc-by-4.0', 'release_date': '2026-10-07', 'variants': verification, + 'benchmark_url': 'https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html', + 'files': {str(p.relative_to(FOLDER)): {'bytes': p.stat().st_size, 'sha256': digest(p)} for p in paths}} + manifest_path = FOLDER / 'release_manifest.json' + manifest_path.write_text(json.dumps(manifest, indent=2) + '\n') + paths.append(manifest_path) + api = HfApi() + api.create_repo(repo_id=REPO, repo_type='model', private=False, exist_ok=True) + info = api.repo_info(REPO, repo_type='model') + if info.private: + raise RuntimeError('Expected a public repository') + operations = [CommitOperationAdd(path_in_repo=str(p.relative_to(FOLDER)), path_or_fileobj=str(p)) for p in paths] + print('Uploading verified release', REPO, len(operations), 'files', + round(sum(p.stat().st_size for p in paths) / 1024**3, 3), 'GiB', flush=True) + commit = api.create_commit(repo_id=REPO, repo_type='model', operations=operations, parent_commit=info.sha, + commit_message='Release best Gemini-tuned Whisper Base and Small with code, normalization and evaluation', + num_threads=4) + remote = api.model_info(REPO, revision=commit.oid, files_metadata=True) + siblings = {s.rfilename: s for s in remote.siblings} + for path in paths: + name = str(path.relative_to(FOLDER)) + assert name in siblings and siblings[name].size == path.stat().st_size, name + if siblings[name].lfs: + assert siblings[name].lfs.sha256 == digest(path), name + card = Path(hf_hub_download(REPO, 'README.md', revision=commit.oid)).read_text() + assert card == (FOLDER / 'README.md').read_text() + assert 'license: cc-by-4.0' in card and 'Christoph Schuhmann' in card + record = {'repo_id': REPO, 'url': 'https://huggingface.co/' + REPO, + 'commit': commit.oid, 'commit_url': commit.commit_url, 'public': not remote.private, + 'remote_file_count': len(paths), 'remote_sizes_and_large_file_hashes_verified': True} + (ROOT / 'publication.json').write_text(json.dumps(record, indent=2) + '\n') + print(json.dumps(record), flush=True) + + +if __name__ == '__main__': + main() diff --git a/release_tools/verify.sbatch b/release_tools/verify.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..ea976b1426f215456aeb765bf2c6afb4c0d41e1d --- /dev/null +++ b/release_tools/verify.sbatch @@ -0,0 +1,19 @@ +#!/bin/bash +#SBATCH --account=reformo +#SBATCH --partition=booster +#SBATCH --job-name=whisper-release-verify +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=16 +#SBATCH --gres=gpu:4 +#SBATCH --time=00:20:00 +#SBATCH --time-min=00:05:00 +#SBATCH --output=/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_gemini_release_20261007/verify-%j.out +#SBATCH --error=/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_gemini_release_20261007/verify-%j.err +set -euo pipefail +source /e/scratch/reformo/schuhmann1_moss/code/fastgen.sh +export PYTHONNOUSERSITE=1 OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=1 +export HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 TOKENIZERS_PARALLELISM=false +code=/e/home/jusers/schuhmann1/jupiter/whisper_score_regression/gemini_release +export RELEASE_CODE="$code" +srun --ntasks=1 python -m torch.distributed.run --standalone --nproc_per_node=2 "$code/export_rank.py" diff --git a/requirements-training.txt b/requirements-training.txt new file mode 100644 index 0000000000000000000000000000000000000000..fc77507e9afdefab7429ab0950fdb07063c291cb --- /dev/null +++ b/requirements-training.txt @@ -0,0 +1,2 @@ +-r requirements.txt +pyarrow>=20 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6990108e599b43214ac78fc79f48cab0fb9bba8f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +numpy>=2.0 +scipy>=1.13 +soundfile>=0.13 +torch>=2.6 +transformers>=5.14,<6 +safetensors>=0.5 +huggingface_hub>=0.25 diff --git a/small/checkpoint.pt b/small/checkpoint.pt new file mode 100644 index 0000000000000000000000000000000000000000..665f4a2cd86985869be33db89f3f448019802a6b --- /dev/null +++ b/small/checkpoint.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63f2dd8314cde82e998fa7ef802020ff984948d2cf4b7da32711e89b2bc637e6 +size 1165746759 diff --git a/small/config.json b/small/config.json new file mode 100644 index 0000000000000000000000000000000000000000..afe2c04e1a3281af11013fec42925b78c4439f9e --- /dev/null +++ b/small/config.json @@ -0,0 +1,142 @@ +{ + "_name_or_path": "laion/whisper-base-small-gemini-audio-scores/small", + "activation_dropout": 0.0, + "activation_function": "gelu", + "architectures": [ + "LayeredMultiTaskWhisper" + ], + "attention_dropout": 0.0, + "begin_suppress_tokens": [ + 220, + 50257 + ], + "bos_token_id": 50257, + "d_model": 768, + "decoder_attention_heads": 12, + "decoder_ffn_dim": 3072, + "decoder_layerdrop": 0.0, + "decoder_layers": 12, + "decoder_start_token_id": 50258, + "dropout": 0.0, + "encoder_attention_heads": 12, + "encoder_ffn_dim": 3072, + "encoder_layerdrop": 0.0, + "encoder_layers": 12, + "eos_token_id": 50257, + "forced_decoder_ids": [ + [ + 1, + 50259 + ], + [ + 2, + 50359 + ], + [ + 3, + 50363 + ] + ], + "init_std": 0.02, + "is_encoder_decoder": false, + "max_length": 448, + "max_source_positions": 1500, + "max_target_positions": 448, + "model_type": 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"return_attention_mask": false, + "sampling_rate": 16000 +} diff --git a/training/configs/base_recorded_run.json b/training/configs/base_recorded_run.json new file mode 100644 index 0000000000000000000000000000000000000000..4bba25e4faacfaa863ef9848537a29320f8c9c63 --- /dev/null +++ b/training/configs/base_recorded_run.json @@ -0,0 +1,99 @@ +{ + "model": "base", + "stage": "S4", + "selection": { + "model": "base", + "stage": "S4", + "validation_rank": 1, + "update": 266238, + "selection_loss": 0.8878263272196334, + "p3_validation_loss": 0.7239464588798667, + "ladder_validation_loss": 0.16387986833976667, + "evaluated_on_independent_test": true, + "safetensors_bytes": 105795724, + "safetensors_sha256": "62284fc7543c398f801fdff0c69a8175b7fa5ec6703da92f6527b31c82c7ec41", + "checkpoint_bytes": 311605464, + "checkpoint_sha256": "2c9b077f79c0e184a9bc626eca8348c95ad0535036a24a43ae3ffb8c2d8c3568" + }, + "checkpoint": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model/base/S4/checkpoint.pt", + "checkpoint_sha256": "2c9b077f79c0e184a9bc626eca8348c95ad0535036a24a43ae3ffb8c2d8c3568", + "model_config": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model/base", + "data_root": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/prepared", + "output": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/training/whisper_base", + "epochs": 2, + "encoder_lr": 1e-05, + "head_lr": 0.0001, + "minimum_lr_ratio": 0.1, + "warmup_ratio": 0.05, + "batch_per_gpu": 32, + "accumulation": 1, + "workers": 4, + "weight_decay": 0.01, + "seed": 20261005, + "normalization": "checkpoint_normalization.json", + "optimizer_policy": "fresh fine-tune AdamW/cosine; best curriculum weights restored strictly", + "loss_policy": "existing masked Huber192+CPS, Orange cosine+Huber, frames BCE+Dice, onset BCE, duration Huber, event CE", + "save_policy": "best and latest epoch checkpoint, optimizer/scheduler and per-rank RNG; epoch-boundary resume", + "world": 4, + "updates": 914, + "train_clips": 58380, + "validation_clips": 3239, + "event_class_counts": [ + 4711.0, + 531.0, + 57.0, + 9463.0, + 2215.0, + 534.0, + 157.0, + 8715.0, + 3381.0, + 202.0, + 0.0, + 685.0, + 1221.0, + 11244.0, + 2765.0, + 740.0, + 2159.0, + 403.0, + 665.0, + 1.0, + 109.0, + 3333.0, + 5.0, + 0.0, + 289.0, + 878.0, + 28.0, + 282.0, + 1.0, + 1.0, + 919.0, + 2665.0, + 937.0, + 16118.0, + 60.0, + 213.0, + 0.0, + 0.0, + 770.0, + 71.0, + 595.0, + 27.0, + 11.0, + 3.0, + 487.0, + 0.0, + 439.0, + 28.0, + 1245.0, + 49.0, + 23.0, + 1095.0, + 243.0 + ], + "fine_tune_mode": "Full encoder and every multitask head; no LoRA or frozen encoder", + "trainable_encoder_parameters": 20590592, + "trainable_total_parameters": 26441735 +} diff --git a/training/configs/small_recorded_run.json b/training/configs/small_recorded_run.json new file mode 100644 index 0000000000000000000000000000000000000000..03da4ad272caf6ad585f57ab9df8f31338cad9d8 --- /dev/null +++ b/training/configs/small_recorded_run.json @@ -0,0 +1,99 @@ +{ + "model": "small", + "stage": "S3", + "selection": { + "model": "small", + "stage": "S3", + "validation_rank": 1, + "update": 237434, + "selection_loss": 0.795881355066459, + "p3_validation_loss": 0.6556822225672116, + "ladder_validation_loss": 0.14019913249924737, + "evaluated_on_independent_test": true, + "safetensors_bytes": 388446964, + "safetensors_sha256": "cfa8973b9789fc09685d3975eb6270ac069dc4385a95ab92ddd3131f5589227b", + "checkpoint_bytes": 1156559840, + "checkpoint_sha256": "60221b4df841562434292f9c034fe9464da0799f1899d66a2f2059e26ea98881" + }, + "checkpoint": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model/small/S3/checkpoint.pt", + "checkpoint_sha256": "60221b4df841562434292f9c034fe9464da0799f1899d66a2f2059e26ea98881", + "model_config": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model/small", + "data_root": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/prepared", + "output": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/training/whisper_small", + "epochs": 2, + "encoder_lr": 5e-06, + "head_lr": 0.0001, + "minimum_lr_ratio": 0.1, + "warmup_ratio": 0.05, + "batch_per_gpu": 16, + "accumulation": 2, + "workers": 4, + "weight_decay": 0.01, + "seed": 20261005, + "normalization": "checkpoint_normalization.json", + "optimizer_policy": "fresh fine-tune AdamW/cosine; best curriculum weights restored strictly", + "loss_policy": "existing masked Huber192+CPS, Orange cosine+Huber, frames BCE+Dice, onset BCE, duration Huber, event CE", + "save_policy": "best and latest epoch checkpoint, optimizer/scheduler and per-rank RNG; epoch-boundary resume", + "world": 4, + "updates": 914, + "train_clips": 58380, + "validation_clips": 3239, + "event_class_counts": [ + 4711.0, + 531.0, + 57.0, + 9463.0, + 2215.0, + 534.0, + 157.0, + 8715.0, + 3381.0, + 202.0, + 0.0, + 685.0, + 1221.0, + 11244.0, + 2765.0, + 740.0, + 2159.0, + 403.0, + 665.0, + 1.0, + 109.0, + 3333.0, + 5.0, + 0.0, + 289.0, + 878.0, + 28.0, + 282.0, + 1.0, + 1.0, + 919.0, + 2665.0, + 937.0, + 16118.0, + 60.0, + 213.0, + 0.0, + 0.0, + 770.0, + 71.0, + 595.0, + 27.0, + 11.0, + 3.0, + 487.0, + 0.0, + 439.0, + 28.0, + 1245.0, + 49.0, + 23.0, + 1095.0, + 243.0 + ], + "fine_tune_mode": "Full encoder and every multitask head; no LoRA or frozen encoder", + "trainable_encoder_parameters": 88154112, + "trainable_total_parameters": 97102031 +} diff --git a/training/embedding_probe_study/backbones.py b/training/embedding_probe_study/backbones.py new file mode 100644 index 0000000000000000000000000000000000000000..213e947222cdf30b6216a205c7c6414c4da35506 --- /dev/null +++ b/training/embedding_probe_study/backbones.py @@ -0,0 +1,163 @@ +"""Frozen native audio encoders, pooled embeddings and aligned temporal features.""" +from __future__ import annotations + +import sys +from pathlib import Path +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F +from study_paths import ROOT, CLAP_CODE, SEED + + +class FrozenEncoder: + def __init__(self, spec, device): + self.spec, self.device = spec, device + self.kind = spec['backend'] + self.frame_projection = None + local = Path(spec.get('local_path') or '') + if self.kind == 'clap': + sys.path.insert(0, str(CLAP_CODE / 'src')) + import open_clip + from open_clip.audio.naflex_audio import AudioNaFlexCfg, AudioNaFlexPatchify + self.model = open_clip.create_model(spec['model_name'], output_dict=True) + ck = torch.load(spec['checkpoint'], map_location='cpu', weights_only=False) + state = {k.removeprefix('module.'): v for k, v in ck.get('state_dict', ck).items()} + self.model.load_state_dict(state, strict=True) + del ck, state + self.audio_cfg = AudioNaFlexCfg.from_clip_audio_cfg(self.model.audio.cfg) + self.patchify = AudioNaFlexPatchify(self.audio_cfg, random_crop=False) + self.tokens = None + self.model.audio.encoder.vit.norm.register_forward_hook(self._capture) + elif self.kind == 'commercial': + from transformers import AutoModel + self.model = AutoModel.from_pretrained(local, local_files_only=True, trust_remote_code=True) + self.tokens = None + self.model.audio_encoder.register_forward_hook(self._capture) + elif self.kind == 'gemma': + from transformers import AutoModel, AutoProcessor + self.processor = AutoProcessor.from_pretrained(local, local_files_only=True) + self.model = AutoModel.from_pretrained(local, local_files_only=True, torch_dtype=torch.bfloat16, + vision_config=None, attn_implementation='sdpa') + elif self.kind == 'omni': + from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerConfig, Qwen2_5OmniThinkerForConditionalGeneration + import json + local = local / spec.get('subfolder', '') + config = json.loads((local / 'config.json').read_text()) + thinker = config.get('thinker_config', config) + self.processor = Qwen2_5OmniProcessor.from_pretrained(local, local_files_only=True) + self.model, loading = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained( + local, config=Qwen2_5OmniThinkerConfig(**thinker), local_files_only=True, + torch_dtype=torch.bfloat16, attn_implementation='sdpa', output_loading_info=True) + missing = [k for k in loading.get('missing_keys', []) if not k.startswith(('visual.', 'lm_head.'))] + if missing: + raise RuntimeError('Missing trained audio/Thinker weights: ' + str(missing[:8])) + self.model.lm_head = nn.Identity() # No vocabulary logits or generation. + self.tokens = None + self.model.model.register_forward_hook(self._capture) + else: + raise ValueError(self.kind) + self.model.to(device).eval() + self.model.requires_grad_(False) + + def _capture(self, _module, _args, result): + self.tokens = result.last_hidden_state if hasattr(result, 'last_hidden_state') else result + if isinstance(self.tokens, tuple): + self.tokens = self.tokens[0] + + def _compress_frames(self, tensor): + width = tensor.shape[-1] + if self.frame_projection is None: + generator = torch.Generator(device='cpu').manual_seed(SEED + width) + matrix = torch.randn(width, min(64, width), generator=generator) + self.frame_projection = torch.linalg.qr(matrix, mode='reduced').Q.to(self.device) + result = tensor.float() @ self.frame_projection + return F.pad(result, (0, 64 - result.shape[-1])) + + @torch.inference_mode() + def encode(self, waves): + durations = [len(w) / 16000 for w in waves] + frames, times = [], [] + with torch.autocast('cuda', dtype=torch.bfloat16): + if self.kind == 'clap': + patches = [self.patchify((torch.from_numpy(w).float()[None], 16000)) for w in waves] + width = max(len(p['patches']) for p in patches) + inputs = {} + for name in ('patches', 'patch_coord', 'patch_valid'): + shape = (len(waves), width, *patches[0][name].shape[1:]) + inputs[name] = torch.zeros(shape, dtype=patches[0][name].dtype, device=self.device) + for i, p in enumerate(patches): + inputs[name][i, :len(p[name])] = p[name].to(self.device) + pooled = self.model.encode_audio(inputs, normalize=True) + if self.tokens is None or self.tokens.ndim != 3 or self.tokens.shape[1] < width: + raise RuntimeError('NaFlex temporal hook did not expose the patch sequence') + tokens = self.tokens[:, -width:] + dt = self.audio_cfg.patch_time * self.audio_cfg.hop_size / self.audio_cfg.sample_rate + for i, p in enumerate(patches): + coords = inputs['patch_coord'][i, :len(p['patches']), 1] + valid = inputs['patch_valid'][i, :len(p['patches'])].bool() + columns = coords[valid].unique(sorted=True) + frame = torch.stack([tokens[i, :len(coords)][valid & (coords == t)].mean(0) for t in columns]) + frames.append(self._compress_frames(frame)) + times.append((columns.float() + .5) * dt) + elif self.kind == 'commercial': + wave = torch.zeros(len(waves), 480000, device=self.device) + for i, w in enumerate(waves): + wave[i, :len(w)] = torch.from_numpy(w).to(self.device) + pooled = self.model.encode_waveform(wave) + if self.tokens is None: + raise RuntimeError('Commercial audio encoder hook failed') + for i, duration in enumerate(durations): + count = min(self.tokens.shape[1], int(np.ceil(duration * 50))) + frames.append(self._compress_frames(self.tokens[i, :count])) + times.append((torch.arange(count, device=self.device) + .5) / 50) + else: + if self.kind == 'gemma': + inputs = self.processor(audio=waves, padding=True, return_tensors='pt') + else: + conversation = [{'role': 'user', 'content': [{'type': 'audio', 'audio': 'unused'}]}] + text = self.processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=False) + inputs = self.processor(text=[text] * len(waves), audio=waves, padding=True, + sampling_rate=16000, return_tensors='pt') + inputs = {k: v.to(self.device) if torch.is_tensor(v) else v for k, v in inputs.items()} + output = self.model(**inputs, use_cache=False) if self.kind == 'omni' else self.model(**inputs) + hidden = output.last_hidden_state if self.kind == 'gemma' else self.tokens + if hidden is None: + raise RuntimeError('Native hidden state extraction failed') + mask = inputs['attention_mask'].bool() + if self.kind == 'gemma': + pooled = (hidden.float() * mask[:, :, None]).sum(1) / mask.sum(1)[:, None].clamp_min(1) + else: + last = (mask * torch.arange(mask.shape[1], device=self.device)[None]).max(1).values + pooled = hidden[torch.arange(len(waves), device=self.device), last] + config = self.model.config + token = getattr(config, 'audio_token_id', None) + if token is None: + token = getattr(config, 'audio_token_index', None) + if token is None: + raise RuntimeError('Native audio token ID is unavailable') + for i, duration in enumerate(durations): + audio_mask = (inputs['input_ids'][i] == token) & mask[i] + frame = hidden[i, audio_mask] + if not len(frame): + raise RuntimeError('No aligned audio-token features') + frames.append(self._compress_frames(frame)) + # One contiguous complete audio input. Native token count + # defines the interval grid; no invented frame resolution. + times.append((torch.arange(len(frame), device=self.device) + .5) * duration / len(frame)) + pooled = F.normalize(pooled.float(), dim=-1) + if pooled.shape != (len(waves), self.spec['native_dim']): + raise RuntimeError('Native pooled embedding dimension mismatch') + outputs = [] + for i, duration in enumerate(durations): + embedding = pooled[i].float().cpu().numpy() + frame = frames[i].float().cpu().numpy() + time = times[i].float().cpu().numpy() + keep = time < duration + frame, time = frame[keep], time[keep] + if not len(frame) or not np.isfinite(embedding).all() or not np.isfinite(frame).all(): + raise RuntimeError('Empty or non-finite frozen features') + outputs.append({'embedding': embedding.astype(np.float16), 'frame_features': frame.astype(np.float32), + 'frame_times_s': time.astype(np.float32), 'duration_s': np.float32(duration)}) + self.tokens = None + return outputs diff --git a/training/embedding_probe_study/benchmark_supplement.py b/training/embedding_probe_study/benchmark_supplement.py new file mode 100644 index 0000000000000000000000000000000000000000..e50301dc9d52df3a2db7920f4dad596580053276 --- /dev/null +++ b/training/embedding_probe_study/benchmark_supplement.py @@ -0,0 +1,537 @@ +"""Consolidate completed probe and Whisper measurements into the main report. + +Only saved measurements enter these tables. This module uses the standard +library and publishes no audio, model weights, predictions or credentials. +""" +from __future__ import annotations + +import gzip +import html +import json +from datetime import datetime, timezone +from pathlib import Path + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/embedding_probe_study_20261006') +GEMINI = ROOT.parent / 'gemini_finetune_preparation_20261005' +BENCH = ROOT.parent / 'bench_eval_20261004' +RELEASE = ROOT.parent / 'hf_layered_release/model' +HOME = Path('/e/home/jusers/schuhmann1/jupiter') +BUNDLE = HOME / 'WHISPER_ALL_BENCHMARK_RESULTS_2026-10-07.json.gz' +MANIFEST = ROOT / 'consolidated_benchmark/result_manifest.json' +PUBLIC_RESULTS = 'embedding-probe-results/' +LABELS = {'embeddinggemma2': 'EmbeddingGemma 2', 'ovis3b': 'Ovis Omni 3B', + 'voiceclap_large_v2': 'VoiceCLAP Large v2', 'voiceclap_commercial': 'VoiceCLAP Commercial', + 'clapv2_xxs': 'CLAPv2 XXS', 'clapv2_xs': 'CLAPv2 XS', 'clapv2_s': 'CLAPv2 S', + 'clapv2_m': 'CLAPv2 M', 'clapv2_l': 'CLAPv2 L'} + + +def esc(value): + return html.escape(str(value), quote=True) + + +def read(path): + return json.loads(Path(path).read_text()) + + +def optional(path): + return read(path) if Path(path).exists() else {} + + +def num(value, digits=4): + return 'Not recorded / undefined' if value is None else f'{value:.{digits}f}' + + +def pct(value): + return 'Not recorded / undefined' if value is None else f'{100 * value:.2f}%' + + +def table(headers, rows): + return '
' + ''.join( + '' for h in headers) + '' + ''.join( + '' + ''.join('' for cell in row) + '' + for row in rows) + '
' + esc(h) + '
' + esc(cell) + '
' + + +def raw_link(path): + return 'Exact metric JSON' + + +def score_table(metrics): + return table(['Target (exact model key)', 'Valid clips N', 'Raw MAE ↓', 'Normalized MAE ↓', + 'Pearson r ↑', 'Spearman ρ ↑'], + [[v['score'], v['n'], num(v.get('raw_mae')), num(v.get('normalized_mae')), + num(v.get('pearson')), num(v.get('spearman'))] for v in metrics.get('per_score', [])]) + + +def internal_metrics(metrics): + cps_raw = metrics.get('cps_raw_mae') + cps_normalized = cps_raw / read(RELEASE / 'training_normalization.json')['cps_std'] if cps_raw is not None else None + rows = [['Evaluated clips', metrics.get('clips')], ['Multitask loss ↓', num(metrics.get('loss'))], + ['CPS raw MAE (characters/second) ↓', num(metrics.get('cps_raw_mae'))], + ['CPS normalized MAE, derived using frozen training SD ↓', num(cps_normalized)], + ['Frame F1 @ probability 0.5 ↑', pct(metrics.get('frame_f1'))], + ['Oracle-span class accuracy ↑', pct(metrics.get('burst_class_oracle_span_accuracy'))], + ['Oracle-span labeled events', metrics.get('burst_class_events')], + ['Detected-span class accuracy, matched at IoU ≥0.1 ↑', pct(metrics.get('burst_matched_event_class_accuracy'))], + ['Detected-span matched labeled events', metrics.get('burst_matched_event_class_n')], + ['Start-time MAE, matches at IoU ≥0.1 (seconds) ↓', num(metrics.get('burst_start_mae_s'))], + ['End-time MAE, matches at IoU ≥0.1 (seconds) ↓', num(metrics.get('burst_end_mae_s'))]] + for name, vector in metrics.get('speaker_cosine', {}).items(): + rows += [[name.title() + ' mean cosine ↑', num(vector.get('mean'))], + [name.title() + ' valid single-speaker clips', vector.get('n')]] + result = table(['Measurement', 'Recorded result'], rows) + localization = metrics.get('burst_localization', {}) + result += table(['Interval IoU required', 'Precision ↑', 'Recall ↑', 'Event F1 ↑', 'Matched events'], + [[level, pct(v.get('precision')), pct(v.get('recall')), pct(v.get('f1')), v.get('matched')] + for level, v in localization.items()]) + return result + score_table(metrics) + + +def records(cfg, old_public): + runs = [] + for phase in ('legacy', 'gemini'): + for model in cfg['models']: + for variant in ('linear', 'mlp'): + out = ROOT / 'probes' / phase / model['id'] / variant + runs.append({'id': 'probe/' + phase + '/' + model['id'] + '/' + variant, + 'label': LABELS[model['id']] + ' / ' + variant.upper(), 'phase': phase, + 'kind': 'frozen probe', 'head': variant, 'out': out, + 'public': read(out / 'public_metrics.json'), + 'internal': read(out / 'test_metrics.json'), + 'domain': 'Flash test' if phase == 'gemini' else 'Ladder/P3 test', + 'config': read(out / 'config.json'), + 'completion': read(out / 'COMPLETE.json')}) + for phase in ('legacy', 'gemini'): + for size in ('base', 'small'): + out = ROOT / 'whisper' / phase / size + public = (old_public[size] if phase == 'legacy' else read(out / 'public_metrics.json')) + if phase == 'legacy': + public = {key: public[source] for key, source in [ + ('emolia-emo', 'emolia_emo'), ('emolia-dim', 'emolia_dim'), + ('emonet', 'emonet'), ('crema', 'crema'), ('ravdess', 'ravdess')]} + internal_path = ROOT / 'whisper/gemini' / size / ( + 'legacy_on_flash_test_metrics.json' if phase == 'legacy' else 'gemini_test_metrics.json') + runs.append({'id': 'whisper/' + phase + '/' + size, 'label': 'Whisper ' + size.title(), + 'phase': phase, 'kind': 'full encoder tuning' if phase == 'gemini' else 'original encoder', + 'head': 'layer-pooled multitask heads', 'out': out, + 'public': public, 'internal': read(internal_path), 'internal_path': internal_path, + 'domain': 'Flash test', 'config': {}, 'completion': optional(out / 'COMPLETE.json')}) + for run in runs: + run['adapters'] = {kind: read(run['out'] / (kind + '_matched_adapter.json')) + for kind in ('crema', 'ravdess')} + run['validation'] = optional(run['out'] / 'validation_metrics.json') + return runs + + +def benchmark_summary(runs): + rows = [] + for run in runs: + p = run['public']; emo = p['emolia-emo']['repo_min_2_raters']; dim = p['emolia-dim']['repo_min_2_raters'] + en = p['emonet']['all_mapped_40'] + rows.append([run['label'], run['phase'], num(en.get('pearson')), num(en.get('spearman')), + num(en.get('mae')), num(emo.get('mean_prompt_spearman')), + num(emo.get('balanced_accuracy_oracle_per_prompt')), + num(emo.get('balanced_accuracy_5fold_clip_grouped')), + num(dim.get('mean_prompt_spearman')), + num(dim.get('balanced_accuracy_oracle_per_prompt')), + num(dim.get('balanced_accuracy_5fold_clip_grouped')), + pct(run['adapters']['crema']['metrics']['accuracy']), + pct(run['adapters']['ravdess']['metrics']['accuracy'])]) + return table(['Model / head', 'Training phase', 'EmoNet Pearson ↑', 'EmoNet Spearman ↑', 'EmoNet MAE ↓', + 'VoiceNet-Emo mean ρ ↑', 'Emo oracle bal@pp ↑', 'Emo held-clip threshold bal ↑', + 'VoiceNet-Ext mean ρ ↑', 'Ext oracle bal@pp ↑', 'Ext held-clip threshold bal ↑', + 'CREMA-D matched MLP accuracy ↑', 'RAVDESS matched MLP accuracy ↑'], rows) + + +def burst_summary(runs): + rows = [] + for run in runs: + x = run['internal']; loc = x['burst_localization']; speaker = x['speaker_cosine'] + rows.append([run['label'], run['phase'], run['domain'], x['clips'], pct(x.get('frame_f1')), + pct(loc['0.1']['f1']), pct(loc['0.3']['f1']), pct(loc['0.5']['f1']), + num(x.get('burst_start_mae_s')), num(x.get('burst_end_mae_s')), + pct(x.get('burst_class_oracle_span_accuracy')), pct(x.get('burst_matched_event_class_accuracy')), + num(speaker['timbre'].get('mean')), speaker['timbre']['n'], + num(speaker['identity'].get('mean')), speaker['identity']['n'], num(x.get('cps_raw_mae'))]) + return table(['Model / head', 'Phase', 'Test domain', 'Clips', 'Frame F1 ↑', + 'Event F1 @ IoU .1 ↑', 'Event F1 @ IoU .3 ↑', 'Event F1 @ IoU .5 ↑', + 'Start MAE s ↓', 'End MAE s ↓', 'Oracle-span class acc ↑', 'Detected-span class acc ↑', + 'Timbre cosine ↑', 'Timbre N', 'Identity cosine ↑', 'Identity N', 'CPS MAE ↓'], rows) + + +def score_mean(run, prefix, field): + values = [x[field] for x in run['internal']['per_score'] + if x['score'].startswith(prefix) and x.get(field) is not None] + return sum(values) / len(values) if values else None + + +def task_leaders(runs): + frozen = [r for r in runs if r['kind'] == 'frozen probe'] + flash = [r for r in frozen if r['phase'] == 'gemini'] + whisper = {r['label']: r for r in runs if r['id'].startswith('whisper/gemini/')} + public_tasks = [ + ('EmoNet emotion intensity', 'Human labels; 12,000 clips; Pearson r ↑', + lambda r: r['public']['emonet']['all_mapped_40']['pearson'], frozen, True), + ('VoiceNet-Emo / emolia-emo', 'Human labels; ≥2 raters; mean prompt ρ ↑', + lambda r: r['public']['emolia-emo']['repo_min_2_raters']['mean_prompt_spearman'], frozen, True), + ('VoiceNet-Ext / emolia-dim', 'Human labels; ≥2 raters; mean prompt ρ ↑', + lambda r: r['public']['emolia-dim']['repo_min_2_raters']['mean_prompt_spearman'], frozen, True), + ('CREMA-D', 'Supervised matched actor-CV MLP; accuracy ↑', + lambda r: r['adapters']['crema']['metrics']['accuracy'], frozen, True), + ('RAVDESS speech', 'Supervised matched actor-CV MLP; accuracy ↑', + lambda r: r['adapters']['ravdess']['metrics']['accuracy'], frozen, True), + ('Orange timbre128 prediction', 'Same Flash test; mean cosine ↑', + lambda r: r['internal']['speaker_cosine']['timbre']['mean'], flash, True), + ('Orange identity250 prediction', 'Same Flash test; mean cosine ↑', + lambda r: r['internal']['speaker_cosine']['identity']['mean'], flash, True), + ('Characters per second', 'Same Flash test; raw MAE ↓', + lambda r: r['internal']['cps_raw_mae'], flash, False), + ('Burst frame detection', 'Same Flash test; frame F1 ↑', + lambda r: r['internal']['frame_f1'], flash, True), + ('Burst interval localization', 'Same Flash test; event F1 @ IoU .5 ↑', + lambda r: r['internal']['burst_localization']['0.5']['f1'], flash, True), + ('Burst class with true intervals', 'Same Flash test; oracle-span accuracy ↑', + lambda r: r['internal']['burst_class_oracle_span_accuracy'], flash, True), + ('40 emotion-target regressions', 'Same Flash test; mean normalized MAE ↓', + lambda r: score_mean(r, 'emo_', 'normalized_mae'), flash, False), + ('57 VoiceNet-target regressions', 'Same Flash test; mean normalized MAE ↓', + lambda r: score_mean(r, 'vn_', 'normalized_mae'), flash, False), + ('Genuineness (0–6)', 'Same Flash test; normalized MAE ↓', + lambda r: score_mean(r, 'genuineness_0_6', 'normalized_mae'), flash, False), + ('Vocal-burst blend (0–10)', 'Same Flash test; valid blend labels only; normalized MAE ↓', + lambda r: score_mean(r, 'blend_0_10', 'normalized_mae'), flash, False), + ('AudioBox aesthetics, four axes', 'Same Flash test; mean normalized MAE ↓', + lambda r: score_mean(r, 'audiobox:', 'normalized_mae'), flash, False), + ('DNSMOS, seven outputs', 'Same Flash test; mean normalized MAE ↓', + lambda r: score_mean(r, 'dnsmos:', 'normalized_mae'), flash, False)] + rows = [] + leaders = [] + for label, metric, extract, candidates, higher in public_tasks: + best = (max if higher else min)(candidates, key=extract) + b = whisper['Whisper Base']; s = whisper['Whisper Small'] + win = (max if higher else min)([best, b, s], key=extract) + display = pct if 'accuracy' in metric or 'F1' in metric else num + phase = lambda r: 'Flash' if r['phase'] == 'gemini' else 'S8–S10' + rows.append([label, metric, best['label'] + ' / ' + phase(best), display(extract(best)), + display(extract(b)), display(extract(s)), win['label'] + ' / ' + phase(win)]) + leaders.append({'task': label, 'metric': metric, 'best_frozen_run': best['id'], + 'best_frozen_value': extract(best), 'whisper_base_flash': extract(b), + 'whisper_small_flash': extract(s), 'highest_measured_run': win['id']}) + result = table(['Task', 'Metric and selection', 'Best frozen embedding probe', 'Probe result', + 'Whisper Base full FT', 'Whisper Small full FT', 'Best observed result'], rows) + return result.replace('class="sortable"', 'class="sortable task-leaders"', 1), leaders + + +def quality_summary(runs): + flash = [r for r in runs if r['phase'] == 'gemini'] + rows = [] + for r in flash: + rows.append([r['label'], num(score_mean(r, 'emo_', 'normalized_mae')), + num(score_mean(r, 'vn_', 'normalized_mae')), + num(score_mean(r, 'audiobox:', 'normalized_mae')), + num(score_mean(r, 'audiobox:', 'spearman')), + num(score_mean(r, 'dnsmos:', 'normalized_mae')), + num(score_mean(r, 'dnsmos:', 'spearman')), + num(score_mean(r, 'eiv_extra:', 'normalized_mae')), + num(score_mean(r, 'voiceclap_attribute:', 'normalized_mae'))]) + return table(['Flash-tuned model / head', '40 emotion mean norm MAE ↓', '57 VoiceNet mean norm MAE ↓', + 'AudioBox mean norm MAE ↓', 'AudioBox mean ρ ↑', 'DNSMOS mean norm MAE ↓', + 'DNSMOS mean ρ ↑', 'Empathic extras mean norm MAE ↓', 'VoiceCLAP attributes mean norm MAE ↓'], rows) + + +def quality_axes(runs): + flash = [r for r in runs if r['phase'] == 'gemini'] + frozen = [r for r in flash if r['kind'] == 'frozen probe'] + whisper = {r['label']: r for r in flash if r['id'].startswith('whisper/')} + lookup = {r['id']: {s['score']: s for s in r['internal']['per_score']} for r in flash} + names = [s['score'] for s in whisper['Whisper Base']['internal']['per_score'] + if s['score'].startswith(('audiobox:', 'dnsmos:')) or s['score'] in + ('eiv_extra:score_content_enjoyment', 'eiv_extra:score_speech_quality', + 'eiv_extra:score_background_quality', 'eiv_extra:score_overall_quality', + 'eiv_extra:Background_Noise', 'eiv_extra:Recording_Quality', 'R_quality', + 'genuineness_0_6', 'blend_0_10')] + rows = [] + for name in names: + candidates = [r for r in frozen if lookup[r['id']][name].get('normalized_mae') is not None] + best = min(candidates, key=lambda r: lookup[r['id']][name]['normalized_mae']) if candidates else None + b = lookup[whisper['Whisper Base']['id']][name]; s = lookup[whisper['Whisper Small']['id']][name] + p = lookup[best['id']][name] if best else {} + rows.append([name, b['n'], s['n'], best['label'] if best else 'No valid targets', + num(p.get('normalized_mae')), num(b.get('raw_mae')), num(b.get('normalized_mae')), + num(b.get('spearman')), num(s.get('raw_mae')), num(s.get('normalized_mae')), num(s.get('spearman'))]) + return table(['Quality / enjoyment / genuineness target', 'Base valid N', 'Small valid N', + 'Best Flash frozen probe by norm MAE', 'Probe norm MAE ↓', 'Base raw MAE ↓', + 'Base norm MAE ↓', 'Base Spearman ↑', 'Small raw MAE ↓', 'Small norm MAE ↓', 'Small Spearman ↑'], rows) + + +def public_details(run): + public = run['public']; parts = [] + en = public['emonet'] + parts.append('

EmoNet intensity: all recorded cuts

' + table( + ['Cut', 'N', 'MAE ↓', 'RMSE ↓', 'Pearson ↑', 'Spearman ↑'], + [[cut, en[cut]['n'], num(en[cut].get('mae')), num(en[cut].get('rmse')), + num(en[cut].get('pearson')), num(en[cut].get('spearman'))] + for cut in ('all_mapped_40', 'strict_unanimous', 'untruncated_mapped_40')])) + parts.append('
All 40 EmoNet emotion categories' + table( + ['Emotion', 'N', 'MAE ↓', 'RMSE ↓', 'Pearson ↑', 'Spearman ↑'], + [[r['category'], r['n'], num(r.get('mae')), num(r.get('rmse')), num(r.get('pearson')), num(r.get('spearman'))] + for r in en['per_emotion']]) + '
') + for kind in ('emolia-emo', 'emolia-dim'): + x = public[kind] + parts.append('

' + esc(kind) + ': all label-selection audits

' + table( + ['Cut', 'Question pairs N', 'Prompts', 'Positive fraction', 'Mean prompt Spearman ↑', + 'Oracle bal@pp ↑', 'Held-clip threshold balanced accuracy ↑'], + [[cut, x[cut]['n'], x[cut]['prompts'], pct(x[cut].get('positive_rate')), + num(x[cut].get('mean_prompt_spearman')), num(x[cut].get('balanced_accuracy_oracle_per_prompt')), + num(x[cut].get('balanced_accuracy_5fold_clip_grouped'))] + for cut in ('all_repo_rows', 'repo_min_2_raters', 'unflagged_min_2_raters', 'untruncated_min_2_raters')])) + for cut in ('all_repo_rows', 'repo_min_2_raters', 'unflagged_min_2_raters', 'untruncated_min_2_raters'): + z = x[cut] + parts.append('
' + esc(kind + ' / ' + cut) + ': every dimension and prompt') + parts.append(table(['Emotion / dimension', 'Questions N', 'Mean prompt ρ ↑', 'Oracle balanced accuracy ↑'], + [[r['family'], r['n'], num(r.get('mean_prompt_rho')), num(r.get('balanced_accuracy_oracle'))] + for r in z.get('per_family', [])])) + parts.append('
All individual prompts in this cut' + table( + ['Prompt (dimension | ordinal level)', 'Questions N', 'Spearman ρ ↑', 'Oracle balanced accuracy ↑'], + [[r['prompt'], r['n'], num(r.get('rho')), num(r.get('balanced_accuracy'))] + for r in z.get('per_prompt', [])]) + '
') + parts.append('

CREMA-D and RAVDESS: fixed mappings versus matched supervised MLPs

' + table( + ['Corpus', 'Protocol', 'Clips N', 'Accuracy ↑', 'Balanced accuracy ↑', 'Macro F1 ↑', 'Actor-bootstrap accuracy 95% CI'], + [[kind, protocol, x['n'], pct(x.get('accuracy')), pct(x.get('balanced_accuracy')), pct(x.get('macro_f1')), + '–'.join(pct(v) for v in x.get('accuracy_actor_bootstrap_95ci', [])) or 'Not recorded'] + for kind in ('crema', 'ravdess') + for protocol, x in [('Fixed taxonomy; no corpus labels fitted', public[kind]), + ('Nested actor-disjoint matched MLP', run['adapters'][kind]['metrics'])]])) + for kind in ('crema', 'ravdess'): + adapter = run['adapters'][kind] + parts.append('
' + esc(kind) + ': every class, confusion matrix and matched-MLP fold') + for protocol, x in [('Fixed mapping', public[kind]), ('Matched MLP', adapter['metrics'])]: + parts.append('

' + esc(protocol) + '

' + table(['Class', 'Support N', 'Recall ↑', 'Precision ↑'], + [[r['class'], r['support'], pct(r.get('recall')), pct(r.get('precision'))] for r in x['per_class']])) + classes = list(x['confusion']) + parts.append(table(['True label / predicted label'] + classes, + [[c] + [x['confusion'][c][other] for other in classes] for c in classes])) + parts.append('

' + esc(adapter['protocol']) + '; ' + str(adapter['parameters']) + ' trainable parameters.

') + parts.append(table(['Outer fold', 'Train clips', 'Test clips', 'Train actors', 'Test actors', + 'Selected LR', 'Selected epochs', 'Inner accuracy', 'Outer accuracy'], + [[x['fold'], x['train_clips'], x['held_clips'], len(x['train_actors']), len(x['held_actors']), + x['selected']['lr'], x['selected']['epochs'], pct(x['selected']['inner_accuracy']), + pct(x['outer_accuracy'])] for x in adapter['folds']])) + trials = [[x['fold'], t['lr'], t['epochs'], pct(t['inner_accuracy'])] + for x in adapter['folds'] for t in x['trials']] + parts.append('
Every inner-fold hyperparameter trial' + table( + ['Outer fold', 'Candidate LR', 'Candidate epochs', 'Mean inner held-actor accuracy'], trials) + '
') + parts.append(raw_link(run['out'] / (kind + '_matched_adapter.json')) + '
') + return ''.join(parts) + + +def make_sections(): + cfg = read(ROOT / 'study.json'); workflow = read(ROOT / 'workflow.json') + old = read(BENCH / 'public_scores.json')['models']; runs = records(cfg, old) + audit = read(ROOT / 'consolidated_benchmark/job_audit.json') + latest_ids = {workflow['pilot_job']} | {a[-1]['id'] for group in ('cache_jobs', 'tasks') + for a in workflow[group].values()} + by_id = {int(x['JobIDRaw']): x for x in audit['jobs']} + if any(by_id[j]['State'] != 'COMPLETED' or by_id[j]['ExitCode'] != '0:0' for j in latest_ids): + raise RuntimeError('A final study allocation is not successfully completed') + if workflow['state'] != 'COMPLETE' or workflow.get('errors'): + raise RuntimeError('Consolidation requires a complete error-free study') + for run in runs: + if len(run['internal']['per_score']) != 192: + raise RuntimeError('Incomplete 192-target evaluation: ' + run['id']) + stamp = datetime.now(timezone.utc).isoformat() + gpu_hours = sum(int(x['ElapsedRaw']) * 4 / 3600 for x in audit['jobs'] if 'gres/gpu=4' in x['AllocTRES']) + leader_table, leaders = task_leaders(runs) + validation_job = optional(ROOT / 'consolidated_benchmark/validation_job.json') + validation_count = sum(bool(r['validation']) for r in runs) + result = ['
Completed comparison · 7 October 2026
', + '

What we trained, in plain language

', + '

We want one audio model to describe how a voice sounds: its emotions, speaking style, sound quality and non-speech vocal events such as laughter or crying. It also predicts two speaker-characteristic vectors and how many transcript characters are spoken per second. Every detected event has a start and an end time. These models use the Whisper audio encoder only; they do not transcribe speech, generate captions or identify a person by name.

', + '

We tested two approaches. First, we kept nine existing audio embedding models fixed and trained small prediction heads on their saved audio features. Second, we trained all weights of our existing Whisper Base and Small multitask models for two further epochs using the final Gemini Flash 3.8 annotations. All models predict the same target schema; frozen embeddings use equal head capacities within each linear/MLP variant.

', + '

An embedding is a list of numbers that represents an audio recording. A prediction head maps those numbers to an emotion or another target. We tested a single linear mapping and a small two-layer neural network, called an MLP. Full fine-tuning updates both the audio encoder and its output networks. An epoch is one pass through the training clips.

', + '

The frozen models are EmbeddingGemma 2, Ovis Omni 3B, VoiceCLAP Large v2, VoiceCLAP Commercial, and five CLAPv2 sizes: XXS, XS, S, M and L. We tested a linear head and a small MLP for each, on both the older S8–S10 targets and the final Flash targets. Repository links and exact versions are in the training section.

', + '

Human-rated benchmarks test emotion and voice-style agreement. Separate audio holdouts test agreement with the Flash and other teacher annotations for sound quality, speaker vectors and burst times. A model can lead on one task and lose on another, so the table below identifies the best measured result for each task.

', + '

EmoNet-Voice measures emotion intensity in synthetic speech. VoiceNet-Emo / emolia-emo measures 40 emotions in real speech. VoiceNet-Ext / emolia-dim measures 57 voice-style dimensions: EXT and DIM are two names for the same benchmark. CREMA-D and RAVDESS test six and eight basic acted-emotion classes respectively.

', + '

This is the current consolidated report. The original S1–S10 Whisper evaluation and paper context remain below as historical measurements. New results are read directly from completed experiment artifacts, including all measurements in the embedding-probe report.

', + '

Which model is best for which task?

', + '

For accuracy, F1, correlations and cosine, higher is better. For mean absolute error (MAE), lower is better. Correlation 1 means a perfect linear relationship or ordering; cosine 1 means the same vector direction; MAE 0 means no error. A normalized MAE of 0.20 means an average error of 0.20 training standard deviations. Each row uses one stated metric; a model can rank differently on another metric.

', leader_table, + '

Public-benchmark probe winners are selected from all 36 measured probe configurations; Flash-target winners are selected from the 18 Flash-tuned probes on the same 3,544 test clips. Genuineness and vocal-burst blend are compared against valid Flash annotations, not independent human ratings. A null blend on a clip without bursts is excluded rather than treated as zero; per-target sample counts, raw MAE and correlations are in the detailed model panels. Picking the largest test score is descriptive and does not demonstrate statistical superiority. Family MAEs are unweighted means over valid targets. Only matched nested actor-CV classifier results are compared in the acted-speech rows.

', + '

How our Whisper fine-tunes compare

', + '

Whisper Small is the strongest measured choice for speaker-vector regression, the AudioBox/DNSMOS target families and EmoNet intensity correlation. Base has the highest measured VoiceNet-Emo rank correlation and the lowest CPS error. Both full fine-tunes reproduce the new Flash burst membership labels more closely than the frozen probes. Their advantages on teacher-scored holdouts are not independent human quality judgments.

', + '

Frozen VoiceCLAP Large v2 probes are stronger on CREMA-D and RAVDESS after matched supervised actor adaptation. The best frozen probe also has better Flash event-interval F1 than either full Whisper fine-tune, despite lower frame F1. VoiceNet-Ext correlations remain low for every model and are close enough that a universal style winner would be misleading. The full Whisper fine-tunes also lose accuracy on the original P3 burst domain.

', + '

Study completion and data

', + table(['Component', 'Verified outcome'], [ + ['Frozen embedding backbones', '9; all feature caches complete'], + ['Probe configurations', '36 = 9 backbones × linear/MLP × legacy/Flash; all trained and evaluated'], + ['Whisper full fine-tunes', 'Base and Small; two epochs each; all encoder weights and heads trainable'], + ['Public benchmarks per configuration', 'EmoNet-Voice, VoiceNet-Emo, VoiceNet-Ext, CREMA-D, RAVDESS'], + ['Final Slurm allocations', str(len(latest_ids)) + ' including smoke pilot; every final state COMPLETED with exit 0:0'], + ['Workflow completion', workflow['updated_utc'] + ' (UTC)'], + ['Flash dataset', '65,163 valid annotations; 58,380 train / 3,239 validation / 3,544 test'], + ['Excluded annotations', '1,036 invalid or blocked; retained for review, excluded from tuning'], + ['Tracked study allocation usage', num(gpu_hours, 2) + ' GH200 GPU-hours; entire four-GPU allocations counted'], + ['Original training and benchmark jobs', 'None pending or running'], + ['Supplementary full validation evaluation', str(validation_count) + '/40 configurations evaluated; ' + + ('all metrics complete' if validation_count == 40 else 'Slurm job ' + str(validation_job.get('id', 'not submitted yet')) + ' processes the saved best checkpoints')]]), + '

Allocation usage includes the tracked encoder caches, head training, full fine-tuning, evaluation and repaired failed attempts. It excludes earlier S1–S10 pretraining, Gemini API charges, teacher backfills, model downloads and other projects. Superseded held jobs used no GPU time. Other account jobs are outside this study.

', + '

What changed, and what did not improve

', + '

Report generated ' + esc(stamp) + '. Download every exact result, configuration, fold and job receipt (compressed JSON).

'] + result += ['

Protocols and metric definitions

', + table(['Experiment', 'Audio / targets', 'Training and checkpoint selection'], [ + ['Legacy frozen probes', 'S8 → S9 → S10; 10% P3 at each stage', 'One epoch per stage; backbone frozen; validation-selected best head'], + ['Flash frozen probes', 'Same 58,380 final Flash train clips as Whisper tuning', 'Two epochs from corresponding legacy best; backbone and legacy PCA frozen'], + ['Original Whisper comparison', 'Base S4 and Small S3, selected stage checkpoints from the S1–S10 campaign', 'Original fixed encoder and layered heads; re-evaluated on identical Flash/P3 selections'], + ['Full Whisper Flash tuning', 'Same Flash split; missing values masked; Flash targets override older scores', 'Two epochs; entire encoder plus heads optimized; Flash validation selects best'], + ['Matched acted-emotion MLP', '192 raw output scores, not hidden representations', 'Same capacity for all models; five actor-disjoint outer folds; three inner folds select LR/epochs']]), + '

Legacy probe test contains 12,059 mixed Ladder/P3 clips; Flash test contains 3,544 clips. These domains have different teacher targets. Probe PCA uses only legacy training features, maps pooled vectors to 256 dimensions and scales training components; XXS retains 224 components and pads zeros. Temporal features use a fixed seeded projection to 64 dimensions. Native CLAP patches are 160 ms; interpolation to 20 ms does not add timing information. Backbones receive audio only; reference captions, transcripts and ASR outputs do not enter inference.

', + table(['Metric', 'Meaning / interpretation'], [ + ['Raw MAE ↓', 'Mean absolute error in the target’s native units. burst_count_log1p is log(1 + count), not count error.'], + ['Normalized MAE ↓', 'MAE after frozen training mean/SD normalization; benchmark/test labels never fit these statistics.'], + ['Pearson r / Spearman ρ ↑', 'Linear agreement / ordering. Undefined if the reference or predictions are constant or too few labels exist.'], + ['Emolia mean prompt ρ ↑', 'Mean per-query ranking agreement against human yes-vote share; threshold-free.'], + ['Oracle bal@pp ↑', 'Per-prompt balanced accuracy with threshold fitted on the same evaluated labels; optimistic separability statistic.'], + ['Held-clip threshold accuracy ↑', 'Five-fold audit; threshold fitted on other clips. Ext exact audio duplicates are grouped by hash.'], + ['Acted matched-MLP accuracy ↑', 'Supervised outer-fold actor-held-out classification; not zero-shot. Same MLP architecture for all compared models.'], + ['Speaker cosine ↑', 'Cosine against Orange model vectors: timbre128 / identity250. Only valid single-speaker targets are scored.'], + ['CPS MAE ↓', 'Error in literal Unicode transcript characters per second; an independent 193rd scalar output.'], + ['Frame F1 ↑', 'Binary vocal-burst membership on the 20 ms grid at fixed probability threshold 0.5.'], + ['Event F1 @ IoU ↑', 'One-to-one Hungarian assignment of predicted start/end intervals to references at the stated overlap threshold.'], + ['Start/end MAE ↓', 'Mean boundary error in seconds, conditional on a matched event at IoU ≥0.1; missed bursts are not included.'], + ['Oracle-span class accuracy ↑', '53-class event recognition given reference start/end times; only pooled frames inside the event are used.'], + ['Detected-span class accuracy ↑', 'Recognition on predicted intervals matched at IoU ≥0.1; conditional, not end-to-end class F1.']]), + '

The new study decodes onset/duration event proposals (up to 32 per clip); its event metric differs from the original report’s continuous-positive-frame regions and greedy matching. Compare old and new event scores only with this distinction in mind. Both use a fixed 0.5 operating threshold, but event decoding and matching are different. The original 9,693-clip P3 test and the new fixed 2,000-clip P3 audit also differ in coverage.

', + '

VoiceNet-Ext and emolia-dim name the same benchmark, not two separate tests. Current ≥2-rater coverage is 7,986 Emo questions and 13,917 Ext questions, with an additional unflagged audit. EmoNet evaluates 12,000 mapped clips; Arousal and Authenticity are unsupported by the 40-emotion head. Scores use the fixed 0–4 → 0–10 endpoint conversion; clips longer than 30 seconds use the first 30 seconds. Source overlap with upstream pretraining/Emolia and the ladder is not exhaustively audited. Original Whisper training budgets and head sizes differ from frozen probes. The original campaign ran through S10; evaluation restores the earlier validation-selected S4/S3 checkpoints rather than the final S10 weights.

'] + result += ['

All 40 model/phase combinations: public benchmark results

', + '

legacy = S8–S10 head training for frozen probes, or original S1–S10-trained Whisper. gemini = two further epochs on final Flash targets. All acted accuracy columns below use the same 192 → 64 GELU → class MLP; the older 80-feature logistic adapters remain separately labeled below.

', + benchmark_summary(runs), '
', + '

Speaker vectors, CPS and vocal-burst timing for every run

', + '

Compare within a test domain. All Whisper rows and all gemini probe rows use the same 3,544 Flash test clips; legacy probe rows use the mixed Ladder/P3 holdout. Valid speaker count is shown beside cosine; only 1,718 Flash test clips satisfy the valid single-speaker target mask. Boundary and detected-class metrics include matched events only.

', + burst_summary(runs), '
'] + result += ['

Emotion, voice-style, aesthetics and DNSMOS target agreement

', + '

All rows here use the same Flash test selection. A family mean averages its recorded valid targets equally, not its clips. The detailed native-unit errors and per-target counts appear in the model panels below. These are agreement scores against recorded annotations, not ratings from a fresh listening panel.

', + quality_summary(runs), + '

Every aesthetics, DNSMOS and related quality axis

', quality_axes(runs), + '

AudioBox Aesthetics supplies Content Enjoyment (CE), Content Usefulness (CU), Production Complexity (PC) and Production Quality (PQ). DNSMOS supplies speech signal (SIG), background (BAK), overall quality (OVRL), their raw variants and P808 MOS. Empathic extras include recording/background/speech quality, content enjoyment, valence/arousal and other voice attributes. MOS means mean opinion score; here its target is an automatic model prediction.

'] + retention = []; audits = [] + for size in ('base', 'small'): + out = ROOT / 'whisper/gemini' / size + for phase in ('legacy', 'gemini'): + for domain in ('p3_validation', 'p3_test'): + path = out / (phase + '_' + domain + '_metrics.json'); x = read(path) + audits.append((size, phase, domain, path, x)) + retention.append([size.title(), phase, domain, x['clips'], pct(x['frame_f1']), + pct(x['burst_localization']['0.5']['f1']), num(x['burst_start_mae_s']), num(x['burst_end_mae_s']), + num(x['speaker_cosine']['timbre']['mean']), num(x['speaker_cosine']['identity']['mean'])]) + result += ['

Full Whisper tuning: P3 retention before and after

', + table(['Encoder', 'Phase', 'Identical P3 selection', 'Clips', 'Frame F1 ↑', 'Event F1 @ IoU .5 ↑', + 'Start MAE s ↓', 'End MAE s ↓', 'Timbre cosine ↑', 'Identity cosine ↑'], retention), + '

P3 validation and test selections each contain 2,000 clips. Within each split, exactly the same audio and masks are evaluated before/after tuning. P3 has constructed burst intervals, while Flash targets are model-generated event annotations. The two domains measure different kinds of agreement. Synthetic sample partitions are disjoint; source-pool disjointness for this new cached selection is not fully audited.

'] + for size, phase, domain, path, x in audits: + result.append('
' + esc('Whisper ' + size.title() + ' / ' + phase + ' / ' + domain) + + ': all 192 scores and event metrics' + internal_metrics(x) + raw_link(path) + '
') + result.append('
') + result += ['

Complete per-run results: every score, emotion, dimension, class and fold

', + '

Each panel contains the 192 native scalar targets, all recorded benchmark cuts, 40 EmoNet emotions, every VoiceNet dimension and ordinal prompt, speaker vectors, CPS, burst boundaries/classes and acted-emotion confusions. Missing/undefined measurements are explicit. Expand a model to read its tables; headings in all tables can be clicked to sort.

'] + extras = [] + for run in runs: + result.append('
' + esc(run['label'] + ' / ' + run['phase'] + ' / ' + run['domain']) + '') + result.append('

Held-out audio targets

' + internal_metrics(run['internal'])) + path = run.get('internal_path', run['out'] / 'test_metrics.json') + result.append(raw_link(path)) + if run['validation']: + validation_note = ('The original Whisper checkpoint was selected using Ladder validation. This row re-evaluates it on the same Flash validation clips as its tuned counterpart.' + if run['kind'] == 'original encoder' else + 'This split selected the checkpoint. Its metrics are validation measurements, not an untouched test.') + result.append('
Full selection-validation metrics: every score and class objective' + + '

' + esc(validation_note) + '

' + + internal_metrics(run['validation']) + raw_link(run['out'] / 'validation_metrics.json') + '
') + previous = run['out'] / 'pre_finetune_test_metrics.json' + if previous.exists(): + pre = read(previous); extras.append((str(previous.relative_to(ROOT)), pre)) + result.append('
Same Flash test: legacy head before its two tuning epochs' + + internal_metrics(pre) + '
') + result.append(public_details(run) + '
') + result.append('
') + training = [] + validation = [] + for run in runs: + c = run['config'] + if c: + training.append([run['label'], run['phase'], ', '.join(c['stages']), c['epochs_per_stage'], + c['train_clips'], c['validation_clips'], c['test_clips'], + ', '.join(map(str, c['stage_training_clips'])), c['parameters'], c['scalar_parameters_each'], + run['completion']['updates'], num(run['completion']['best_validation_loss'])]) + for line in (run['out'] / 'metrics.jsonl').read_text().splitlines(): + row = json.loads(line) + validation.append([run['label'], run['phase'], row['stage'], row['epoch'], row['update'], + row['validation']['clips'], num(row['validation']['loss'])]) + for size in ('base', 'small'): + for line in (GEMINI / 'training' / ('whisper_' + size) / 'metrics.jsonl').read_text().splitlines(): + row = json.loads(line) + validation.append(['Whisper ' + size.title(), 'gemini full FT', 'Flash', row['epoch'], + row['update'], 3239, num(row['validation_loss'])]) + result += ['

Executed training details and reproducibility

', + table(['Model / head', 'Phase', 'Stages', 'Epochs per stage', 'Unique train pool', 'Validation clips', + 'Test clips', 'Actual stage exposure counts', 'Total head parameters', 'Parameters per scalar', + 'Optimizer updates', 'Best validation loss ↓'], training), + '

Probe heads use AdamW LR 0.001, weight decay 0.01, batch 256 per GPU, 5% warmup and continuous cosine to a 10% floor. MLP scalars use 256 → 64 GELU → 1 (16,513 parameters each); linear scalars use 256 → 1 (257 each). Other heads are capped at 50,000 parameters. In the linear control, an oversized speaker head is factorized through a 64-dimensional linear layer. The backbone has zero trainable parameters for every probe.

', + '

Full Whisper Base uses encoder LR 0.00001; Small uses 0.000005; both use head LR 0.0001, global batch 128, two epochs/914 optimizer updates and a four-GPU allocation. Every encoder parameter, including positional embeddings, and every head is trainable. Losses combine masked normalized Huber, cosine/Huber speaker targets, frame BCE/Dice, onset BCE, duration Huber and event categorical cross entropy. Checkpoints store model, optimizer, schedule and random states. Flash tuning has no P3 replay.

', + '

Every recorded validation result

', + '

The training loop recorded multitask validation loss, not a scalar-regression “accuracy”. This combined loss selected checkpoints and cannot be interpreted as a percentage; compare losses only within the same phase and target masks. The supplementary evaluation below measures actual validation class accuracy, frame/event F1, speaker cosine and CPS from the saved best checkpoint. Recomputed holdout losses use unit burst-class weights; full Whisper training selected checkpoints using its configured class-balancing weights, so those loss values differ. P3 validation is in the retention table; acted-classifier inner/outer accuracies are in each model panel.

', + table(['Model / head', 'Phase', 'Stage', 'Completed epoch', 'Optimizer updates', 'Validation clips', + 'Recorded multitask validation loss ↓'], validation), + '

Full validation quality of the selected best checkpoint

', + '

These existing validation clips participated in checkpoint selection. Accuracy is reported for the burst class head; continuous regression targets use MAE and correlation. The complete per-target validation tables are inside each model panel. Original Whisper rows here use the same Flash validation audio as tuned Whisper.

', + burst_summary([{**r, 'internal': r['validation'], 'domain': 'Flash validation' if r['domain'] == 'Flash test' else 'Ladder/P3 validation'} + for r in runs if r['validation']]), + '

The matched acted MLP uses 192 raw scores, training-fold StandardScaler, 64 GELU hidden units and class logits: 12,742 parameters for six-class CREMA-D; 12,872 for eight-class RAVDESS. Five outer actor folds and three inner folds select LR 0.001/0.003 and 20/50 epochs. Outer test actors never fit the scaler or MLP. Confidence intervals resample actors. This exploratory supervised adaptation is different from paper zero-shot comparison and from the earlier 80-score logistic adapter.

', + '
Backbones, exact revisions and native feature widths' + table( + ['Backbone', 'Source', 'Revision / original checkpoint', 'Native pooled dimensions'], + [[LABELS[m['id']], m.get('repo', m.get('model_name')), m.get('revision', m.get('checkpoint')), m['native_dim']] + for m in cfg['models']]) + '
', + '

Models, teachers and training data repositories

', + '
Canonical 53 vocal-burst class labels' + table( + ['Class index', 'Canonical label'], enumerate(read(RELEASE / 'classes.json')['names'])) + '
', + '

The supplementary validation produced all 40 metric files successfully. Its original trailing upload step failed because the compute node has no external network route. Publication was recovered on the login node; the Slurm receipt retains that post-evaluation failure for an accurate audit.

' if validation_job.get('publication_recovery') else '', + '
Every Slurm attempt: repaired failures, replacements and final receipts' + table( + ['Slurm job', 'Name', 'Final study job?', 'State', 'Exit', 'Allocated elapsed seconds', 'Resources'], + [[x['JobIDRaw'], x['JobName'], 'Yes' if int(x['JobIDRaw']) in latest_ids else + 'Supplementary validation; publication recovered on login' if int(x['JobIDRaw']) == validation_job.get('id') else 'Superseded attempt', + x['State'], x['ExitCode'], x['ElapsedRaw'], x['AllocTRES']] for x in audit['jobs']]) + '
', + '

Machine-readable result inventory and complete compressed result bundle. Source: consolidation code, probe training and scoring code, full Whisper tuning code. Original normalization: training_normalization.json.

', + '

Study code and this report: CC BY 4.0, LAION. Upstream models and source datasets retain their own licenses. Published paper tables and earlier adapter studies follow in the historical sections; their original protocols and coverage remain visible.

'] + bundle_runs = [] + for run in runs: + value = {k: v for k, v in run.items() if k not in ('out', 'internal_path', 'config')} + value['config'] = {k: v for k, v in run['config'].items() if k != 'normalization'} + bundle_runs.append(value) + payload = {'schema': 'consolidated-audio-benchmarks-v1', 'generated_utc': stamp, + 'study': cfg, 'workflow': workflow, 'job_audit': audit, + 'normalization': read(RELEASE / 'training_normalization.json'), 'runs': bundle_runs, + 'task_leaders': leaders, 'validation_history': validation, 'supplementary_validation_job': validation_job, + 'whisper_p3_audits': [{'size': s, 'phase': p, 'domain': d, 'metrics': x} + for s, p, d, _, x in audits], + 'probe_before_flash_tuning': dict(extras)} + BUNDLE.write_bytes(gzip.compress(json.dumps(payload, ensure_ascii=False, allow_nan=False).encode(), mtime=0)) + manifest = {'schema': payload['schema'], 'generated_utc': stamp, 'study_completed_utc': workflow['updated_utc'], + 'probe_runs': 36, 'whisper_runs': 4, 'all_run_ids': [x['id'] for x in runs], + 'scalar_target_count': 192, 'extra_scalar': 'CPS', 'recorded_job_attempts': len(audit['jobs']), + 'latest_successful_jobs_including_pilot': len(latest_ids), 'tracked_gh200_gpu_hours': gpu_hours, + 'result_bundle': 'benchmark-results-2026-10-07.json.gz', + 'p3_retention_evaluations': len(audits), 'probe_pre_flash_evaluations': len(extras)} + manifest['full_validation_configurations'] = validation_count + MANIFEST.parent.mkdir(exist_ok=True); MANIFEST.write_text(json.dumps(manifest, indent=2) + '\n') + return result diff --git a/training/embedding_probe_study/cache_dataset.py b/training/embedding_probe_study/cache_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..36c9e9e0731b836fd5451258011720e29ab355da --- /dev/null +++ b/training/embedding_probe_study/cache_dataset.py @@ -0,0 +1,152 @@ +"""Read cached native features and attach the selected target regime.""" +import io +import json +import math +import os +from collections import OrderedDict +from pathlib import Path +import numpy as np +import torch +from study_paths import ROOT, GEMINI, RELEASE, read + +_COMMITTED_INDEX_ROWS = {} + + +class FeatureData: + def __init__(self, model, domains=None, split=None, phase='legacy', stage=None): + self.rows = [] + self.fds = OrderedDict() + self.projection = None + self.phase = phase + self.flash = None + self.norm = read(RELEASE / 'training_normalization.json') + if phase == 'gemini': + if not (GEMINI / 'prepared/TRAINING_READY.json').exists(): + raise RuntimeError('Final Gemini training gate is not ready') + self.flash = {r['sha256_mp3']: r for r in map(json.loads, (GEMINI / 'prepared/targets.jsonl').open()) + if r['ready_for_whisper_training'] and (split is None or r['split'] == split)} + feature_root=ROOT/'features'/model + domain_key=tuple(sorted(domains)) if domains else () + cache_key=(model,domain_key) + committed=bool(domains) and all((feature_root/(d+'-rank'+str(r)+'-COMPLETE.json')).exists() + for d in domains for r in range(4)) + # The same immutable index serves train/validation/test and S8/S9/S10. + # Decode its metadata once per process; keep the original split filters. + indexed=_COMMITTED_INDEX_ROWS.get(cache_key) if committed else None + if indexed is None: + indexed=[] + for path in sorted(feature_root.glob('*.jsonl')): + if domains and not any(path.name.startswith(d+'-rank') for d in domains): + continue + with path.open() as stream: + indexed.extend(row for row in map(json.loads,stream) + if not domains or row['source'] in domains) + if committed:_COMMITTED_INDEX_ROWS[cache_key]=indexed + for row in indexed: + if phase == 'gemini': + if row['source'] != 'gemini' or row['key'] not in self.flash: + continue + elif split and row.get('split') != split: + continue + if stage and stage not in row.get('stages', []): + continue + self.rows.append(row) + + def __len__(self): + return len(self.rows) + + def payload(self, row): + path = row['feature_tar'] + if path not in self.fds: + if len(self.fds) >= 24: + _, old = self.fds.popitem(last=False) + os.close(old) + self.fds[path] = os.open(path, os.O_RDONLY) + self.fds.move_to_end(path) + raw = os.pread(self.fds[path], row['feature_size'], row['feature_offset']) + if len(raw) != row['feature_size']: + raise RuntimeError('Short feature cache read') + with np.load(io.BytesIO(raw), allow_pickle=False) as data: + return {k: data[k] for k in data.files} + + def __getstate__(self): + state = {**self.__dict__, 'fds': OrderedDict()} + state.pop('teacher_reader', None) + return state + + def __getitem__(self, index): + row = self.rows[index] + value = self.payload(row) + embedding = value['embedding'].astype(np.float32) + if self.projection: + p = self.projection + embedding = ((embedding - p['mean']) @ p['components'].T) / p['scale'] + embedding = np.pad(embedding, (0, 256 - len(embedding))).astype(np.float32) + frames = int(math.ceil(float(value['duration_s']) * 50)) + # The model sees interpolated native features on the shared 20-ms grid. + # Native timestamps/count are retained for resolution-aware reporting. + ticks = (np.arange(frames) + .5) / 50 + native = value['frame_features'].astype(np.float32) + times = value['frame_times_s'] + value['frame_features'] = np.stack([np.interp(ticks, times, native[:, i]) for i in range(64)], axis=1).astype(np.float32) + value['embedding'] = embedding + value['feature_key'] = row['feature_key'] + if self.phase == 'gemini': + # Targets are prepared without decoding the cached full audio. + r = self.flash[row['key']] + norm = self.norm + raw = np.asarray([r['raw_scores'].get(n, np.nan) for n in norm['score_names']], np.float32) + valid = np.isfinite(raw) + teachers = r.get('teacher_metadata', {}) + if not teachers.get('dnsmos', {}).get('valid_for_speech', False): + valid[123:130] = False + if not teachers.get('empathic', {}).get('speech_domain_valid', False): + valid[100:119] = False + if not teachers.get('voiceclap', {}).get('speech_domain_valid', False): + for i in range(131, 192): + if r['score_provenance'].get(norm['score_names'][i]) != 'gemini-3.8-flash': + valid[i] = False + value.update(scores=np.where(valid, (raw - norm['score_mean']) / norm['score_std'], 0).astype(np.float32), + score_mask=valid, frame=np.zeros(frames, np.float32), + frame_mask=np.full(frames, r['burst_frames_valid'], np.float32)) + events = r['vocal_bursts'] + starts = np.asarray([max(0, min(frames-1, int(e['start'] * 50))) for e in events], np.int64) + ends = np.asarray([min(frames, max(int(a)+1, math.ceil(e['end'] * 50))) for a,e in zip(starts,events)], np.int64) + for a,b in zip(starts,ends): + value['frame'][a:b] = 1 + cps = r['characters_per_second'] + value.update(event_starts=starts, event_ends=ends, + event_classes=np.asarray([e['checkpoint_class_id'] for e in events], np.int64), + cps=np.float32((cps-norm['cps_mean'])/norm['cps_std']) if cps is not None else np.float32(0), + cps_valid=np.bool_(cps is not None)) + import sys + from study_paths import CODE + sys.path.insert(0, str(CODE / 'gemini_finetune')) + from common import TarReader + if not hasattr(self, 'teacher_reader'): + self.teacher_reader = TarReader() + for name,dim in [('timbre',128),('identity',250)]: + reference = r['embeddings'].get(name) + value[name] = np.zeros(dim,np.float32) + if reference: + value[name] = np.load(io.BytesIO(self.teacher_reader.read(reference['tar'],reference['member'])),allow_pickle=False).astype(np.float32) + value[name+'_valid'] = np.bool_(reference and teachers.get('orange',{}).get('valid_single_speaker_target')) + return value + + +def collate(items): + width = max(len(x['frame_features']) for x in items) + events = max(1, max(len(x.get('event_starts', [])) for x in items)) + arrays = {} + for name in ('embedding','scores','score_mask','timbre','identity','timbre_valid','identity_valid','cps','cps_valid'): + arrays[name] = torch.from_numpy(np.stack([x[name] for x in items])) + for name in ('frame','frame_mask','frame_features'): + source = [np.pad(x[name], ((0,width-len(x[name])),(0,0)) if x[name].ndim == 2 else (0,width-len(x[name]))) for x in items] + arrays[name] = torch.from_numpy(np.stack(source)) + for name in ('event_starts','event_ends','event_classes'): + arrays[name] = torch.from_numpy(np.stack([np.pad(x[name], (0,events-len(x[name]))) for x in items])) + mask = np.stack([np.arange(events)= 0) + result = {'dataset': kind, 'parameters': parameters, 'classes': classes, 'seed': SEED, + 'protocol': '192 raw predictions -> training-fold StandardScaler -> 64 GELU -> class logits; ' + 'five actor-disjoint outer folds; three inner folds select LR/epochs; full-batch AdamW', + 'comparison': 'Supervised benchmark adaptation; distinct from paper zero-shot', + 'metrics': metrics(y, prediction, groups, classes), 'folds': folds} + with (output / (kind + '_matched_oof.jsonl')).open('w') as stream: + for i, row in enumerate(rows): + stream.write(json.dumps({'audio_name': row['audio_name'], 'actor': str(groups[i]), + 'outer_fold': int(fold_ids[i]), 'truth': classes[y[i]], + 'prediction': classes[prediction[i]]}) + '\n') + write(path, result) + return result diff --git a/training/embedding_probe_study/predict_benchmarks.py b/training/embedding_probe_study/predict_benchmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..7254630a39fdfb56dae52c47463c53756efd8e87 --- /dev/null +++ b/training/embedding_probe_study/predict_benchmarks.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +"""Apply selected probes and the established, explicitly labeled benchmark metrics.""" +import argparse +import json +import sys +from pathlib import Path +import numpy as np +import torch +from study_paths import ROOT, CODE, RELEASE, read, write +from cache_dataset import FeatureData +from probe_model import Probe + + +def score_metrics(predictions, model_id, out): + norm=read(RELEASE/'training_normalization.json') + sys.path.insert(0,str(CODE));import score_public_benchmarks as metric + metric.data=lambda kind,_model:predictions[kind] + result={kind:(metric.emonet(model_id,norm['score_names']) if kind=='emonet' else + metric.emolia(kind,model_id,norm['score_names']) if kind.startswith('emolia') else + metric.acted(kind,model_id,norm['score_names'],np.asarray(norm['score_mean']),np.asarray(norm['score_std']))) + for kind in predictions} + write(out/'public_metrics.json',result) + + +def run(model_id,variant,phase): + out=ROOT/'probes'/phase/model_id/variant + checkpoint=torch.load(out/'best.pt',map_location='cpu',weights_only=False) + classes=read(RELEASE/'classes.json')['names'];norm=read(RELEASE/'training_normalization.json') + device=torch.device('cuda',int(__import__('os').environ.get('LOCAL_RANK',0))) + core=Probe(len(classes),variant=='linear').to(device).eval();core.load_state_dict(checkpoint['model']) + predictions={} + with torch.inference_mode(): + for kind in read(ROOT/'study.json')['benchmarks']: + data=FeatureData(model_id,[kind]);data.projection=checkpoint['pca'] + scores=[];rows=[] + for index in range(0,len(data),32): + values=[data[j] for j in range(index,min(index+32,len(data)))] + # Scalar benchmark inference uses only the cached pooled feature. + x=torch.from_numpy(np.stack([v['embedding'] for v in values])).to(device) + score=core.scalars(x)[:,:192].cpu().numpy()*np.asarray(norm['score_std'])+np.asarray(norm['score_mean']) + scores.append(score);rows.extend(data.rows[index:index+len(values)]) + scores=np.concatenate(scores).astype(np.float32) + np.savez_compressed(out/(kind+'_predictions.npz'),scores=scores) + (out/(kind+'_predictions.jsonl')).write_text(''.join(json.dumps(r,ensure_ascii=False)+'\n' for r in rows)) + predictions[kind]=(scores,rows) + score_metrics(predictions,model_id,out) + print('public_metrics',model_id,variant,phase,flush=True) + + +if __name__=='__main__': + ap=argparse.ArgumentParser();ap.add_argument('--model',required=True);ap.add_argument('--variant',choices=['linear','mlp'],required=True);ap.add_argument('--phase',choices=['legacy','gemini'],required=True) + args=ap.parse_args();run(args.model,args.variant,args.phase) diff --git a/training/embedding_probe_study/probe_model.py b/training/embedding_probe_study/probe_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d41da6c72b9e7ed4bc12d358891ce16218359f1c --- /dev/null +++ b/training/embedding_probe_study/probe_model.py @@ -0,0 +1,59 @@ +"""Matched small heads on cached frozen pooled and temporal features.""" +import torch +from torch import nn +from torch.nn import functional as F + + +class ScalarHeads(nn.Module): + def __init__(self, linear=False): + super().__init__() + self.linear = linear + if linear: + self.output = nn.Linear(256, 193) + else: + self.first = nn.Parameter(torch.empty(193, 256, 64)) + self.bias = nn.Parameter(torch.zeros(193, 64)) + self.second = nn.Parameter(torch.empty(193, 64)) + self.final_bias = nn.Parameter(torch.zeros(193)) + nn.init.normal_(self.first, std=.02) + nn.init.normal_(self.second, std=.02) + self.dropout = nn.Dropout(.1) + + def forward(self, features): + if self.linear: + return self.output(features) + hidden = self.dropout(F.gelu(torch.einsum('bi,sih->bsh', features, self.first) + self.bias)) + return (hidden * self.second[None]).sum(-1) + self.final_bias + + +class Probe(nn.Module): + def __init__(self, n_classes, linear=False): + super().__init__() + self.scalars = ScalarHeads(linear) + def head(d, out): + if linear: + return nn.Linear(d, out) if (d + 1) * out <= 50000 else nn.Sequential(nn.Linear(d, 64), nn.Linear(64, out)) + return nn.Sequential(nn.Linear(d, 64), nn.GELU(), nn.Dropout(.1), nn.Linear(64, out)) + self.timbre_head, self.identity_head = head(256, 128), head(256, 250) + self.frame_head = head(64, 3) + self.event_head = head(64, n_classes) + for name, module in [('timbre', self.timbre_head), ('identity', self.identity_head), ('frame', self.frame_head), ('event', self.event_head)]: + if sum(p.numel() for p in module.parameters()) > 50000: + raise ValueError('Head parameter cap exceeded: ' + name) + + def forward(self, features, frame_features, starts, ends): + scalar = self.scalars(features) + temporal = self.frame_head(frame_features) + # Prefix sums pool only each event's frames without per-event CUDA sync. + width = frame_features.shape[1] + a = starts.clamp(0, width - 1) + b = torch.maximum(ends, a + 1).clamp(max=width) + prefix = F.pad(frame_features.cumsum(1), (0, 0, 1, 0)) + ai = a[..., None].expand(-1, -1, frame_features.shape[-1]) + bi = b[..., None].expand_as(ai) + event = (prefix.gather(1, bi) - prefix.gather(1, ai)) / (b - a)[..., None] + return {'scores': scalar[:, :192], 'cps': scalar[:, 192], + 'timbre': F.normalize(self.timbre_head(features), dim=-1), + 'identity': F.normalize(self.identity_head(features), dim=-1), + 'frame': temporal[:, :, 0], 'onset': temporal[:, :, 1], 'log_duration': temporal[:, :, 2], + 'event_class': self.event_head(event)} diff --git a/training/embedding_probe_study/publish_consolidated_benchmark.py b/training/embedding_probe_study/publish_consolidated_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..c4588712240619521dd824f8e1ad978051c38915 --- /dev/null +++ b/training/embedding_probe_study/publish_consolidated_benchmark.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 +"""Publish the requested main benchmark report and its exact result inventory.""" +import hashlib +import os +import re +from pathlib import Path + +# Compute inference stays offline; this separate publication process needs Hub +# access and inherits the compute launcher environment on the final step. +os.environ.pop('HF_HUB_OFFLINE', None) +os.environ.pop('TRANSFORMERS_OFFLINE', None) +from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download + +from benchmark_supplement import BUNDLE, HOME, MANIFEST, ROOT, read +from study_paths import HERE, write + + +def main(): + repo = 'laion/whisper-base-small-emotion-voice-burst' + report = HOME / 'WHISPER_BASE_SMALL_COMPREHENSIVE_PUBLIC_BENCHMARKS_2026-10-04.html' + manifest = read(MANIFEST) + if manifest['probe_runs'] != 36 or manifest['whisper_runs'] != 4: + raise RuntimeError('Incomplete consolidated result inventory') + api = HfApi() + info = api.repo_info(repo_id=repo, repo_type='space') + original = Path(hf_hub_download(repo, 'README.md', repo_type='space', revision=info.sha)).read_text() + begin, end = '', '' + section = begin + ''' +## Completed comprehensive evaluation — 7 October 2026 + +[Plain-language overview, task winners and every measured result](https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html). +All nine frozen audio backbones × linear/MLP heads × legacy/Flash target phases +(36 probe configurations), plus original and fully Flash-tuned Whisper Base/Small, +have completed training and evaluation. The report includes EmoNet-Voice, +VoiceNet-Emo, VoiceNet-Ext (emolia-dim), matched actor-disjoint CREMA-D/RAVDESS +MLPs, 192 scalar targets, CPS, Orange speaker cosines and timed/classified vocal bursts. +Whisper tuning improves Flash-target agreement but reduces P3 burst retention; +both domains and the limitations of comparison to paper results are explicit. +Exact aggregate metrics, configurations and fold/job receipts are downloadable +from the report. Human-label benchmarks are distinguished from teacher-target agreement. +''' + end + card = re.sub(re.escape(begin) + '.*?' + re.escape(end), lambda _: section, original, flags=re.S) if begin in original else original.rstrip() + '\n\n' + section + '\n' + folder = ROOT / 'consolidated_benchmark' + card_path = folder / 'README.md'; card_path.write_text(card) + sources = { + 'benchmark.html': report, + 'benchmark-results-2026-10-07.json.gz': BUNDLE, + 'benchmark-result-manifest.json': MANIFEST, + 'embedding-probe-code/benchmark_supplement.py': HERE / 'benchmark_supplement.py', + 'embedding-probe-code/publish_consolidated_benchmark.py': HERE / 'publish_consolidated_benchmark.py', + 'embedding-probe-code/evaluate_validation.py': HERE / 'evaluate_validation.py', + 'embedding-probe-code/validation.sbatch': HERE / 'validation.sbatch', + 'write_comprehensive_benchmark_html.py': HERE.parent / 'write_comprehensive_benchmark_html.py', + 'README.md': card_path, + } + for tree in ('probes', 'whisper'): + for path in sorted((ROOT / tree).rglob('validation_metrics.json')): + sources['embedding-probe-results/' + str(path.relative_to(ROOT))] = path + operations = [CommitOperationAdd(path_in_repo=name, path_or_fileobj=str(path)) for name, path in sources.items()] + commit = api.create_commit(repo_id=repo, repo_type='space', operations=operations, + commit_message='Consolidate all completed probe and full Whisper tuning benchmark results', + parent_commit=info.sha) + write(folder / 'publication.json', { + 'commit': commit.oid, 'commit_url': commit.commit_url, + 'report_url': 'https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/benchmark.html', + 'artifacts': {name: {'bytes': path.stat().st_size, 'sha256': hashlib.sha256(path.read_bytes()).hexdigest()} + for name, path in sources.items()}, + }) + print(commit.commit_url) + + +if __name__ == '__main__': + main() diff --git a/training/embedding_probe_study/publish_report.py b/training/embedding_probe_study/publish_report.py new file mode 100644 index 0000000000000000000000000000000000000000..17a82535fd74d1a7feb91052c51632948873d01f --- /dev/null +++ b/training/embedding_probe_study/publish_report.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +"""Update the existing authorized HF report Space, preserving unrelated files.""" +import hashlib +import re +from pathlib import Path +from huggingface_hub import HfApi, CommitOperationAdd, hf_hub_download +from study_paths import ROOT, HERE, read, write +from write_report import OUT + + +def main(): + cfg=read(ROOT/'study.json');repo=cfg['report_space'];api=HfApi() + # Read the current remote card immediately before applying a narrow update. + info=api.repo_info(repo_id=repo,repo_type='space') + original=Path(hf_hub_download(repo,'README.md',repo_type='space',revision=info.sha)).read_text() + begin,end='','' + section=begin+'\n## Frozen embedding comparison study\n\n[English methods, execution status and measured comparisons](https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/embedding-probes.html). ' + section+='Nine frozen audio backbones with matched linear/MLP heads; S8–S10 then Gemini target tuning, plus two-epoch Whisper Base/Small fine-tuning. ' + section+='CREMA-D/RAVDESS use identical actor-held-out score MLPs, reported separately from paper zero-shot results. Pending experiments are explicitly labeled.\n'+end + card=re.sub(re.escape(begin)+'.*?'+re.escape(end),lambda _:section,original,flags=re.S) if begin in original else original.rstrip()+'\n\n'+section+'\n' + local=ROOT/'publication';local.mkdir(exist_ok=True);(local/'README.md').write_text(card) + operations=[CommitOperationAdd(path_in_repo=cfg['report_path'],path_or_fileobj=str(OUT)), + CommitOperationAdd(path_in_repo='embedding-probes-study.json',path_or_fileobj=str(ROOT/'study.json')), + CommitOperationAdd(path_in_repo='README.md',path_or_fileobj=str(local/'README.md'))] + for path in sorted(HERE.iterdir()): + if path.suffix in ('.py','.sh','.sbatch','.txt'): + operations.append(CommitOperationAdd(path_in_repo='embedding-probe-code/'+path.name,path_or_fileobj=str(path))) + from study_paths import CODE + for name in ('common.py','data.py','train_whisper.py','train_whisper.sbatch'): + path=CODE/'gemini_finetune'/name + operations.append(CommitOperationAdd(path_in_repo='gemini-full-ft-code/'+name,path_or_fileobj=str(path))) + for model in ('base','small'): + path=CODE/'gemini_finetune/configs'/('whisper_'+model+'.json') + operations.append(CommitOperationAdd(path_in_repo='gemini-full-ft-code/configs/'+path.name,path_or_fileobj=str(path))) + # Only measured evaluation artifacts are published; no audio, credentials, + # unfinished checkpoints or unrelated Space content enter this commit. + for tree in ('probes','whisper'): + for path in sorted((ROOT/tree).rglob('*.json')): + if path.name.endswith(('metrics.json','matched_adapter.json')) or path.name=='COMPLETE.json': + operations.append(CommitOperationAdd(path_in_repo='embedding-probe-results/'+str(path.relative_to(ROOT)),path_or_fileobj=str(path))) + for name in ('TRAINING_PIPELINE_CONTRACT.json','WHISPER_FULL_FT_CONTRACT.json','DISTRIBUTED_PIPELINE_CONTRACT.json'): + path=ROOT/name + if path.exists():operations.append(CommitOperationAdd(path_in_repo='embedding-probe-results/software-checks/'+name,path_or_fileobj=str(path))) + result=api.create_commit(repo_id=repo,repo_type='space',operations=operations, + commit_message='Update frozen audio embedding comparison methods and available results',parent_commit=info.sha) + write(ROOT/'publication/latest.json',{'commit':result.oid,'url':result.commit_url, + 'report_url':'https://laion-whisper-base-small-emotion-voice-burst.static.hf.space/embedding-probes.html', + 'html_sha256':hashlib.sha256(OUT.read_bytes()).hexdigest()}) + print(result.commit_url) + + +if __name__=='__main__': + main() diff --git a/training/embedding_probe_study/queue_whisper.py b/training/embedding_probe_study/queue_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..11ba27c703320531f15651d6d2d0e3e9246b68a4 --- /dev/null +++ b/training/embedding_probe_study/queue_whisper.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +"""Pre-submit full FT on hold; queue four-rank evaluation after both runs.""" +import fcntl +import subprocess +from datetime import datetime, timezone +from study_paths import ROOT, HERE, CODE, read, write + + +def queue(script,name,env,extra): + r=subprocess.run(['sbatch','--parsable','--job-name='+name, + '--export=ALL,'+','.join(k+'='+str(v) for k,v in env.items()), + *extra,str(script)],capture_output=True,text=True,check=True,timeout=20) + return int(r.stdout.strip().split(';')[0]) + + +def main(): + with (ROOT/'watcher.lock').open('a') as lock: + fcntl.flock(lock,fcntl.LOCK_EX|fcntl.LOCK_NB) + w=read(ROOT/'workflow.json');ids=[] + for model in ('base','small'): + attempts=w.setdefault('tasks',{}).setdefault('whisper-gemini-'+model,[]) + if not attempts: + job=queue(CODE/'gemini_finetune/train_whisper.sbatch','flash38-fullft-'+model, + {'WHISPER_MODEL':model,'RESUME_CHECKPOINT':''},['--hold']) + attempts.append({'id':job,'state':'PENDING','held_for_targets':True, + 'submitted_utc':datetime.now(timezone.utc).isoformat(), + 'reason':'Release on final TRAINING_READY; full encoder and heads, two epochs, four GPUs'}) + write(ROOT/'workflow.json',w) + ids.append(attempts[-1]['id']) + attempts=w.setdefault('tasks',{}).setdefault('whisper-eval-all',[]) + if not attempts: + job=queue(HERE/'whisper_eval.sbatch','flash38-fullft-evaluation',{'WHISPER_MODEL':'all'}, + ['--dependency=afterok:'+':'.join(map(str,ids)),'--kill-on-invalid-dep=yes']) + attempts.append({'id':job,'state':'PENDING','wait_for_ids':ids, + 'submitted_utc':datetime.now(timezone.utc).isoformat(), + 'reason':'Fine-tuned Base/Small on Flash holdout and all benchmarks; matched original Whisper MLPs'}) + write(ROOT/'workflow.json',w) + w.update(scheduling_policy={'cache_nodes':2,'whisper_full_ft_nodes':2,'probe_or_evaluation_nodes':1, + 'whisper_release':'Independent of cache/probe capacity; release on final Flash export', + 'probe_start':'Individual model training domains complete'}, + priority_updated_utc=datetime.now(timezone.utc).isoformat()) + write(ROOT/'workflow.json',w) + print('WHISPER_FULL_FT',ids,'EVALUATION',attempts[-1]['id']) + + +if __name__=='__main__': + main() diff --git a/training/embedding_probe_study/run_evaluations.py b/training/embedding_probe_study/run_evaluations.py new file mode 100644 index 0000000000000000000000000000000000000000..f98d93499bc295289cae12a9dc3ac0803f9d588c --- /dev/null +++ b/training/embedding_probe_study/run_evaluations.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +"""Evaluate every trained probe and matched score adapters, once per artifact.""" +import argparse +import json +import os +import numpy as np +from study_paths import ROOT, BENCH, read +from predict_benchmarks import run as predict +from matched_acted import run as acted + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--phase', choices=['legacy', 'gemini'], required=True) + ap.add_argument('--models', nargs='+') + ap.add_argument('--include-whisper-baselines', action='store_true') + args = ap.parse_args() + rank, world = int(os.environ.get('LOCAL_RANK', 0)), int(os.environ.get('WORLD_SIZE', 1)) + tasks = [(m['id'], v) for m in read(ROOT / 'study.json')['models'] if not args.models or m['id'] in args.models for v in ('linear', 'mlp')] + for model, variant in tasks[rank::world]: + out = ROOT / 'probes' / args.phase / model / variant + if not (out / 'public_metrics.json').exists(): + predict(model, variant, args.phase) + for kind in ('crema', 'ravdess'): + scores = np.load(out / (kind + '_predictions.npz'))['scores'] + rows = [json.loads(line) for line in (out / (kind + '_predictions.jsonl')).open()] + acted(kind, scores, rows, out) + # Apply the same new MLPs to the original best Whisper checkpoints. + for index, model in enumerate(('base', 'small')): + if not args.include_whisper_baselines or index % world != rank: + continue + stage = 'S4' if model == 'base' else 'S3' + out = ROOT / 'whisper' / 'legacy' / model + out.mkdir(parents=True, exist_ok=True) + for kind in ('crema', 'ravdess'): + stem = BENCH / 'predictions' / (kind + '_' + model + '_' + stage) + acted(kind, np.load(stem.with_suffix('.npz'))['scores'], + [json.loads(line) for line in stem.with_suffix('.jsonl').open()], out) + + +if __name__ == '__main__': + main() diff --git a/training/embedding_probe_study/stage_models.py b/training/embedding_probe_study/stage_models.py new file mode 100644 index 0000000000000000000000000000000000000000..edb8390104bcc5c7a85adf030fc692e588f4ba4c --- /dev/null +++ b/training/embedding_probe_study/stage_models.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +"""Stage pinned public weights with two resumable streams before offline jobs.""" +import concurrent.futures +import os +import time +import urllib.request +from datetime import datetime, timezone +from study_paths import ROOT, read, write + + +def fetch(task): + url, path, size = task + path.parent.mkdir(parents=True, exist_ok=True) + if path.exists() and path.stat().st_size == size: + return + part = path.with_name(path.name + '.part') + for attempt in range(5): + offset = part.stat().st_size if part.exists() else 0 + if offset == size: + part.replace(path) + return + request = urllib.request.Request(url, headers={'Range': f'bytes={offset}-'} if offset else {}) + try: + with urllib.request.urlopen(request, timeout=90) as response: + if offset and response.status != 206: + offset = 0 + began = time.monotonic() + sent = 0 + with part.open('ab' if offset else 'wb') as stream: + while chunk := response.read(1024 * 1024): + stream.write(chunk) + sent += len(chunk) + delay = sent / (8 * 1024 * 1024) - (time.monotonic() - began) + if delay > 0: + time.sleep(delay) + if part.stat().st_size != size: + raise RuntimeError('Incomplete pinned file') + part.replace(path) + return + except Exception: + if attempt == 4: + raise RuntimeError('Pinned download failed: ' + path.name) from None + time.sleep(min(30, 2 ** attempt)) + + +def main(): + cfg = read(ROOT / 'study.json') + tasks = [] + for model in cfg['models']: + if not model.get('repo'): + continue + repo, revision = model['repo'], model['revision'] + with urllib.request.urlopen(f'https://huggingface.co/api/models/{repo}/revision/{revision}?blobs=true', timeout=30) as response: + info = __import__('json').load(response) + if info['sha'] != revision: + raise RuntimeError('Model revision mismatch') + folder = model.get('subfolder', '') + for file in info['siblings']: + name = file['rfilename'] + if folder and not name.startswith(folder + '/'): + continue + if not name.endswith(('.json', '.safetensors', '.model', '.jinja', '.py', '.txt', '.md')): + continue + if name.startswith('adapter/') or name.endswith('zero_to_fp32.py'): + continue # VoiceCLAP root is the merged released backbone. + size = file.get('size') or file.get('lfs', {}).get('size') + if size is None: + raise RuntimeError('Missing pinned size metadata') + local = ROOT / 'models' / model['id'] / name + tasks.append((f'https://huggingface.co/{repo}/resolve/{revision}/{name}', local, size)) + write(ROOT / 'models' / model['id'] / 'MODEL_SOURCE.json', {'repo': repo, 'revision': revision, 'subfolder': folder}) + write(ROOT / 'staging_status.json', {'state': 'DOWNLOADING', 'pid': os.getpid(), 'files': len(tasks), + 'bytes': sum(t[2] for t in tasks), 'streams': 2, + 'started_utc': datetime.now(timezone.utc).isoformat()}) + with concurrent.futures.ThreadPoolExecutor(2) as pool: + for _ in pool.map(fetch, tasks): + pass + write(ROOT / 'MODELS_READY.json', {'files': len(tasks), 'bytes': sum(t[2] for t in tasks), + 'finished_utc': datetime.now(timezone.utc).isoformat()}) + write(ROOT / 'staging_status.json', {'state': 'COMPLETE', 'files': len(tasks), + 'bytes': sum(t[2] for t in tasks), 'updated_utc': datetime.now(timezone.utc).isoformat()}) + + +if __name__ == '__main__': + main() diff --git a/training/embedding_probe_study/study_data.py b/training/embedding_probe_study/study_data.py new file mode 100644 index 0000000000000000000000000000000000000000..2615d27ab7ee9d3fa6d01950c2d0ede9df2e73f7 --- /dev/null +++ b/training/embedding_probe_study/study_data.py @@ -0,0 +1,88 @@ +"""Frozen selections and targets from existing Ladder/P3/Gemini readers.""" +import hashlib +import math +import sys +import numpy as np +from study_paths import CODE, ROOT, GEMINI, RELEASE, SEED, read + +sys.path.insert(0, str(CODE)) +from train_full_curriculum import normalization, TIMBRE_INDEX, IDENTITY_INDEX +from layered_curriculum_data import LadderAuxDataset, taxonomy +from p3_frozen_loader import P3FrozenDataset +from timbre_targets import TimbreIndex + + +class SourceData: + def __init__(self, domain): + self.domain = domain + self.mean, self.std, self.names = normalization() + self.norm = read(RELEASE / 'training_normalization.json') + self.classes = read(RELEASE / 'classes.json') + self.map = self.classes if 'ladder_raw_to_canonical' in self.classes else taxonomy() + if domain == 'ladder': + self.data = LadderAuxDataset(self.mean, self.std) + self.indices = np.asarray([i for s, shard in enumerate(self.data.shards) + if shard['level'] in ('t0', 't1', 't2') + for i in range(0 if s == 0 else int(self.data.ends[s-1]), int(self.data.ends[s]))], np.int64) + self.speakers = {'timbre': TimbreIndex(TIMBRE_INDEX, 128), 'identity': TimbreIndex(IDENTITY_INDEX, 250)} + elif domain.startswith('p3_'): + split = domain[3:] + self.data = P3FrozenDataset(split, score_mean=self.mean, score_std=self.std, + event_class_path=__import__('pathlib').Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3/frozen_1120k/event_class_ids.npy')) + # Enough training clips for all three 10% stage mixes. Validation + # and test use the same fixed 2k-clips-per-split pilot across models. + size = min(len(self.data), 22000 if split == 'train' else 2000) + self.indices = np.random.default_rng(SEED).choice(len(self.data), size, replace=False) + elif domain == 'gemini': + sys.path.insert(0, str(CODE / 'gemini_finetune')) + from common import TarReader + self.reader = TarReader() + selection = GEMINI.parent / 'gemini_multisource_100h_20261004/combined_unique_manifest.jsonl' + self.data = [__import__('json').loads(line) for line in selection.open()] + self.indices = np.arange(len(self.data)) + else: + from evaluate_public_benchmarks import parquet_samples, emolia_samples + self.data = parquet_samples(domain) if domain in ('emonet', 'crema', 'ravdess') else emolia_samples(domain) + self.indices = np.arange(len(self.data)) + + def __len__(self): + return len(self.indices) + + def __getitem__(self, position): + index = int(self.indices[position]) + if self.domain == 'gemini': + row = self.data[index] + return row['sha256_mp3'], self.reader.audio(row, verify=True), {'source': 'gemini', 'key': row['sha256_mp3']}, {} + if self.domain not in ('ladder', 'p3_train', 'p3_validation', 'p3_test'): + from evaluate_public_benchmarks import decode + sample = self.data[index] + wave, duration, truncated = decode(sample) + return sample.key, wave, {**sample.metadata, 'source': self.domain, 'key': sample.key, + 'duration_s': duration, 'truncated_30s': truncated}, {} + item = self.data[index] + if self.domain == 'ladder': + shard = int(np.searchsorted(self.data.ends, index, side='right')) + level = self.data.shards[shard]['level'] + stages = ['S8'] + (['S9'] if level in ('t0', 't1') else []) + (['S10'] if level == 't0' else []) + value = int(hashlib.sha256(item['uid'].encode()).hexdigest()[:8], 16) % 100 + split = 'train' if value < 90 else 'validation' if value < 95 else 'test' + lookup = self.map['ladder_raw_to_canonical'] + spans = item['events_frames'] + for name, speaker in self.speakers.items(): + vector, valid = speaker.lookup([item['uid']]) + item[name], item[name + '_valid'] = vector[0].astype(np.float32), bool(valid[0]) + else: + stages = ['S8', 'S9', 'S10'] + split = self.domain[3:] + lookup = self.map['p3_raw_to_canonical'] + spans = [(start // 320, math.ceil(end / 320)) for start, end in item['events_samples']] + for name in ('timbre', 'identity'): + item[name + '_valid'] = item['speaker_valid'] + classes = [lookup[int(c)] for c in item['event_raw_class_ids'][:len(spans)]] + target = {k: np.asarray(item[k]) for k in ('scores', 'score_mask', 'frame', 'frame_mask', 'timbre', 'identity', 'timbre_valid', 'identity_valid')} + cps = item.get('cps_raw') + cps_valid = cps is not None and np.isfinite(cps) + target.update(cps=np.float32((cps - self.norm['cps_mean']) / self.norm['cps_std']) if cps_valid else np.float32(0), + cps_valid=np.bool_(cps_valid), event_starts=np.asarray([x[0] for x in spans], np.int64), + event_ends=np.asarray([x[1] for x in spans], np.int64), event_classes=np.asarray(classes, np.int64)) + return self.domain + ':' + item['key'], item['audio'], {'source': self.domain, 'key': item['key'], 'split': split, 'stages': stages}, target diff --git a/training/embedding_probe_study/study_paths.py b/training/embedding_probe_study/study_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a5caa6261c99887315b54e9dca2ed5c0faae48 --- /dev/null +++ b/training/embedding_probe_study/study_paths.py @@ -0,0 +1,44 @@ +"""Pinned experiment paths and atomic, credential-free metadata.""" +import hashlib +import json +import os +from pathlib import Path + +HERE = Path(__file__).resolve().parent +CODE = HERE.parent +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/embedding_probe_study_20261006') +BENCH = ROOT.parent / 'bench_eval_20261004' +GEMINI = ROOT.parent / 'gemini_finetune_preparation_20261005' +RELEASE = ROOT.parent / 'hf_layered_release/model' +CLAP_CODE = Path('/e/project1/laionize/foerster5_jupiter/clap_v2_refactor/open_clip') +CLAP_BENCH = CLAP_CODE.parent / 'clap_benchmark' +TRANSFORMERS_REVISION = '14e738b5d0cc69aa27a95dde272aea41fde44f2f' +SEED = 20261006 + + +def read(path): + return json.loads(Path(path).read_text()) + + +def json_number(value): + # Keep the orchestration process stdlib-only while accepting NumPy metric + # scalars/arrays produced by the compute jobs. Nonfinite values still fail. + if type(value).__module__.split('.')[0] == 'numpy' and hasattr(value, 'tolist'): + return value.tolist() + raise TypeError('Unsupported metadata value: ' + type(value).__name__) + + +def write(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(path.name + '.tmp-' + str(os.getpid())) + temporary.write_text(json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False, default=json_number) + '\n') + os.replace(temporary, path) + + +def digest(path): + h = hashlib.sha256() + with Path(path).open('rb') as stream: + while chunk := stream.read(8 * 1024 * 1024): + h.update(chunk) + return h.hexdigest() diff --git a/training/embedding_probe_study/train_probes.py b/training/embedding_probe_study/train_probes.py new file mode 100644 index 0000000000000000000000000000000000000000..8393add5353a1308fcf9ccda64c5e9349f471b2d --- /dev/null +++ b/training/embedding_probe_study/train_probes.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +"""Train matched linear/MLP probes and report independent cached holdout scores.""" +import argparse +import json +import math +import os +import sys +import time +from pathlib import Path +import numpy as np +import torch +from torch.utils.data import DataLoader +from scipy.stats import pearsonr, spearmanr +from scipy.optimize import linear_sum_assignment +from sklearn.decomposition import PCA +from study_paths import ROOT, CODE, RELEASE, SEED, read, write +from cache_dataset import FeatureData, collate +from probe_model import Probe + +sys.path.insert(0, str(CODE)) +from train_layered_curriculum import loss_terms + + +def projection(data, output): + path = output / 'pca.npz' + if path.exists(): + with np.load(path) as value: + return {k: value[k] for k in value.files} + indices = np.random.default_rng(SEED).choice(len(data), min(50000,len(data)), replace=False) + x = np.stack([data.payload(data.rows[i])['embedding'].astype(np.float32) for i in indices]) + pca = PCA(n_components=min(256, x.shape[1], len(x)-1), svd_solver='randomized', random_state=SEED) + z = pca.fit_transform(x) + result = {'mean': pca.mean_.astype(np.float32), 'components': pca.components_.astype(np.float32), + 'scale': np.maximum(z.std(0),1e-5).astype(np.float32)} + np.savez(path, **result) + write(output / 'pca_fit.json', {'training_clips':len(data), 'fit_clips':len(indices), + 'training_key_sha256':__import__('hashlib').sha256('\n'.join(data.rows[i]['feature_key'] for i in indices).encode()).hexdigest(), + 'native_dim':x.shape[1], 'retained_dim':len(result['scale']), 'output_dim':256, + 'scope':'Training split only; validation and benchmark features never used'}) + return result + + +def loader(data, shuffle=False): + return DataLoader(data,batch_size=256,shuffle=shuffle,num_workers=4, + multiprocessing_context='spawn',persistent_workers=True,collate_fn=collate) + + +def gpu(item,device): + return {k:v.to(device) if torch.is_tensor(v) else v for k,v in item.items()} + + +def predict(model,batch): + return model(batch['embedding'],batch['frame_features'],batch['event_starts'],batch['event_ends']) + + +@torch.no_grad() +def evaluate(model,data,device,classes,full=False,make_loader=None,predictor=None): + model.eval() + total=count=0 + predictions=[]; targets=[]; masks=[] + speaker={'timbre':[],'identity':[]}; cps=[]; y_cps=[] + burst_tp=burst_fp=burst_fn=0 + event_correct=event_count=0 + span_counts={threshold:[0,0,0] for threshold in (.1,.3,.5)} + starts_error=[];ends_error=[];matched_class_correct=matched_class_total=0 + for item in (make_loader or loader)(data): + batch=gpu(item,device) + output=(predictor or predict)(model,batch) + loss=sum(loss_terms(output,batch,classes).values()) + total+=float(loss)*len(batch['scores']);count+=len(batch['scores']) + if not full:continue + predictions.append(output['scores'].float().cpu().numpy()) + targets.append(batch['scores'].float().cpu().numpy()) + masks.append(batch['score_mask'].cpu().numpy()) + valid=batch['frame_mask'].bool();truth=batch['frame']>0;decision=output['frame']>=0 + burst_tp+=int((valid&truth&decision).sum());burst_fp+=int((valid&~truth&decision).sum());burst_fn+=int((valid&truth&~decision).sum()) + for name in speaker: + mask=batch[name+'_valid'].bool() + reference=torch.nn.functional.normalize(batch[name][mask].float(),dim=-1) + speaker[name].extend((output[name][mask].float()*reference).sum(-1).cpu().tolist()) + valid_cps=batch['cps_valid'].bool() + cps.extend(output['cps'][valid_cps].cpu().tolist());y_cps.extend(batch['cps'][valid_cps].cpu().tolist()) + event_mask=batch['event_valid'].bool() + event_correct+=int((output['event_class'].argmax(-1)[event_mask]==batch['event_classes'][event_mask]).sum());event_count+=int(event_mask.sum()) + for i in range(len(batch['scores'])): + length=int(batch['frame_mask'][i].bool().sum()) + if not length:continue + prob=output['onset'][i,:length].float().sigmoid().cpu().numpy() + peaks=[j for j in range(length) if prob[j]>=.5 and (j==0 or prob[j]>=prob[j-1]) and (j==length-1 or prob[j]>prob[j+1])] + chosen=[] + for j in sorted(peaks,key=lambda j:-prob[j]): + if all(abs(j-k)>=3 for k in chosen):chosen.append(j) + if len(chosen)==32:break + predicted=[] + for j in sorted(chosen): + duration=max(1,round(math.expm1(float(output['log_duration'][i,j].clamp(0,8))))) + predicted.append((j,min(length,j+duration))) + if 'predicted_event_valid' in output: + keep=output['predicted_event_valid'][i] + predicted=list(zip(output['predicted_event_starts'][i,keep].cpu().tolist(), + output['predicted_event_ends'][i,keep].cpu().tolist())) + selected=batch['event_valid'][i].bool() + reference=list(zip(batch['event_starts'][i,selected].cpu().tolist(),batch['event_ends'][i,selected].cpu().tolist())) + overlap=np.zeros((len(reference),len(predicted))) + for a,(s,e) in enumerate(reference): + for b,(t,u) in enumerate(predicted): + intersection=max(0,min(e,u)-max(s,t));overlap[a,b]=intersection/max(1,e-s+u-t-intersection) + matches=list(zip(*linear_sum_assignment(-overlap))) if overlap.size else [] + for threshold,counts in span_counts.items(): + tp=sum(bool(overlap[a,b]>=threshold) for a,b in matches) + counts[0]+=tp;counts[1]+=len(predicted)-tp;counts[2]+=len(reference)-tp + for a,b in matches: + if overlap[a,b]<.1:continue + s,e=reference[a];t,u=predicted[b] + starts_error.append(abs(s-t)*.02);ends_error.append(abs(e-u)*.02) + if 'predicted_event_class' in output: + guess=int(output['predicted_event_class'][i,output['predicted_event_valid'][i]][b].argmax()) + else: + local=batch['frame_features'][i,t:u].mean(0) + guess=int(model.event_head(local).argmax()) + target=int(batch['event_classes'][i,selected][a]) + matched_class_correct+=guess==target;matched_class_total+=1 + result={'loss':total/max(1,count),'clips':count} + if full: + p=np.concatenate(predictions);y=np.concatenate(targets);mask=np.concatenate(masks) + norm=read(RELEASE/'training_normalization.json');per=[] + for i,name in enumerate(norm['score_names']): + take=mask[:,i];a=p[take,i];b=y[take,i] + nonconstant=len(a)>2 and a.std()>1e-8 and b.std()>1e-8 + per.append({'score':name,'n':int(take.sum()),'raw_mae':float(np.abs(a-b).mean()*norm['score_std'][i]) if len(a) else None, + 'normalized_mae':float(np.abs(a-b).mean()) if len(a) else None, + 'pearson':float(pearsonr(a,b).statistic) if nonconstant else None, + 'spearman':float(spearmanr(a,b).statistic) if nonconstant else None}) + result.update(per_score=per,speaker_cosine={k:{'n':len(v),'mean':float(np.mean(v)) if v else None} for k,v in speaker.items()}, + frame_f1=2*burst_tp/max(1,2*burst_tp+burst_fp+burst_fn),frame_threshold=.5, + cps_raw_mae=float(np.abs(np.asarray(cps)-y_cps).mean()*norm['cps_std']) if cps else None, + burst_class_oracle_span_accuracy=event_correct/max(1,event_count),burst_class_events=event_count, + burst_localization={str(t):{'precision':tp/max(1,tp+fp),'recall':tp/max(1,tp+fn),'f1':2*tp/max(1,2*tp+fp+fn),'matched':tp} for t,(tp,fp,fn) in span_counts.items()}, + burst_start_mae_s=float(np.mean(starts_error)) if starts_error else None, + burst_end_mae_s=float(np.mean(ends_error)) if ends_error else None, + burst_matched_event_class_accuracy=matched_class_correct/max(1,matched_class_total), + burst_matched_event_class_n=matched_class_total, + limitations='Oracle-span and predicted-span classification reported separately. Boundaries use fixed 0.5 onset threshold and native-feature interpolation; IoU matching uses Hungarian assignment.') + return result + + +def run(model_id,variant,phase): + cfg=read(ROOT/'study.json');device=torch.device('cuda',int(os.environ.get('LOCAL_RANK',0))) + torch.cuda.set_device(device);torch.set_num_threads(2);torch.manual_seed(SEED) + out=ROOT/'probes'/phase/model_id/variant;out.mkdir(parents=True,exist_ok=True) + if (out/'COMPLETE.json').exists():return + domains=['gemini'] if phase=='gemini' else ['ladder','p3_train','p3_validation','p3_test'] + train=FeatureData(model_id,domains,'train',phase) + val=FeatureData(model_id,domains,'validation',phase) + test=FeatureData(model_id,domains,'test',phase) + if not train or not val or not test:raise RuntimeError('Training and two independent holdout partitions required') + previous=None + if phase=='gemini': + previous=torch.load(ROOT/'probes/legacy'/model_id/variant/'best.pt',map_location='cpu',weights_only=False) + pca=previous['pca'];np.savez(out/'pca.npz',**pca) + else:pca=projection(train,out) + for data in (train,val,test):data.projection=pca + classes=read(RELEASE/'classes.json')['names'] + model=Probe(len(classes),variant=='linear').to(device) + if previous: + model.load_state_dict(previous['model'],strict=True) + write(out/'pre_finetune_test_metrics.json',evaluate(model,test,device,torch.ones(len(classes),device=device),True)) + del previous + weights=torch.ones(len(classes),device=device) + # Stage P3 exposure is 10% of training examples at each stage. + stages=['Gemini'] if phase=='gemini' else cfg['stages'] + training=[] + for stage in stages: + if phase=='gemini':data=train + else: + data=FeatureData(model_id,domains,'train',phase,stage);data.projection=pca + ladder=[r for r in data.rows if r['source']=='ladder'];p3=[r for r in data.rows if r['source']=='p3_train'] + # Hash order avoids sample-ID blocks biasing the later small stages + # toward one synthetic subtype or the positive/negative generation order. + p3=sorted(p3,key=lambda r:__import__('hashlib').sha256((str(SEED)+r['feature_key']).encode()).digest())[:round(len(ladder)/9)] + data.rows=ladder+p3 + training.append(data) + epochs=cfg['gemini_probe_epochs'] if phase=='gemini' else cfg['probe_epochs_per_stage'] + updates=sum(math.ceil(len(data)/cfg['probe_batch'])*epochs for data in training) + warm=max(1,round(updates*cfg['warmup_ratio'])) + def scale(u): + if u=3: + w.setdefault('errors',{})[name]='Three incomplete FT allocations; inspect saved logs.' + elif a['state'] in RESUMABLE: + env={'WHISPER_MODEL':model,'RESUME_CHECKPOINT':str(out/'latest.pt') if (out/'latest.pt').exists() else ''} + job=submit(CODE/'gemini_finetune/train_whisper.sbatch',env,'flash38-fullft-'+model) + attempts.append({'id':job,'state':'PENDING','submitted_utc':stamp,'resumed_from':a['id']}) + write(ROOT/'workflow.json',w) + else:w.setdefault('errors',{})[name]='FT ended '+a['state']+'; repair the concrete error before another allocation.' + parents=[w['tasks']['whisper-gemini-'+m][-1]['id'] for m in ('base','small')] + eval_attempts=w['tasks'].setdefault('whisper-eval-all',[]) + done_whisper=all((ROOT/'whisper/gemini'/m/'COMPLETE.json').exists() for m in ('base','small')) + # Reconnect evaluation to resumed FT jobs, rather than leaving a broken + # afterok dependency attached to an obsolete wall-time-limited allocation. + if not done_whisper: + a=eval_attempts[-1] if eval_attempts else None + if a and a['state']=='PENDING' and a.get('wait_for_ids')!=parents: + subprocess.run(['scontrol','update','JobId='+str(a['id']), + 'Dependency=afterok:'+':'.join(map(str,parents))], + check=True,capture_output=True,text=True,timeout=20) + a['wait_for_ids']=parents;write(ROOT/'workflow.json',w) + elif not a or a['state'] not in ACTIVE|{'UNKNOWN'}: + if retry_allocations(eval_attempts)<3 and (not a or a['state'] in RESUMABLE|{'CANCELLED','COMPLETED'}): + job=submit(HERE/'whisper_eval.sbatch',{'WHISPER_MODEL':'all'},'flash38-fullft-evaluation', + ['--dependency=afterok:'+':'.join(map(str,parents)),'--kill-on-invalid-dep=yes']) + eval_attempts.append({'id':job,'state':'PENDING','wait_for_ids':parents,'submitted_utc':stamp}) + write(ROOT/'workflow.json',w) + else:w.setdefault('errors',{})['whisper-eval-all']='Evaluation incomplete; inspect its concrete error.' + # One probe/evaluation node runs alongside two cache nodes and two dedicated + # FT nodes. Evaluation with an unmet dependency consumes no node or slot. + busy_aux=False;active_aux=0 + for name,attempts in w['tasks'].items(): + if name.startswith('whisper-gemini-') or not attempts:continue + a=attempts[-1] + if a['state'] not in ACTIVE|{'UNKNOWN'}:continue + if a.get('wait_for_ids') and a['state']=='PENDING' and not all(known.get(j)=='COMPLETED' for j in a['wait_for_ids']):continue + busy_aux=True;active_aux+=1 + def probe_task(phase,models,evaluation): + nonlocal busy_aux,active_aux + if busy_aux or not models:return + models=models[:2];name=('eval-' if evaluation else 'train-')+phase+'-'+'-'.join(models) + attempts=w['tasks'].setdefault(name,[]) + if attempts and attempts[-1]['state'] in ACTIVE|{'UNKNOWN'}:return + if retry_allocations(attempts)>=3 or (attempts and attempts[-1]['state'] not in RESUMABLE|{'COMPLETED'}): + w.setdefault('errors',{})[name]='Incomplete allocation; repair its concrete error before retrying.';return + env={'PROBE_PHASE':phase,'PROBE_MODELS':':'.join(models),'PROBE_EVAL':0} + job=submit(HERE/('probe_eval.sbatch' if evaluation else 'train.sbatch'),env,name) + attempts.append({'id':job,'state':'PENDING','submitted_utc':stamp,'models':models,'phase':phase, + 'operation':'evaluation' if evaluation else 'training'}) + busy_aux=True;active_aux+=1;write(ROOT/'workflow.json',w) + for phase in ('gemini','legacy'): + ready=[m['id'] for m in cfg['models'] if probe_done(phase,m['id']) and not probe_done(phase,m['id'],True) + and domains_done(m['id'],cfg['benchmarks'])] + probe_task(phase,ready,True) + ready=[m['id'] for m in cfg['models'] if gate and domains_done(m['id'],['gemini']) + and probe_done('legacy',m['id']) and not probe_done('gemini',m['id'])] + probe_task('gemini',ready,False) + legacy_domains=['ladder','p3_train','p3_validation','p3_test'] + ready=[m['id'] for m in cfg['models'] if domains_done(m['id'],legacy_domains) and not probe_done('legacy',m['id'])] + probe_task('legacy',ready,False) + active_cache=sum(bool(a) and a[-1]['state'] in ACTIVE|{'UNKNOWN'} for a in w['cache_jobs'].values()) + active_ft=sum(w['tasks']['whisper-gemini-'+m][-1]['state'] in ACTIVE|{'UNKNOWN'} for m in ('base','small')) + ft_finished=all((GEMINI/'training'/('whisper_'+m)/'COMPLETE.json').exists() for m in ('base','small')) + # Keep FT capacity available until both full runs finish. Then reuse those + # slots for remaining frozen encoders without exceeding five study nodes. + cache_limit=min(4,max(0,5-active_ft-active_aux)) if ft_finished else 2 + w['cache_node_limit_now']=cache_limit + w['scheduling_policy']['cache_after_full_ft']='Up to four; total study allocations stay at most five' + pilot=state(w['pilot_job']);w['pilot_state']=pilot + pilot_ok=pilot=='COMPLETED' and all((ROOT/'smoke/clapv2_xxs'/('CONTRACT-rank'+str(r)+'.json')).exists() for r in range(4)) + if pilot_ok: + for m in cfg['models']: + mid=m['id'] + if active_cache>=cache_limit:break + if done_cache(mid,cfg):continue + if m.get('repo') and not ((ROOT/'models'/mid/'MODEL_READY.json').exists() or (ROOT/'MODELS_READY.json').exists()):continue + attempts=w['cache_jobs'].setdefault(mid,[]) + if attempts and attempts[-1]['state'] in ACTIVE|{'UNKNOWN'}:continue + if retry_allocations(attempts)>=3 or (attempts and attempts[-1]['state'] not in RESUMABLE): + w.setdefault('errors',{})[mid]='Encoder cache failed; inspect smoke/full-cache logs.';continue + job=submit(HERE/'cache.sbatch',{'PROBE_MODEL':mid,'PROBE_SMOKE':0},'probe-cache-'+mid) + attempts.append({'id':job,'state':'PENDING','submitted_utc':stamp});active_cache+=1 + write(ROOT/'workflow.json',w) + else:w.setdefault('errors',{})['pilot']='Encoder/head smoke contract has not passed.' + w['cache_states']={} + for m in cfg['models']: + attempts=w['cache_jobs'].get(m['id'],[]) + w['cache_states'][m['id']]='COMPLETE' if done_cache(m['id'],cfg) else attempts[-1]['state'] if attempts else 'NOT_SUBMITTED' + complete=done_whisper and all(probe_done(p,m['id'],True) for m in cfg['models'] for p in ('legacy','gemini')) + w['state']='COMPLETE' if complete else 'RUNNING_WITH_ERRORS' if w.get('errors') else 'RUNNING_OR_WAITING_FOR_DATA' + write(ROOT/'workflow.json',w) + return complete + + +def main(): + with (ROOT/'watcher.lock').open('a') as lock: + try:fcntl.flock(lock,fcntl.LOCK_EX|fcntl.LOCK_NB) + except BlockingIOError:return + w=read(ROOT/'workflow.json');last_report=0 + while True: + try: + complete=tick(w,read(ROOT/'study.json'));w.pop('watcher_error',None) + if complete or time.monotonic()-last_report>300: + subprocess.run(['python3',str(HERE/'write_report.py')],check=True,timeout=30) + r=subprocess.run(['bash',str(HERE/'publish.sh')],capture_output=True,text=True,timeout=120) + if r.returncode:w['publication_error']=r.stderr[-1200:] + else:w.pop('publication_error',None) + write(ROOT/'workflow.json',w);last_report=time.monotonic() + if complete:return + except Exception as error: + w['watcher_error']=str(error)[-1800:];write(ROOT/'workflow.json',w) + time.sleep(30) + + +if __name__=='__main__': + main() diff --git a/training/embedding_probe_study/write_report.py b/training/embedding_probe_study/write_report.py new file mode 100644 index 0000000000000000000000000000000000000000..7455251873a55abafadbafdf04154c9e6c786459 --- /dev/null +++ b/training/embedding_probe_study/write_report.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python3 +"""Render an honest English study log; pending experiments have no invented scores.""" +import html +import json +from datetime import datetime, timezone +from pathlib import Path +from study_paths import ROOT, CODE, BENCH, GEMINI, RELEASE, read + +OUT = CODE.parent / 'WHISPER_EMBEDDING_PROBE_COMPARISON_2026-10-06.html' + + +def esc(value): + return html.escape(str(value)) + + +def number(value): + return 'Pending / unavailable' if value is None else f'{value:.4f}' + + +def optional(path): + return read(path) if path.exists() else {} + + +def table(headers, rows): + return '
' + ''.join('' for x in headers) + '' + ''.join(''+''.join('' for x in row)+'' for row in rows)+'
'+esc(x)+'
'+esc(x)+'
' + + +def main(): + cfg = read(ROOT / 'study.json') + workflow = optional(ROOT / 'workflow.json') + selection = ROOT.parent / 'gemini_multisource_100h_20261004/annotation_batch_20261005' + flash = optional(selection / 'status.json') + teachers = optional(GEMINI / 'bulk_annotation_workflow.json') + parts = ['

Frozen audio embedding probes versus Whisper Base and Small

', + '

A controlled downstream-head study of emotion, voice style, quality, speaker embeddings and vocal-burst localization. This page records actual execution state and fills result tables as completed artifacts become available.

', + '

Updated '+esc(datetime.now(timezone.utc).isoformat())+'. Existing public benchmark report · Earlier supervised score adapters · Released Whisper weights.

', + '

Execution status

', + table(['Component','Observed state'], [ + ['Gemini selected audio', str(flash.get('selected_available_unique_clips',66199))+' unique clips; 197.36 hours'], + ['Valid Flash targets',str(flash.get('valid_clips','Unknown'))+'; provider status timestamp '+str(flash.get('updated_utc','Unknown'))], + ['Other teacher annotations',teachers.get('state','Unknown')], + ['Study workflow',workflow.get('state','Prepared; execution gates pending')], + ['Final Flash training gate',str((GEMINI/'prepared/TRAINING_READY.json').exists())], + ['Public comparison results','Available only when individual evaluation artifacts below exist']])] + checks=[] + for filename,label in [('TRAINING_PIPELINE_CONTRACT.json','Real cached-feature probe training and held-out metric pipeline'), + ('WHISPER_FULL_FT_CONTRACT.json','Full Base/Small encoder and head optimizer updates'), + ('DISTRIBUTED_PIPELINE_CONTRACT.json','Four-GPU distributed updates and real P3 evaluation loaders')]: + evidence=optional(ROOT/filename) + checks.append([label,'Passed' if evidence.get('passed') else 'Not yet verified', + evidence.get('scope','No completed check artifact')]) + parts += ['

Execution checks

',table(['Check','Observed result','Scope'],checks), + '

These bounded checks verify software compatibility and finite training updates. They are not scientific evaluation results. A stale console launcher used the system Python instead of the active production interpreter; affected queued scripts were replaced with python -m torch.distributed.run. The live job IDs below are the replacements. Final Flash targets remain a required gate; unresolved or invalid annotations are retained for review and excluded from fine-tuning.

'] + queued=[] + for label,attempts in workflow.get('tasks',{}).items(): + if not attempts:continue + job=attempts[-1] + state='Held until final Flash training targets are ready; no GPU reservation' if job.get('held_for_targets') else job.get('state','Unknown') + queued.append([label,job['id'],state,job.get('reason',job.get('operation','')),', '.join(map(str,job.get('wait_for_ids',[]))) or 'Data / per-model readiness gate']) + if queued: + parts += ['

Submitted training and evaluation jobs

',table(['Job','Slurm ID','Current state','Purpose','Dependencies'],queued), + '

Base and Small each have a dedicated four-GPU full fine-tuning allocation for two epochs. Every encoder weight, including the initially fixed positional embedding table, and every multitask head is trainable. The jobs are pre-submitted on hold and released immediately when the final Flash-priority dataset export passes its training gate. They do not wait for the embedding caches to finish. The pre-submitted evaluation allocation depends on successful completion of both full fine-tunes and evaluates identical Flash test clips before/after tuning, fixed 2,000-clip P3 validation and test selections before/after tuning, the five public benchmarks, and matched actor-disjoint classification adapters.

', + '

Embedding probes start independently as soon as their own Ladder/P3 training features are complete. Their evaluations wait for that model’s complete benchmark features. Gemini probe tuning starts when its legacy head and final Flash features are ready. Before full Whisper training finishes, two encoder-cache nodes run alongside two dedicated Whisper training nodes and one probe/evaluation node. Afterwards, the freed capacity permits up to four encoder-cache nodes; pending/running evaluations reduce that limit as needed. Maximum concurrency remains five study nodes, with four GPUs per node. Held jobs and unmet queue dependencies reserve no compute node.

', + '

To fit earlier scheduler openings, full Whisper fine-tuning and evaluation jobs accept time windows as short as one hour; probe training accepts windows as short as 45 minutes, and encoder caching as short as two hours. These are allocation limits, not promised completion times. Training saves model, optimizer and scheduler states at epoch boundaries; probes additionally save their random states. Interrupted epochs restart from the last completed epoch; caches skip every committed chunk. Queue priority and the remaining provider batches still determine actual start times.

'] + progress=[] + for path in sorted((ROOT/'probes').rglob('metrics.jsonl')): + completed=[] + for line in path.read_text().splitlines(): + try:completed.append(json.loads(line)) + except json.JSONDecodeError:continue + if completed: + row=completed[-1];validation=row.get('validation',{}) + progress.append([str(path.parent.relative_to(ROOT/'probes')),row['stage'],row['epoch'], + row['update'],number(validation.get('loss')),validation.get('clips','Unknown')]) + for size in ('base','small'): + path=GEMINI/'training'/('whisper_'+size)/'metrics.jsonl' + if path.exists(): + for line in path.read_text().splitlines()[::-1]: + try:row=json.loads(line) + except json.JSONDecodeError:continue + progress.append(['Gemini full FT / Whisper '+size,'Gemini',row['epoch'],row['update'], + number(row.get('validation_loss')),'See fine-tuning config']);break + if progress: + parts += ['

Completed training epochs and interim validation

', + table(['Run','Last completed stage','Epoch','Optimizer updates','Multitask validation loss ↓','Validation clips'],progress), + '

These are recorded epoch checks, before the final held-out benchmark evaluation. The loss combines masked normalized regression, speaker embedding and burst objectives with different weights; it is not accuracy, correlation or a paper benchmark score. Compare final per-target and public benchmark metrics below once they exist.

'] + retention=[] + for size in ('base','small'): + out=ROOT/'whisper/gemini'/size + for domain,before_file,after_file in [('Flash test','legacy_on_flash_test_metrics.json','gemini_test_metrics.json'), + ('P3 validation','legacy_p3_validation_metrics.json','gemini_p3_validation_metrics.json'), + ('P3 test','legacy_p3_test_metrics.json','gemini_p3_test_metrics.json')]: + before=optional(out/before_file);after=optional(out/after_file) + if before and after: + retention.append([size,domain,after['clips'],number(before.get('frame_f1')),number(after.get('frame_f1')), + number(after['frame_f1']-before['frame_f1'])]) + if retention: + parts += ['

Whisper burst agreement before and after Flash tuning

', + table(['Encoder','Identical held-out split','Clips','Original frame F1','Tuned frame F1','Absolute change'],retention), + '

Frame F1 uses a fixed 0.5 decision threshold on the 20 ms grid. Flash test labels are machine-generated event annotations; P3 labels are known synthetic construction intervals. These domains have different labels and acoustic distributions. The current fine-tunes improve agreement with Flash targets while reducing performance on P3. Checkpoints were selected by Flash validation loss, and this two-epoch tuning stage contained no P3 replay. The separate localization IoU and boundary-error metrics below must also be considered; frame F1 alone does not measure event localization accuracy.

'] + model_rows=[] + for m in cfg['models']: + source=m.get('repo',m.get('model_name')) + jobs=workflow.get('cache_jobs',{}).get(m['id'],[]) + temporal='160 ms patches' if m['backend']=='clap' else '20 ms' if m['backend']=='commercial' else 'Native audio-token grid; verified by cached token counts' + model_rows.append([m['id'],source,m['native_dim'],temporal,', '.join(str(j['id']) for j in jobs) or 'Not submitted', + workflow.get('cache_states',{}).get(m['id'],'Pending')]) + parts += ['

Backbones and feature extraction

',table(['Backbone','Source','Native pooled dimensions','Native temporal resolution','Cache Slurm jobs','Execution state'],model_rows), + '

All nine embedding backbones stay frozen. Audio is resampled to mono 16 kHz and cropped to 30 seconds where necessary; NaFlex performs its native 32 kHz feature transform. No reference transcription, caption, ASR decoding or generated answer enters a probe. The exact epoch-320 CLAP checkpoints are used; Large is the 32-node checkpoint. Native embeddings and time-indexed features are saved once in immutable TAR sidecars and reused.

', + '

Training-only PCA maps pooled embeddings to 256 dimensions and scales components using training statistics. XXS has 224 native dimensions: 224 retained components are padded with zeros to 256. Temporal features use a fixed seeded projection to 64 dimensions. Interpolation onto the common 20 ms target grid does not improve a backbone’s native timing resolution.

', + '

Heads, targets and losses

',table(['Objective','Head','Trainable parameters','Loss / evaluation'],[ + ['Each of 192 scalar outputs, plus CPS','256 → 64 GELU → 1',16513,'Masked normalized Huber; raw MAE, normalized MAE, Pearson and Spearman'], + ['Linear scalar control','256 → 1',257,'Same target masks and Huber loss'], + ['Orange timbre','256 → 64 → 128',24768,'Cosine + Huber; held-out cosine similarity'], + ['Orange identity','256 → 64 → 250',32698,'Cosine + Huber; held-out cosine similarity'], + ['Burst frame / onset / log duration','64 → 64 → 3',4355,'BCE + Dice, onset BCE and duration Huber; frame F1 and interval IoU'], + ['Burst event class','Mean of frames inside each event → 64 → 53',7605,'Categorical cross entropy; oracle-span and matched predicted-span accuracy'], + ['CREMA-D adapter (every model)','192 scalar predictions → 64 GELU → 6',12742,'Cross entropy; actor-held-out top-1, balanced accuracy and macro F1'], + ['RAVDESS adapter (every model)','192 scalar predictions → 64 GELU → 8',12872,'Same protocol; calm and neutral remain separate']]), + '

The 192-score schema contains 40 EmoNet emotion targets, 57 VoiceNet dimensions, genuineness/blend/quality, Empathic Insight Plus outputs, AudioBox scores, all DNSMOS outputs, burst count and additional VoiceCLAP attributes. CPS is an independent 193rd scalar. Missing or out-of-domain teacher targets are masked; they are not replaced with zero labels. Gemini Flash values override older teacher values wherever a valid corresponding Flash annotation exists. Orange vectors come from their audio models; Gemini does not synthesize embeddings.

', + '

Training schedule and comparison limits

',table(['Run','Data','Epochs','Optimizer / schedule'],[ + ['Legacy embedding probes','S8 → S9 → S10, with a 10% synthetic P3 mixture at each stage','One per stage','AdamW LR 0.001; 5% warmup; continuous cosine with 10% floor; batch 256 per GPU'], + ['Gemini embedding probes','Final hash-deduplicated Flash clips; existing teacher sidecars','Two','Initialize each corresponding legacy probe; frozen backbone and legacy training PCA; new optimizer'], + ['Whisper Base fine-tune','Same Flash targets, same deterministic held-out partitions','Two','Encoder LR 0.00001; heads LR 0.0001; global batch 128'], + ['Whisper Small fine-tune','Same Flash targets','Two','Encoder LR 0.000005; heads LR 0.0001; global batch 128']]), + '

The original Whisper encoders were trained over S1–S10 and have larger heads and a different training budget. Identical probe inputs/head sizes control downstream capacity across embedding backbones; they do not equalize backbone size, pretraining data or previous Whisper exposure. Ladder holdouts here are withheld from the new probes, but may have been seen by the previously trained Whisper model. Underlying Emolia source overlap and upstream benchmark exposure are not audited. Before/after Gemini scores use exactly the same Flash test clips; improvements combine extra training and changed targets.

', + '

Benchmark protocol

VoiceNet-Emo is emolia-emo; VoiceNet-Ext is emolia-dim, not an additional independent benchmark. We report the current repository ≥2-rater cut and unflagged cut, threshold-free mean per-prompt Spearman, the paper-style oracle threshold statistic and a separate five-fold clip-grouped threshold audit. Oracle thresholds fit the evaluated labels and must not be presented as held-out classification. The current Ext snapshot’s rater counts differ from the paper; see the existing detailed report.

', + '

EmoNet human intensities 0/1/2 become 0/5/10; native emotion outputs use a fixed 2.5 endpoint conversion without fitting benchmark labels. Forty mapped emotion categories cover 12,000 of the released 12,600 clips. CREMA-D and RAVDESS fixed taxonomy mappings are reported separately from supervised adapters. Each new adapter uses all 192 predictions, exactly the same parameter count across models, five outer actor-disjoint folds and three inner actor-disjoint folds to select LR (0.001 or 0.003) and epochs (20 or 50). Scaling is fitted inside each training fold. Every clip receives one outer held-out prediction; reported confidence intervals resample actors.

'] + outputs=[] + baseline=optional(BENCH/'public_scores.json').get('models',{}) + transfer=[] + for size in ('base','small'): + previous=baseline.get(size,{}) + tuned=optional(ROOT/'whisper/gemini'/size/'public_metrics.json') + for benchmark,key,metric,label in [ + ('emolia-emo','emolia_emo','mean_prompt_spearman','Mean per-prompt Spearman'), + ('emolia-dim','emolia_dim','mean_prompt_spearman','Mean per-prompt Spearman')]: + before=previous.get(key,{}).get('repo_min_2_raters',{}) + after=tuned.get(benchmark,{}).get('repo_min_2_raters',{}) + if before and after: + transfer.append([size,benchmark,'Repository ≥2 raters; '+str(after['n'])+' pairs',label, + number(before.get(metric)),number(after.get(metric))]) + for benchmark in ('crema','ravdess'): + before=optional(ROOT/'whisper/legacy'/size/(benchmark+'_matched_adapter.json')).get('metrics',{}) + after=optional(ROOT/'whisper/gemini'/size/(benchmark+'_matched_adapter.json')).get('metrics',{}) + if before and after: + transfer.append([size,benchmark,'Same nested actor-disjoint folds','Matched score-MLP accuracy', + number(before.get('accuracy')),number(after.get('accuracy'))]) + if transfer: + parts += ['

Whisper public-benchmark transfer after two full tuning epochs

', + table(['Encoder','Benchmark','Evaluation selection','Metric ↑','Original checkpoint','Flash-tuned checkpoint'],transfer), + '

The original rows reuse existing benchmark predictions for Base S4 and Small S3. The tuned rows evaluate their new full fine-tunes. The two Emolia correlations use the same repository ≥2-rater selection and require no fitted decision threshold. CREMA-D and RAVDESS use supervised score adapters with the same architecture and nested actor-disjoint protocol before and after tuning. Results are mixed: emotion correlations improve, CREMA-D is nearly unchanged for Base and slightly higher for Small, while RAVDESS adapter accuracy decreases for both. These measurements do not establish a uniform improvement across tasks.

'] + reference=[] + for name in ('base','small'): + result=baseline.get(name,{}) + emo=result.get('emolia_emo',{}).get('repo_min_2_raters',{}) + dim=result.get('emolia_dim',{}).get('repo_min_2_raters',{}) + en=result.get('emonet',{}).get('all_mapped_40',{}) + reference.append(['Original Whisper '+name,number(emo.get('balanced_accuracy_oracle_per_prompt')), + number(emo.get('mean_prompt_spearman')),number(dim.get('balanced_accuracy_oracle_per_prompt')), + number(dim.get('mean_prompt_spearman')),number(en.get('pearson')),'Existing regression-head evaluation']) + reference.extend([ + ['VoiceCLAP-Small, VoiceNet paper','0.6754','0.3176','0.6367','0.1051','Different EmoNet protocol','Paper embedding/text-prompt evaluation'], + ['VoiceCLAP-Large, VoiceNet paper','0.7021','0.3719','0.6510','0.1475','Different EmoNet protocol','Paper embedding/text-prompt evaluation'], + ['VoiceCLAP-Large-v2, released model card','0.7069','0.3865','0.6816','0.2125','Not specified here','Released embedding benchmark numbers, not the new probes']]) + parts += ['

Existing results and published reference context

', + table(['Model','Emo oracle bal@pp','Emo mean prompt ρ','Ext oracle bal@pp','Ext mean prompt ρ','EmoNet Pearson','Protocol'],reference), + '

These Whisper rows predate Gemini tuning and this embedding-probe study. Paper and model-card rows are quoted as context from their linked primary sources; current benchmark snapshots, regression scoring versus text-prompt similarity, and upstream training exposure differ. These are not controlled same-protocol rankings. The newly trained probes will appear separately below.

'] + for phase in ('legacy','gemini'): + for m in cfg['models']: + for variant in ('linear','mlp'): + out=ROOT/'probes'/phase/m['id']/variant + outputs.append((phase+' / '+m['id']+' / '+variant,out)) + for phase in ('legacy','gemini'): + for m in ('base','small'): + outputs.append((phase+' / Whisper '+m,ROOT/'whisper'/phase/m)) + rows=[] + details=[] + for name,out in outputs: + public=optional(out/'public_metrics.json') + internal=optional(out/'test_metrics.json') or optional(out/'gemini_test_metrics.json') + domain='Flash test' if name.startswith('gemini') else 'Ladder S8–S10 + P3 test' + if name.startswith('legacy / Whisper '): + size=name.rsplit(' ',1)[-1] + old=baseline.get(size,{}) + public=public or {key:old[source] for key,source in [ + ('emolia-emo','emolia_emo'),('emolia-dim','emolia_dim'), + ('emonet','emonet'),('crema','crema'),('ravdess','ravdess')] if source in old} + internal=internal or optional(ROOT/'whisper/gemini'/size/'legacy_on_flash_test_metrics.json') + domain='Flash test, original checkpoint' + emo=public.get('emolia-emo',{}).get('repo_min_2_raters',{}) + dim=public.get('emolia-dim',{}).get('repo_min_2_raters',{}) + emonet=public.get('emonet',{}).get('all_mapped_40',{}) + adapters=[optional(out/(kind+'_matched_adapter.json')).get('metrics',{}) for kind in ('crema','ravdess')] + rows.append([name,'Complete' if public else 'Pending',number(emo.get('balanced_accuracy_oracle_per_prompt')), + number(emo.get('mean_prompt_spearman')),number(dim.get('mean_prompt_spearman')), + number(adapters[0].get('accuracy')),number(adapters[1].get('accuracy')),domain,number(internal.get('frame_f1'))]) + if internal or public: + details.append('
'+esc(name)+' — complete metrics and target tables') + if internal: + details.append(table(['Target','Valid N','Raw MAE ↓','Normalized MAE ↓','Pearson ↑','Spearman ↑'], + [[v['score'],v['n'],number(v.get('raw_mae')),number(v.get('normalized_mae')), + number(v.get('pearson')),number(v.get('spearman'))] for v in internal.get('per_score',[])])) + details.append(table(['Additional metric','Result'],[[k,json.dumps(v,ensure_ascii=False)] for k,v in internal.items() if k!='per_score'])) + details.append('
'+esc(json.dumps(public,indent=2))+'
') + if 'Whisper ' in name and name.startswith('gemini'): + for phase in ('legacy','gemini'): + for domain in ('p3_validation','p3_test'): + result=optional(out/(phase+'_'+domain+'_metrics.json')) + if result: + details.append('
'+esc(name+' / '+phase+' / '+domain)+' — synthetic retention audit'+table( + ['Target','Valid N','Raw MAE','Normalized MAE','Pearson','Spearman'], + [[v['score'],v['n'],number(v.get('raw_mae')),number(v.get('normalized_mae')), + number(v.get('pearson')),number(v.get('spearman'))] for v in result.get('per_score',[])])+ + '
'+esc(json.dumps({k:v for k,v in result.items() if k!='per_score'},indent=2))+'
') + parts += ['

Measured comparisons

',table(['Run','Public scores','Emo oracle bal@pp','Emo mean prompt ρ','Ext mean prompt ρ','CREMA-D matched MLP accuracy','RAVDESS matched MLP accuracy','Internal test domain','Burst frame F1'],rows), + '

Internal frame F1 must be compared within the same test domain. Legacy embedding probes use the mixed Ladder/P3 holdout; Gemini runs use Flash test labels. Original and tuned Whisper rows both use the same Flash test clips; their separate P3 retention audit is above.

', + '

Pending means the experiment has not produced a complete evaluation artifact. None of the pending rows are estimates. Supervised actor-CV adapter accuracy cannot be directly ranked against zero-shot paper accuracy.

',*details, + '

Reference papers and source models

VoiceNet paper · EmoNet-Voice paper · Human benchmark labels and taxonomy · Empathic Insight Voice Plus.

    '+''.join('
  • '+esc(m['repo'])+'; pinned revision '+esc(m['revision'])+'
    • ' for m in cfg['models'] if m.get('repo'))+'
', + '

Study artifacts: '+esc(ROOT)+'. Machine-readable protocol: study.json. Source code: embedding-probe-code; Gemini full fine-tuning code and exact configurations. Report and new study code: CC BY 4.0, LAION; upstream models and datasets retain their own licenses.

'] + style='body{font:16px/1.6 system-ui;background:#f3f6fa;color:#172b40;margin:0}main{max-width:1400px;margin:auto;padding:36px}h1{font-size:38px;line-height:1.2}h2{margin-top:36px}.lead{font-size:20px}.scroll{overflow:auto}table{border-collapse:collapse;width:100%;background:white;font-size:14px}th,td{padding:10px;border-bottom:1px solid #dde4ed;text-align:left;vertical-align:top}th{background:#e0ebf5}details{background:white;padding:14px;margin:12px 0}pre{white-space:pre-wrap;max-height:600px;overflow:auto}a{color:#075a9d}' + OUT.write_text('Audio embedding probe comparison
'+''.join(parts)+'
') + print(OUT) + + +if __name__=='__main__': + main() diff --git a/training/evaluate_public_benchmarks.py b/training/evaluate_public_benchmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..28ac23f84e91c065f4287a3fbbc4ca670a995001 --- /dev/null +++ b/training/evaluate_public_benchmarks.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python3 +"""Reproducible inference of best layered checkpoints on public emotion benchmarks. + +Writes one NPZ with raw 192 target scores per benchmark and an aligned JSONL +manifest. Metric calculation and HTML rendering are intentionally separate. +""" +from __future__ import annotations + +import argparse +import ast +import csv +import io +import json +import math +import os +from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq +import soundfile as sf +import torch +from safetensors.torch import load_file +from scipy.signal import resample_poly +from transformers import WhisperFeatureExtractor + +from layered_multitask_model import LayeredMultiTaskWhisper + + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/bench_eval_20261004') +RELEASE = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/hf_layered_release/model') +STAGES = {'base': 'S4', 'small': 'S3'} + + +@dataclass +class Sample: + key: str + audio: bytes | Path + metadata: dict + + +def parquet_samples(kind: str) -> list[Sample]: + folder = {'emonet': 'emonet', 'crema': 'crema', 'ravdess': 'ravdess'}[kind] + samples: list[Sample] = [] + for path in sorted((ROOT / folder / 'data').glob('*.parquet')): + pf = pq.ParquetFile(path) + for batch in pf.iter_batches(batch_size=256): + for row in batch.to_pylist(): + col = 'audioId' if kind == 'emonet' else 'audio' + audio = row[col] + if kind == 'emonet': + ratings = ast.literal_eval(row['label']) + targets = [] + for annotation in ratings: + for response in annotation.values(): + for name, intensity in response.items(): + targets.append([name, int(intensity)]) + meta = {'ratings': targets, 'audio_name': audio['path']} + elif kind == 'crema': + meta = {'emotion': row['emotion'], 'audio_name': audio['path']} + else: + meta = {'emotion': row['emotion'], 'audio_name': audio['path'], + 'actor': row['actor'], 'vocal_channel': row['vocal_channel']} + samples.append(Sample(f'{kind}:{len(samples)}', audio['bytes'], meta)) + return samples + + +def emolia_samples(kind: str) -> list[Sample]: + repo = ROOT / 'emolia' / 'repo' + labels = repo / 'analysis_outputs' / kind / 'benchmark_labels.csv' + rows = list(csv.DictReader(labels.open())) + samples: list[Sample] = [] + if kind == 'emolia-emo': + index: dict[str, Path] = {} + for path in sorted((repo / 'dataset' / kind / 'data').rglob('*.mp3')): + index.setdefault(path.stem, path) + grouped: dict[str, list[dict]] = {} + for row in rows: + grouped.setdefault(Path(row['file_name']).stem, []).append(row) + for stem, group in grouped.items(): + path = index.get(stem) + if path is None: + raise FileNotFoundError(stem) + samples.append(Sample(f'{kind}:{stem}', path, + {'audio_name': path.name, 'rows': group})) + else: + for row in rows: + if row['dimension'] == 'LANG': + continue # No LANG head; all 57 ordinal dimensions are retained. + path = (repo / 'dataset' / kind / 'data' / row['dimension'] / + row['level'] / row['polarity'] / row['file_name']) + if not path.is_file(): + raise FileNotFoundError(path) + samples.append(Sample(f'{kind}:{len(samples)}', path, + {'audio_name': str(path.relative_to(repo)), 'rows': [row]})) + return samples + + +def decode(sample: Sample) -> tuple[np.ndarray, float, bool]: + source = io.BytesIO(sample.audio) if isinstance(sample.audio, bytes) else sample.audio + wave, rate = sf.read(source, dtype='float32', always_2d=True) + wave = wave.mean(axis=1) + duration = len(wave) / rate + if rate != 16000: + divisor = math.gcd(rate, 16000) + wave = resample_poly(wave, 16000 // divisor, rate // divisor).astype(np.float32) + truncated = len(wave) > 480000 + wave = wave[:480000] + if len(wave) < 1600: + wave = np.pad(wave, (0, 1600 - len(wave))) + return np.nan_to_num(wave), duration, truncated + + +def run(args: argparse.Namespace) -> None: + torch.set_num_threads(2) + device = torch.device(args.device) + stage = STAGES[args.model] + suffix = f'_smoke{args.limit}' if args.limit else '' + out = ROOT / 'predictions' / f'{args.dataset}_{args.model}_{stage}{suffix}' + out.parent.mkdir(parents=True, exist_ok=True) + if out.with_suffix('.npz').exists() and not args.overwrite: + print('EXISTS', out, flush=True) + return + print('LOADING', args.dataset, flush=True) + samples = (parquet_samples(args.dataset) if args.dataset in ('emonet', 'crema', 'ravdess') + else emolia_samples(args.dataset)) + if args.limit: + samples = samples[:args.limit] + print('DECODING', len(samples), flush=True) + with ThreadPoolExecutor(max_workers=args.decode_workers) as pool: + decoded = list(pool.map(decode, samples)) + print('DECODED', len(decoded), 'seconds', round(sum(d for _, d, _ in decoded)), flush=True) + model_dir = RELEASE / args.model + classes = json.loads((RELEASE / 'classes.json').read_text())['names'] + train_norm = json.loads((RELEASE / 'training_normalization.json').read_text()) + mean = np.asarray(train_norm['score_mean'], dtype=np.float32) + std = np.asarray(train_norm['score_std'], dtype=np.float32) + model = LayeredMultiTaskWhisper(model_dir, len(classes), initialize_pretrained=False) + model.load_state_dict(load_file(str(model_dir / stage / 'model.safetensors'), device='cpu'), strict=True) + model = model.to(device).eval() + extractor = WhisperFeatureExtractor.from_pretrained(model_dir, local_files_only=True) + order = sorted(range(len(samples)), key=lambda i: len(decoded[i][0])) + result = np.empty((len(samples), 192), dtype=np.float32) + print('INFER', args.model, stage, args.dataset, flush=True) + with torch.inference_mode(): + for offset in range(0, len(order), args.batch_size): + ids = order[offset:offset + args.batch_size] + wave = [decoded[i][0] for i in ids] + features = extractor(wave, sampling_rate=16000, padding='longest', + truncation=True, max_length=480000, + return_attention_mask=True, return_tensors='np') + mel = features['input_features'].astype(np.float32) + mask = features['attention_mask'].astype(np.int64) + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + mask = np.pad(mask, ((0, 0), (0, 1))) + if device.type == 'cuda': + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(torch.from_numpy(mel).to(device), + torch.from_numpy(mask).to(device)) + else: + output = model(torch.from_numpy(mel).to(device), + torch.from_numpy(mask).to(device)) + scores = output['scores'].float().cpu().numpy() * std + mean + result[ids] = scores + if offset // args.batch_size % 50 == 0 or offset + len(ids) == len(order): + print(args.model, args.dataset, min(offset + len(ids), len(order)), '/', len(order), flush=True) + rows = [{**sample.metadata, 'key': sample.key, 'duration_s': float(decoded[i][1]), + 'truncated_30s': bool(decoded[i][2])} for i, sample in enumerate(samples)] + np.savez_compressed(out.with_suffix('.npz'), scores=result) + tmp = out.with_suffix('.jsonl.incomplete') + tmp.write_text(''.join(json.dumps(row, ensure_ascii=False) + '\n' for row in rows)) + os.replace(tmp, out.with_suffix('.jsonl')) + print('COMPLETE', out, len(samples), flush=True) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--model', choices=('base', 'small'), required=True) + parser.add_argument('--dataset', choices=('emolia-emo', 'emolia-dim', 'emonet', 'crema', 'ravdess'), required=True) + parser.add_argument('--device', default='cuda:0') + parser.add_argument('--batch-size', type=int, default=16) + parser.add_argument('--decode-workers', type=int, default=16) + parser.add_argument('--limit', type=int) + parser.add_argument('--overwrite', action='store_true') + run(parser.parse_args()) diff --git a/training/full_ladder_dataset.py b/training/full_ladder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..87e468d0ea844ff37a312e32825b1336d0c93c09 --- /dev/null +++ b/training/full_ladder_dataset.py @@ -0,0 +1,253 @@ +#!/usr/bin/env python3 +"""Mixed random-access Ladder/P3 loader for the continuous score curriculum.""" +from __future__ import annotations + +import bisect +import io +import json +import math +import os +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from scipy.signal import resample_poly +from torch.utils.data import Dataset, Sampler +from transformers import WhisperFeatureExtractor + +from p3_frozen_loader import P3FrozenDataset +from p3_frozen_mix_schedule import p3_train_position +from prepare_full_ladder import DTYPE, LEVELS, OUT + + +class LadderAudioDataset(Dataset): + def __init__(self, mean: np.ndarray, std: np.ndarray, root: Path = OUT): + spec = json.loads((root / 'manifest.json').read_text()) + if spec['status'] != 'complete' or spec['levels'] != list(LEVELS): + raise RuntimeError('full Ladder index is not complete') + self.shards = spec['shards'] + self.counts = np.asarray([x['rows'] for x in self.shards], dtype=np.int64) + self.ends = np.cumsum(self.counts) + self.mean = np.asarray(mean, dtype=np.float32) + self.std = np.asarray(std, dtype=np.float32) + if self.mean.shape != (192,) or self.std.shape != (192,): + raise ValueError('expected 192 shared score normalization entries') + self._rows: OrderedDict[int, np.ndarray] = OrderedDict() + self._fds: OrderedDict[int, int] = OrderedDict() + + def __len__(self) -> int: + return int(self.ends[-1]) + + def __getstate__(self): + state = self.__dict__.copy() + state['_rows'] = OrderedDict() + state['_fds'] = OrderedDict() + return state + + def close(self): + for fd in self._fds.values(): + os.close(fd) + self._fds.clear() + self._rows.clear() + + def __del__(self): + if hasattr(self, '_fds'): + self.close() + + def _row(self, shard: int, local: int): + if shard not in self._rows: + if len(self._rows) >= 32: + self._rows.popitem(last=False) + rows = np.load(self.shards[shard]['meta'], mmap_mode='r') + if rows.dtype != DTYPE or len(rows) != self.shards[shard]['rows']: + raise RuntimeError(f'stale metadata shard {shard}') + self._rows[shard] = rows + else: + self._rows.move_to_end(shard) + return self._rows[shard][local] + + def _audio(self, shard: int, row) -> np.ndarray: + if shard not in self._fds: + if len(self._fds) >= 32: + _, fd = self._fds.popitem(last=False) + os.close(fd) + self._fds[shard] = os.open(self.shards[shard]['audio'], os.O_RDONLY) + else: + self._fds.move_to_end(shard) + raw = os.pread(self._fds[shard], int(row['audio_size']), int(row['audio_offset'])) + if len(raw) != int(row['audio_size']): + raise RuntimeError(f'short audio read in shard {shard}') + audio, sr = sf.read(io.BytesIO(raw), dtype='float32', always_2d=False) + if audio.ndim == 2: + audio = audio.mean(axis=1) + if sr != 16000: + common = math.gcd(sr, 16000) + audio = resample_poly(audio, 16000 // common, sr // common).astype(np.float32) + audio = np.nan_to_num(audio[:480000], copy=False) + if len(audio) < 1600: + audio = np.pad(audio, (0, 1600 - len(audio))) + return audio + + def get_shard_row(self, shard: int, local: int) -> dict: + row = self._row(shard, local) + audio = self._audio(shard, row) + raw_scores = row['scores'].astype(np.float32) + valid = np.isfinite(raw_scores) + scores = np.zeros(192, dtype=np.float32) + score_mask = np.zeros(192, dtype=np.bool_) + scores[:100] = np.where(valid, (raw_scores - self.mean[:100]) / self.std[:100], 0) + score_mask[:100] = valid + if row['burst_count_valid']: + scores[130] = (float(row['burst_count_log1p']) - self.mean[130]) / self.std[130] + score_mask[130] = True + n_frames = min(1500, (len(audio) + 319) // 320) + frame = np.zeros(n_frames, dtype=np.float32) + frame_mask = np.full(n_frames, float(row['burst_frame_valid']), dtype=np.float32) + if row['burst_frame_valid']: + for i in range(int(row['event_count'])): + start = int(float(row['starts'][i]) * 50) + end = math.ceil(float(row['ends'][i]) * 50) + frame[max(0, start):min(n_frames, end)] = 1 + uid = bytes(row['uid']).rstrip(b'\0').decode('utf-8') + return {'key': uid, 'uid': uid, 'domain': 'ladder', 'audio': audio, + 'scores': scores, 'score_mask': score_mask, + 'frame': frame, 'frame_mask': frame_mask, + 'timbre': np.zeros(128, np.float32), + 'identity': np.zeros(250, np.float32), + 'timbre_valid': False, 'identity_valid': False} + + def __getitem__(self, index: int) -> dict: + shard = bisect.bisect_right(self.ends, int(index)) + start = 0 if shard == 0 else int(self.ends[shard - 1]) + return self.get_shard_row(shard, int(index) - start) + + +class MixedDataset(Dataset): + def __init__(self, ladder: LadderAudioDataset, p3: P3FrozenDataset): + self.ladder, self.p3 = ladder, p3 + + def __len__(self): + return len(self.ladder) + len(self.p3) + + def __getitem__(self, coordinate: tuple[str, int, bool]) -> dict: + domain, index, enabled = coordinate + if domain == 'ladder': + result = self.ladder[int(index)] + elif domain == 'p3': + result = self.p3[int(index)] + result['uid'] = result['key'] + result['domain'] = 'p3' + result['timbre_valid'] = result['speaker_valid'] + result['identity_valid'] = result['speaker_valid'] + else: + raise ValueError(domain) + result['enabled'] = bool(enabled) + if not enabled: + result['score_mask'][:] = False + result['frame_mask'][:] = 0 + result['timbre_valid'] = False + result['identity_valid'] = False + return result + + +class StageBatches(Sampler[list[tuple[str, int, bool]]]): + """Exact stage counts, whole-domain batches, deterministic DDP sharding.""" + def __init__(self, ladder: LadderAudioDataset, schedule: dict, stage_index: int, + *, batch_per_rank: int, world: int, rank: int, start_batch: int = 0): + self.ladder = ladder + self.schedule = schedule + self.stage = schedule['stages'][stage_index] + self.stage_index = stage_index + self.batch_per_rank = batch_per_rank + self.world = world + self.rank = rank + self.width = batch_per_rank * world + self.ladder_batches = math.ceil(self.stage['ladder'] / self.width) + self.p3_batches = math.ceil(self.stage['p3'] / self.width) + self.total_batches = self.ladder_batches + self.p3_batches + self.start_batch = start_batch + if not 0 <= rank < world or not 0 <= start_batch <= self.total_batches: + raise ValueError('invalid DDP batch coordinate') + allowed = set(LEVELS[:len(LEVELS) - stage_index]) + rng = np.random.default_rng(640913 + stage_index) + self.shards = np.asarray([i for i, spec in enumerate(ladder.shards) + if spec['level'] in allowed], dtype=np.int32) + rng.shuffle(self.shards) + self.ends = np.cumsum(ladder.counts[self.shards]) + if int(self.ends[-1]) != self.stage['ladder']: + raise RuntimeError(f'{self.stage["stage"]}: Ladder row count mismatch') + + def __len__(self) -> int: + return self.total_batches - self.start_batch + + def _ladder_index(self, position: int) -> int: + shard_position = bisect.bisect_right(self.ends, position) + shard = int(self.shards[shard_position]) + start = 0 if shard_position == 0 else int(self.ends[shard_position - 1]) + local = position - start + return (0 if shard == 0 else int(self.ladder.ends[shard - 1])) + local + + def __iter__(self): + for batch in range(self.start_batch, self.total_batches): + p3_before = batch * self.p3_batches // self.total_batches + p3_after = (batch + 1) * self.p3_batches // self.total_batches + if p3_after > p3_before: + domain = 'p3' + first = p3_before * self.width + self.rank * self.batch_per_rank + limit = self.stage['p3'] + else: + domain = 'ladder' + first = (batch - p3_before) * self.width + self.rank * self.batch_per_rank + limit = self.stage['ladder'] + result = [] + for i in range(first, first + self.batch_per_rank): + enabled = i < limit + position = i if enabled else 0 + index = (p3_train_position(self.schedule, + self.stage['p3_exposure_start'] + position) + if domain == 'p3' else self._ladder_index(position)) + result.append((domain, index, enabled)) + yield result + + +class MixedCollator: + def __init__(self, model_path: Path): + self.model_path = str(model_path) + self.extractor = None + + def __getstate__(self): + return {'model_path': self.model_path, 'extractor': None} + + def __call__(self, batch: list[dict]) -> dict: + if self.extractor is None: + self.extractor = WhisperFeatureExtractor.from_pretrained( + self.model_path, local_files_only=True) + feat = self.extractor([x['audio'] for x in batch], sampling_rate=16000, + padding='longest', truncation=True, max_length=480000, + return_attention_mask=True, return_tensors='np') + mel = feat['input_features'].astype(np.float32) + mel_mask = feat['attention_mask'].astype(np.int64) + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + mel_mask = np.pad(mel_mask, ((0, 0), (0, 1))) + n_frames = mel.shape[-1] // 2 + frame = np.zeros((len(batch), n_frames), dtype=np.float32) + frame_mask = np.zeros_like(frame) + for i, item in enumerate(batch): + n = min(n_frames, len(item['frame'])) + frame[i, :n] = item['frame'][:n] + frame_mask[i, :n] = item['frame_mask'][:n] + return {'uid': [x['uid'] for x in batch], + 'domain': [x['domain'] for x in batch], + 'enabled': torch.tensor([x.get('enabled', True) for x in batch]), + 'mel': torch.from_numpy(mel), 'mel_mask': torch.from_numpy(mel_mask), + 'scores': torch.from_numpy(np.stack([x['scores'] for x in batch])), + 'score_mask': torch.from_numpy(np.stack([x['score_mask'] for x in batch])), + 'frame': torch.from_numpy(frame), + 'frame_mask': torch.from_numpy(frame_mask), + 'timbre': torch.from_numpy(np.stack([x['timbre'] for x in batch])), + 'identity': torch.from_numpy(np.stack([x['identity'] for x in batch])), + 'timbre_valid': torch.tensor([x['timbre_valid'] for x in batch]), + 'identity_valid': torch.tensor([x['identity_valid'] for x in batch])} diff --git a/training/gemini_finetune/backfill.py b/training/gemini_finetune/backfill.py new file mode 100644 index 0000000000000000000000000000000000000000..18985c51c2aa7ec05f660f63aa03cf00b7ba6e1f --- /dev/null +++ b/training/gemini_finetune/backfill.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +"""Offline missing-teacher annotation, exact-audio hashes, resumable TAR sidecars. + +Runs on compute GPUs. Waveforms are decoded once per batch and reused by the +requested teachers. Never creates per-clip audio/embedding files. +""" +from __future__ import annotations + +import argparse +import io +import json +import os +import sys +import tarfile +import time +import threading +from concurrent.futures import ThreadPoolExecutor +from datetime import datetime, timezone +from pathlib import Path + +from common import (CODE, HOME, ROOT, TarReader, add_member, jsonl, read_json, write_json) + + +class Teachers: + def __init__(self, names): + import torch + self.torch = torch + self.models = {} + self.names = set(names) + self.timing_s = {} + sys.path.insert(0, str(HOME / 'vocal_burst_pool/p3')) + sys.path.insert(0, str(CODE)) + if 'orange' in names: + from timbre_targets import OrangeTimbreTeacher, OrangeIdentityTeacher + self.models['timbre'] = OrangeTimbreTeacher().to('cuda').eval() + self.models['identity'] = OrangeIdentityTeacher().to('cuda').eval() + if 'dnsmos' in names: + import annotate_dnsmos as dns + self.dns = dns + self.dns_workers = int(os.environ.get('TEACHER_DNSMOS_WORKERS', '1')) + self.dns_threads = int(os.environ.get('TEACHER_DNSMOS_THREADS', '2')) + if self.dns_workers < 1 or self.dns_threads < 1: + raise ValueError('DNSMOS workers and threads must be positive') + self._dns_local = threading.local() + self.dns_pool = None + if self.dns_workers == 1: + self.models['dnsmos'] = self._new_dns_scorer() + else: + self.dns_pool = ThreadPoolExecutor(max_workers=self.dns_workers, + initializer=self._init_dns_scorer) + if 'audiobox' in names: + import annotate_audiobox as box + self.box = box + self.models['audiobox'] = box.AesMultiOutput.from_pretrained(str(box.MODEL)).to('cuda').eval() + if 'voiceclap' in names: + import annotate_voiceclap as voice + self.voice = voice + predictor = voice.VoiceNetPredictor(root=str(voice.VN), device='cuda') + self.models['voiceclap'] = predictor + self.gen = voice.weights(voice.GEN / 'genuineness_head.pt', 'genuineness') + self.blend = voice.weights(voice.BLEND / 'blend_head_commercial.pt', 'blend') + bundle = torch.load(voice.ATTR / 'heads.pt', map_location='cpu', weights_only=False) + self.attr_mu, self.attr_sd = bundle['emb_mu'].to('cuda').float(), bundle['emb_sd'].to('cuda').float() + self.attr_heads = {n: voice.build_attribute_head(state).to('cuda').eval() for n, state in bundle['heads'].items()} + if 'empathic' in names: + import annotate_empathic as eiv + self.eiv = eiv + self.models['eiv_feature'] = eiv.WhisperFeatureExtractor.from_pretrained(eiv.BUD, local_files_only=True) + full = eiv.WhisperModel.from_pretrained(eiv.BUD, local_files_only=True, low_cpu_mem_usage=True) + self.models['eiv_encoder'] = full.get_encoder().to('cuda').eval() + del full + self.models['eiv_experts'] = eiv.models() + + def _new_dns_scorer(self): + return self.dns.ComputeScore(str(self.dns.CODE / 'DNSMOS/sig_bak_ovr.onnx'), + str(self.dns.CODE / 'DNSMOS/model_v8.onnx'), threads=self.dns_threads) + + def _init_dns_scorer(self): + self._dns_local.model = self._new_dns_scorer() + + def _score_dns(self, wav): + scorer = self._dns_local.model if self.dns_pool is not None else self.models['dnsmos'] + return self.dns.score_wav(scorer, wav) + + def close(self): + if getattr(self, 'dns_pool', None) is not None: + self.dns_pool.shutdown(wait=True) + + def annotate(self, rows, wavs): + import numpy as np + torch = self.torch + result = [{'sha256_mp3': row['sha256_mp3'], 'scores': {}, 'embeddings': {}, 'teachers': {}, + 'schema': 'gemini-teacher-sidecar-v1', 'audio_tar': row['audio_tar'], + 'audio_member': row['audio_member'], 'annotated_utc': datetime.now(timezone.utc).isoformat(), + 'input': 'exact_selected_audio_sha256; mono16k; no teacher-specific source substitution'} for row in rows] + arrays = [{} for _ in rows] + with torch.inference_mode(): + for kind in sorted(self.names): + positions = [i for i, row in enumerate(rows) if kind in row['missing_teachers']] + if not positions: + continue + teacher_start = time.monotonic() + part = [wavs[i] for i in positions] + docs = [result[i] for i in positions] + if kind == 'orange': + for name, dim in [('timbre', 128), ('identity', 250)]: + emb = self.models[name].encode_waveforms(part, torch.device('cuda'), batch_size=2) + if emb.shape != (len(part), dim): + raise ValueError(f'Wrong Orange {name} dimensions: {emb.shape}') + for i, value in zip(positions, emb): + arrays[i][name] = np.asarray(value, dtype=np.float16) + for i in positions: + result[i]['teachers']['orange'] = { + 'timbre': 'Orange/Speaker-wavLM-tbr@b8d2608d56f18e2b6e27ba566cf69132e2c2ad6c', + 'identity': 'Orange/Speaker-wavLM-id@abbb3c7b8d220ceebc33d9bd3bb5aedb580342c7', + 'valid_single_speaker_target': rows[i].get('speaker_target_valid', False), + 'speaker_validity_source': rows[i].get('speaker_validity_source', 'unknown'), + } + elif kind == 'dnsmos': + values = (list(self.dns_pool.map(self._score_dns, part)) + if self.dns_pool is not None else [self._score_dns(wav) for wav in part]) + for i, (score, windows) in zip(positions, values): + result[i]['scores'].update({'dnsmos:' + n: float(v) for n, v in score.items()}) + result[i]['teachers']['dnsmos'] = {'model': 'Microsoft DNSMOS non-personalized + P808', + 'code': str(self.dns.CODE), 'valid_for_speech': rows[i].get('speech_target_valid', False), + 'window_count': windows, + 'short_audio_policy': 'repeat to 9.01 s; one-second window hop'} + elif kind == 'audiobox': + values = self.box.infer(self.models['audiobox'], [(str(i), w) for i, w in zip(positions, part)]) + for doc, value in zip(docs, values): + doc['scores'].update({'audiobox:' + n: float(v) for n, v in value.items()}) + doc['teachers']['audiobox'] = {'model': 'facebook/audiobox-aesthetics', 'revision': self.box.MODEL_REVISION} + elif kind == 'voiceclap': + predictor = self.models[kind] + tensor = torch.zeros(len(part), 480000, dtype=torch.float32, device='cuda') + for i, wav in enumerate(part): + tensor[i, :len(wav)] = torch.from_numpy(wav).to('cuda') + emb = torch.nn.functional.normalize(predictor.embedder.encode_waveform(tensor).float(), dim=-1) + if emb.shape != (len(part), 768): + raise ValueError('VoiceCLAP embedding dimension differs') + for dim in predictor.dims: + reg = predictor.reg[dim] + value = reg['net']((emb - reg['mu']) / reg['sd']).reshape(-1).cpu().numpy() + for doc, score in zip(docs, value): + doc['scores']['vn_' + dim.upper() + '_reg'] = float(score) + for key, (net, mean, std), upper in [('genuineness_0_6', self.gen, 6), ('blend_0_10', self.blend, 10)]: + values = net((emb - mean) / std).reshape(-1).clamp(0, upper).cpu().numpy() + for doc, value in zip(docs, values): + doc['scores'][key] = float(value) + attr_input = (emb - self.attr_mu) / self.attr_sd + for name, net in self.attr_heads.items(): + values = net(attr_input).reshape(-1).cpu().numpy() + for doc, value in zip(docs, values): + doc['scores']['voiceclap_attribute:' + name] = float(value) + for i, value in zip(positions, emb.cpu().numpy()): + arrays[i]['voiceclap'] = value.astype(np.float16) + result[i]['teachers']['voiceclap'] = {**self.voice.REVISIONS, 'speech_domain_valid': rows[i].get('speech_target_valid', False)} + elif kind == 'empathic': + features = self.models['eiv_feature'](part, sampling_rate=16000, return_tensors='pt').input_features.to('cuda') + with torch.autocast('cuda', torch.bfloat16): + hidden = self.models['eiv_encoder'](features).last_hidden_state.float() + pooled = torch.cat([hidden.mean(1), hidden.min(1).values, hidden.max(1).values, hidden.std(1)], 1) + for name, (net, quality) in self.models['eiv_experts'].items(): + values = net(pooled if quality else hidden).reshape(-1).cpu().numpy() + for doc, value in zip(docs, values): + doc['scores']['emo_' + name] = float(value) + doc['scores']['eiv_extra:' + name] = float(value) + for i, value in zip(positions, pooled.cpu().numpy()): + arrays[i]['bud_e_pooled'] = value.astype(np.float16) + result[i]['teachers']['empathic'] = {'model': 'laion/Empathic-Insight-Voice-Plus', + 'revision': self.eiv.EIV_SHA, 'encoder_revision': self.eiv.BUD_SHA, + 'speech_domain_valid': rows[i].get('speech_target_valid', False)} + self.timing_s[kind] = self.timing_s.get(kind, 0.0) + time.monotonic() - teacher_start + for doc, stored in zip(result, arrays): + for name, value in stored.items(): + if not np.isfinite(value).all(): + raise ValueError(f'Non-finite teacher embedding {name}') + doc['embeddings'][name] = {'member': doc['sha256_mp3'] + '.' + name + '.npy', 'dim': len(value), 'dtype': 'float16'} + if any(not np.isfinite(v) for v in doc['scores'].values()): + raise ValueError('Non-finite teacher score') + return result, arrays + + +def completed(root): + result = {} + for path in root.glob('*.tar'): + with tarfile.open(path, 'r:') as archive: + for member in archive: + if member.name.endswith('.teachers.json'): + doc = json.load(archive.extractfile(member)) + result.setdefault(doc['sha256_mp3'], set()).update(doc['teachers']) + return result + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--queue', type=Path, default=ROOT / 'prepared/teacher_backfill_queue.jsonl') + parser.add_argument('--limit', type=int, default=0) + parser.add_argument('--batch-size', type=int, default=4) + parser.add_argument('--shard-size', type=int, default=128) + parser.add_argument('--rank', type=int, default=int(os.environ.get('SLURM_PROCID', 0))) + parser.add_argument('--world', type=int, default=int(os.environ.get('SLURM_NTASKS', 1))) + args = parser.parse_args() + if args.world < 1 or not 0 <= args.rank < args.world: + parser.error('rank must be in [0, world)') + if args.batch_size < 1 or args.shard_size < 1: + parser.error('batch and shard sizes must be positive') + import numpy as np + import torch + if not torch.cuda.is_available(): + raise RuntimeError('Teacher backfill must run on an allocated GPU') + # Slurm assigns one visible GPU per task. + torch.set_num_threads(2) + output = ROOT / 'teacher_sidecars' + output.mkdir(exist_ok=True) + old = completed(output) + queue = list(jsonl(args.queue)) + if args.limit: + queue = queue[:args.limit] + queue.sort(key=lambda row: (row['audio_tar'], row['audio_member'])) + queue = [row for i, row in enumerate(queue) if i % args.world == args.rank] + for row in queue: + row['missing_teachers'] = [n for n in row['missing_teachers'] if n not in old.get(row['sha256_mp3'], set())] + queue = [row for row in queue if row['missing_teachers']] + job = os.environ.get('SLURM_JOB_ID', str(os.getpid())) + progress_path = ROOT / f'backfill-{job}-r{args.rank:02d}-status.json' + progress = {'job_id': job, 'rank': args.rank, 'world': args.world, + 'queue': str(args.queue), 'queue_clips_for_rank': len(queue), + 'already_cached_clips_global': len(old), + 'dnsmos_workers': int(os.environ.get('TEACHER_DNSMOS_WORKERS', '1')), + 'dnsmos_threads_per_worker': int(os.environ.get('TEACHER_DNSMOS_THREADS', '2')), + 'completed': 0, 'remaining': len(queue)} + def update(state, **extra): + progress.update(state=state, updated_utc=datetime.now(timezone.utc).isoformat(), **extra) + write_json(progress_path, progress) + if not queue: + update('COMPLETE') + write_json(ROOT / f'backfill-{job}-r{args.rank:02d}-complete.json', {'clips': 0, 'teachers': [], 'limit': args.limit}) + print('No missing targets for this rank', flush=True) + return + names = set(n for row in queue for n in row['missing_teachers']) + update('LOADING_TEACHERS', teachers=sorted(names)) + teachers = Teachers(names) + reader = TarReader() + started = time.monotonic() + update('ANNOTATING') + for start in range(0, len(queue), args.shard_size): + shard = queue[start:start + args.shard_size] + path = output / f'teachers-{job}-r{args.rank:02d}-b{start:07d}.tar' + temporary = path.with_suffix('.tar.incomplete') + with tarfile.open(temporary, 'w') as archive: + for batch_start in range(0, len(shard), args.batch_size): + part = shard[batch_start:batch_start + args.batch_size] + wavs = [reader.audio(row, verify=True) for row in part] + docs, arrays = teachers.annotate(part, wavs) + for doc, array in zip(docs, arrays): + for name, value in array.items(): + buffer = io.BytesIO() + np.save(buffer, value, allow_pickle=False) + add_member(archive, doc['embeddings'][name]['member'], buffer.getvalue()) + add_member(archive, doc['sha256_mp3'] + '.teachers.json', json.dumps(doc, ensure_ascii=False, allow_nan=False).encode()) + os.replace(temporary, path) + count = start + len(shard) + elapsed = time.monotonic() - started + update('ANNOTATING', completed=count, remaining=len(queue) - count, + inference_elapsed_s=round(elapsed, 3), + clips_per_second=round(count / max(elapsed, .001), 4), + teacher_elapsed_s={n: round(v, 3) for n, v in teachers.timing_s.items()}, + last_committed_sidecar=str(path)) + print(json.dumps({'rank': args.rank, 'completed': start + len(shard), 'remaining': len(queue) - start - len(shard), 'sidecar': str(path)}), flush=True) + reader.close() + teachers.close() + write_json(ROOT / f'backfill-{job}-r{args.rank:02d}-complete.json', {'clips': len(queue), 'teachers': sorted(names), 'limit': args.limit}) + update('COMPLETE') + + +if __name__ == '__main__': + main() diff --git a/training/gemini_finetune/common.py b/training/gemini_finetune/common.py new file mode 100644 index 0000000000000000000000000000000000000000..c1af7849675380fbde4b7ad429917aa84dd34c20 --- /dev/null +++ b/training/gemini_finetune/common.py @@ -0,0 +1,110 @@ +"""Shared paths, atomic outputs and random access to audio in WebDataset TARs.""" +from __future__ import annotations + +import hashlib +import io +import json +import math +import os +import tarfile +from collections import OrderedDict +from pathlib import Path + +HERE = Path(__file__).resolve().parent +CODE = HERE.parent +HOME = CODE.parent +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005') +SELECTION = ROOT.parent / 'gemini_multisource_100h_20261004' +GEMINI = SELECTION / 'annotation_batch_20261005' +RELEASE = ROOT.parent / 'hf_layered_release/model' +MOSS_UPSTREAM = ROOT / 'upstream/MOSS-Audio' +MOSS_REVISION = 'd4dc5a6d8cd79b43dcd82884c75e303b1ecd016d' +UPSTREAM_REVISION = '66326e6e0db34f036c86a76ba005efa4830c69dd' +TEACHER_NAMES = ('orange', 'dnsmos', 'audiobox', 'voiceclap', 'empathic') +EMOTION_ALIASES = { + 'Hope_Enthusiasm_Optimism': 'hope_optimism', + 'Impatience_and_Irritability': 'impatience_irritability', + 'Intoxication_Altered_States_of_Consciousness': 'intoxication_altered_states', + 'Jealousy_and_Envy': 'jealousy_envy', 'Jealousy_&_Envy': 'jealousy_envy', + 'Thankfulness_Gratitude': 'gratitude', +} + + +def emotion_key(name): + return EMOTION_ALIASES.get(name, name.lower()) + + +def read_json(path): + return json.loads(Path(path).read_text()) + + +def write_json(path, obj): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(path.name + '.incomplete') + temporary.write_text(json.dumps(obj, ensure_ascii=False, indent=2, allow_nan=False) + '\n') + os.replace(temporary, path) + + +def jsonl(path): + with Path(path).open() as stream: + for line in stream: + if line.strip(): + yield json.loads(line) + + +def put_jsonl(stream, row): + stream.write(json.dumps(row, ensure_ascii=False, separators=(',', ':'), allow_nan=False) + '\n') + + +def finite(value): + return type(value) in (int, float) and math.isfinite(value) + + +def add_member(archive, name, payload): + member = tarfile.TarInfo(name) + member.size, member.mtime = len(payload), 0 + archive.addfile(member, io.BytesIO(payload)) + + +class TarReader: + """Small per-worker TAR cache; no extracted audio files or network calls.""" + def __init__(self, maximum=12): + self.maximum = maximum + self.archives = OrderedDict() + + def read(self, path, member): + path = str(path) + if path not in self.archives: + if len(self.archives) >= self.maximum: + _, old = self.archives.popitem(last=False) + old.close() + self.archives[path] = tarfile.open(path, 'r:') + self.archives.move_to_end(path) + handle = self.archives[path].extractfile(member) + if handle is None: + raise ValueError(f'Not a regular TAR member: {member}') + return handle.read() + + def audio(self, row, verify=False): + import numpy as np + import soundfile as sf + from scipy.signal import resample_poly + raw = self.read(row['audio_tar'], row['audio_member']) + if verify and hashlib.sha256(raw).hexdigest() != row['sha256_mp3']: + raise ValueError('Audio hash differs from selected, annotated audio') + wav, sr = sf.read(io.BytesIO(raw), dtype='float32', always_2d=True) + wav = wav.mean(axis=1) + if sr != 16000: + divisor = math.gcd(sr, 16000) + wav = resample_poly(wav, 16000 // divisor, sr // divisor).astype(np.float32) + if not len(wav) or not np.isfinite(wav).all(): + raise ValueError('Empty or non-finite audio') + if len(wav) > 480000 + 1600: + raise ValueError('Selected audio exceeds the 30-second training contract') + return wav[:480000] + + def close(self): + for archive in self.archives.values(): + archive.close() + self.archives.clear() diff --git a/training/gemini_finetune/configure.py b/training/gemini_finetune/configure.py new file mode 100644 index 0000000000000000000000000000000000000000..eaa37bf796862bbf37df7ef64ea08b33a512707e --- /dev/null +++ b/training/gemini_finetune/configure.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +"""Pin the best validation checkpoints and create independent training configs.""" +import json +from common import (HERE, MOSS_REVISION, RELEASE, ROOT, UPSTREAM_REVISION, read_json, write_json) + + +def main(): + root = ROOT / 'prepared' + for model in ('base', 'small'): + candidates = [read_json(p) for p in (RELEASE / model).glob('S*/selection.json')] + selected = min(candidates, key=lambda row: row['selection_loss']) + stage = selected['stage'] + cfg = {'model': model, 'stage': stage, 'selection': selected, + 'checkpoint': str(RELEASE / model / stage / 'checkpoint.pt'), + 'checkpoint_sha256': selected['checkpoint_sha256'], + 'model_config': str(RELEASE / model), 'data_root': str(root), + 'output': str(ROOT / 'training' / ('whisper_' + model)), + 'epochs': 2, 'encoder_lr': 1e-5 if model == 'base' else 5e-6, + 'head_lr': 1e-4, 'minimum_lr_ratio': .10, 'warmup_ratio': .05, + 'batch_per_gpu': 32 if model == 'base' else 16, 'accumulation': 1 if model == 'base' else 2, + 'workers': 4, 'weight_decay': .01, 'seed': 20261005, + 'normalization': 'checkpoint_normalization.json', + 'optimizer_policy': 'fresh fine-tune AdamW/cosine; best curriculum weights restored strictly', + 'loss_policy': 'existing masked Huber192+CPS, Orange cosine+Huber, frames BCE+Dice, onset BCE, duration Huber, event CE', + 'save_policy': 'best and latest epoch checkpoint, optimizer/scheduler and per-rank RNG; epoch-boundary resume'} + write_json(HERE / 'configs' / f'whisper_{model}.json', cfg) + for task in ('caption', 'director_caption', 'burst_caption'): + cfg = {'task': task, 'model_id': 'OpenMOSS-Team/MOSS-Audio-8B-Instruct', + 'model_revision': MOSS_REVISION, 'upstream_revision': UPSTREAM_REVISION, + 'model_dir': str(ROOT / 'models/MOSS-Audio-8B-Instruct'), 'data_root': str(root), + 'output': str(ROOT / 'training' / ('moss_' + task)), + 'epochs': 2, 'learning_rate': 1e-4, 'warmup_ratio': .05, + 'weight_decay': .01, 'lora_rank': 32, 'lora_alpha': 64, 'lora_dropout': .05, + 'microbatch_per_gpu': 1, 'accumulation': 16, 'workers': 4, + 'maximum_tokens': 8192, 'attention_backend': 'sdpa', + 'audio_time_markers': task == 'burst_caption', + 'gradient_checkpointing': False, 'seed': 20261005, + 'frozen': ['audio_encoder', 'audio_adapter', 'deepstack_audio_merger_list', 'base_language_model'], + 'loss': 'assistant caption text only; system/user/audio labels -100; no thinking trace', + 'burst_input': 'full original audio + start and end in seconds; no event label or reference caption in input', + 'save_policy': 'best adapter plus full Trainer resume states at each epoch'} + write_json(HERE / 'configs' / f'moss_{task}.json', cfg) + print(json.dumps({'configs': str(HERE / 'configs'), 'whisper_checkpoints': ['Base S4', 'Small S3'], + 'moss_tasks': ['caption', 'director_caption', 'burst_caption'], 'training_started': False})) + + +if __name__ == '__main__': + main() diff --git a/training/gemini_finetune/data.py b/training/gemini_finetune/data.py new file mode 100644 index 0000000000000000000000000000000000000000..5284537d9d8752ca91ec88aaa9045c423be35c81 --- /dev/null +++ b/training/gemini_finetune/data.py @@ -0,0 +1,108 @@ +"""TAR-backed dataset for the existing encoder-only Whisper multitask architecture.""" +from __future__ import annotations + +import io +import math +import sys +from pathlib import Path + +import numpy as np +import torch +from torch.utils.data import Dataset + +from common import CODE, TarReader, jsonl, read_json +sys.path.insert(0, str(CODE)) +from full_ladder_dataset import MixedCollator + + +class GeminiDataset(Dataset): + def __init__(self, root, split, require_complete=True): + self.root = Path(root) + if require_complete and not (self.root / 'TRAINING_READY.json').exists(): + raise RuntimeError('Training gate is not satisfied; inspect coverage.json') + self.norm = read_json(self.root / 'checkpoint_normalization.json') + self.rows = [row for row in jsonl(self.root / 'targets.jsonl') if row['split'] == split and row['ready_for_whisper_training']] + self.reader = None + self.mean = np.asarray(self.norm['score_mean'], np.float32) + self.std = np.asarray(self.norm['score_std'], np.float32) + + def __len__(self): + return len(self.rows) + + def __getstate__(self): + return {**self.__dict__, 'reader': None} + + def __getitem__(self, index): + if self.reader is None: + self.reader = TarReader() + row = self.rows[index] + wav = self.reader.audio(row) + n_frames = min(1500, math.ceil(len(wav) / 320)) + raw = np.asarray([row['raw_scores'].get(n, np.nan) for n in self.norm['score_names']], np.float32) + valid = np.isfinite(raw) + teachers = row.get('teacher_metadata', {}) + if not teachers.get('dnsmos', {}).get('valid_for_speech', False): + valid[123:130] = False + if not teachers.get('empathic', {}).get('speech_domain_valid', False): + valid[100:119] = False + if not teachers.get('voiceclap', {}).get('speech_domain_valid', False): + for i in range(131, 192): + # Valid Gemini emotion annotations remain usable even where + # the older speech-only attribute teacher is outside its domain. + if row['score_provenance'].get(self.norm['score_names'][i]) != 'gemini-3.8-flash': + valid[i] = False + scores = np.where(valid, (raw - self.mean) / self.std, 0).astype(np.float32) + frame = np.zeros(n_frames, np.float32) + spans, classes = [], [] + for event in row['vocal_bursts']: + start = max(0, min(n_frames - 1, int(event['start'] / .02))) + end = min(n_frames, max(start + 1, math.ceil(event['end'] / .02))) + frame[start:end] = 1 + spans.append((start, end)) + classes.append(event['checkpoint_class_id']) + result = {'key': row['sha256_mp3'], 'uid': row['sha256_mp3'], 'domain': 'gemini', + 'audio': wav, 'scores': scores, 'score_mask': valid, + 'frame': frame, 'frame_mask': np.full(n_frames, row['burst_frames_valid'], np.float32), + 'events_frames': spans, 'event_class_ids': classes, + 'cps': row['characters_per_second'], 'enabled': True} + speaker_valid = bool(teachers.get('orange', {}).get('valid_single_speaker_target')) + for name, dim in [('timbre', 128), ('identity', 250)]: + reference = row['embeddings'].get(name) + result[name] = np.zeros(dim, np.float32) + if reference: + value = np.load(io.BytesIO(self.reader.read(reference['tar'], reference['member'])), allow_pickle=False) + if value.shape != (dim,) or not np.isfinite(value).all(): + raise ValueError(f'Invalid {name} target for {row["sha256_mp3"]}') + result[name] = value.astype(np.float32) + result[name + '_valid'] = bool(reference and speaker_valid) + return result + + +class GeminiCollator: + def __init__(self, model_path, normalization): + self.base = MixedCollator(Path(model_path)) + self.cps_mean = normalization['cps_mean'] + self.cps_std = normalization['cps_std'] + + def __call__(self, items): + batch = self.base(items) + n_frames = batch['frame'].shape[1] + width = max(1, max(len(row['events_frames']) for row in items)) + starts = np.zeros((len(items), width), np.int64) + ends = np.zeros_like(starts) + classes = np.zeros_like(starts) + event_valid = np.zeros_like(starts, np.bool_) + cps = np.zeros(len(items), np.float32) + cps_valid = np.zeros(len(items), np.bool_) + for i, row in enumerate(items): + for j, ((start, end), class_id) in enumerate(zip(row['events_frames'], row['event_class_ids'])): + starts[i, j], ends[i, j] = min(start, n_frames - 1), min(end, n_frames) + classes[i, j] = class_id + event_valid[i, j] = ends[i, j] > starts[i, j] + if row['cps'] is not None and math.isfinite(row['cps']): + cps[i], cps_valid[i] = row['cps'], True + batch.update(event_starts=torch.from_numpy(starts), event_ends=torch.from_numpy(ends), + event_classes=torch.from_numpy(classes), event_valid=torch.from_numpy(event_valid), + event_span_valid=torch.from_numpy(event_valid.copy()), cps_raw=torch.from_numpy(cps), + cps=torch.from_numpy((cps - self.cps_mean) / self.cps_std), cps_valid=torch.from_numpy(cps_valid)) + return batch diff --git a/training/gemini_finetune/prepare.py b/training/gemini_finetune/prepare.py new file mode 100644 index 0000000000000000000000000000000000000000..c181cb60ac01b77b35162b3f912fa15cf7bfbfeb --- /dev/null +++ b/training/gemini_finetune/prepare.py @@ -0,0 +1,365 @@ +#!/usr/bin/env python3 +"""Incremental Gemini target parser, coverage audit, backfill queue and SFT export. + +No provider calls, no teacher inference, no audio extraction. Outputs are snapshots; +the final training gate requires complete source/Gemini manifests and all sidecars. +""" +from __future__ import annotations + +import argparse +import fcntl +import hashlib +import json +import math +import os +import re +import sqlite3 +import sys +from collections import Counter, defaultdict +from datetime import datetime, timezone +from pathlib import Path + +from common import (CODE, GEMINI, HERE, RELEASE, ROOT, SELECTION, TEACHER_NAMES, + TarReader, emotion_key, finite, jsonl, put_jsonl, read_json, write_json) +sys.path.insert(0, str(CODE / 'gemini_s10_smoke')) +from annotate import PROMPT, contract, eligible_burst_taxonomy, validate + +TASK_PROMPTS = { + 'caption': 'Describe the audible voice, delivery, emotions, vocal bursts and recording environment in English. Ground the description in this audio.', + 'director_caption': 'Write English voice-director instructions that reproduce the audible performance, including delivery, emotion and vocal bursts. Ground the instructions in this audio.', + 'burst_caption': 'Describe only the vocal burst audible from {start:.6f} to {end:.6f} seconds in the full recording. Times are relative to the beginning of the supplied audio. Use surrounding audio as context. Write a detailed English acoustic and expressive description of this event.', +} + + +def choose_annotations(): + expected = contract(PROMPT.read_text()) + best = {} + attempts = 0 + for path in sorted((GEMINI / 'sidecars').glob('*.jsonl'), key=lambda x: (x.stat().st_mtime_ns, x.name)): + for row in jsonl(path): + attempts += 1 + annotation = row.get('annotation') + errors = validate(annotation, expected, row['duration_s']) if isinstance(annotation, dict) else ['no JSON annotation'] + feedback = row.get('prompt_feedback') or {} + usable = not errors and not row.get('request_error') and not feedback.get('blockReason') + rank = (int(usable), -len(errors)) + old = best.get(row['sha256_mp3']) + if old is None or rank >= old[0]: + best[row['sha256_mp3']] = (rank, {**row, 'parse_validation_errors': errors, 'parser_usable': bool(usable)}) + return {key: row for key, (_, row) in best.items()}, attempts + + +def source_cache(selections, output): + """Read each metadata TAR once, persist original targets in a compact SQLite file.""" + database = sqlite3.connect(output / 'source_metadata.sqlite') + database.execute('CREATE TABLE IF NOT EXISTS metadata (sha TEXT PRIMARY KEY, doc TEXT NOT NULL)') + known = {row[0] for row in database.execute('SELECT sha FROM metadata')} + by_tar = defaultdict(dict) + for row in selections: + if row['sha256_mp3'] in known: + continue + for membership in row['source_memberships']: + if membership.get('metadata_tar') and membership.get('metadata_member'): + by_tar[membership['metadata_tar']].setdefault(membership['metadata_member'], []).append(row['sha256_mp3']) + break + import tarfile + for path, wanted in by_tar.items(): + with tarfile.open(path, 'r:') as archive: + for member in archive: + if member.name in wanted: + document = json.load(archive.extractfile(member)) + for sha in wanted[member.name]: + database.execute('INSERT OR REPLACE INTO metadata VALUES (?, ?)', (sha, json.dumps(document, ensure_ascii=False))) + database.commit() + return database + + +def split_group(selection, metadata): + # Explicit speaker identities take priority; related synthetic voice variants + # share their original family, and exact duplicates always share one SHA. + speaker = metadata.get('speaker_id') or metadata.get('speaker') or metadata.get('voice') + if isinstance(speaker, str) and speaker: + return 'speaker:' + str(metadata.get('dataset', selection['source_memberships'][0]['source'])) + ':' + speaker + uid = metadata.get('uid') or selection['source_memberships'][0].get('uid', '') + if isinstance(uid, str) and '__' in uid: + return 'audio-family:' + uid.split('__', 1)[0] + return 'audio-sha256:' + selection['sha256_mp3'] + + +def assign_split(group): + fraction = int(hashlib.sha256(('gemini-finetune-v1:' + group).encode()).hexdigest()[:12], 16) / 16**12 + return 'test' if fraction < .05 else 'validation' if fraction < .10 else 'train' + + +def backfill_index(): + import tarfile + rows = {} + for path in sorted((ROOT / 'teacher_sidecars').glob('*.tar')): + with tarfile.open(path, 'r:') as archive: + for member in archive: + if member.name.endswith('.teachers.json'): + doc = json.load(archive.extractfile(member)) + # Only same-audio SHA-keyed sidecars are accepted. Array refs + # are bound to this immutable archive, never matched by UID. + sha = doc['sha256_mp3'] + old = rows.setdefault(sha, {'scores': {}, 'embeddings': {}, 'teachers': {}}) + old['scores'].update(doc.get('scores', {})) + old['teachers'].update(doc.get('teachers', {})) + for key, reference in doc.get('embeddings', {}).items(): + old['embeddings'][key] = {**reference, 'tar': str(path)} + return rows + + +def conversation(selection, task, prompt, target, split, event=None): + result = { + 'schema': 'moss-caption-sft-v1', 'sha256_mp3': selection['sha256_mp3'], + 'split': split, 'task': task, 'duration_s': selection['duration_s'], + 'audio_tar': selection['audio_tar'], 'audio_member': selection['audio_member'], + 'full_audio': True, + 'conversation': [ + {'role': 'user', 'message_type': 'audio', 'content': {'tar': selection['audio_tar'], 'member': selection['audio_member']}}, + {'role': 'user', 'message_type': 'text', 'content': prompt}, + {'role': 'assistant', 'message_type': 'text', 'content': target.strip()}, + ], + } + if event: + result['event_start_s'], result['event_end_s'] = event['start'], event['end'] + result['event_index'] = event['i'] + return result + + +def prepare(args): + annotation_finished = (GEMINI / 'ANNOTATION_FINISHED.json').exists() + selected_path = SELECTION / 'combined_unique_manifest.jsonl' + if not selected_path.exists(): + selected_path = GEMINI / 'latest_selection_manifest.jsonl' + if not selected_path.exists(): + selected_path = SELECTION / 'available_unique_manifest.jsonl' + if args.manifest: + selected_path = args.manifest + annotations, attempts = choose_annotations() + selections = list(jsonl(selected_path)) + if args.limit: + selections = [row for row in selections if annotations.get(row['sha256_mp3'], {}).get('parser_usable')][:args.limit] + metadata = source_cache(selections, args.out) + teachers = backfill_index() + normalization = read_json(RELEASE / 'training_normalization.json') + names = normalization['score_names'] + assert len(names) == 192 + _, vn_keys, emo_keys = contract(PROMPT.read_text()) + assert {emotion_key(n[4:]) for n in names[:40]} == emo_keys + assert {n[3:-4].lower() for n in names[40:97]} == vn_keys + classes = read_json(RELEASE / 'classes.json') + class_ids = {name: index for index, name in enumerate(classes['names'])} + taxonomy = eligible_burst_taxonomy() + class_mapping = {name: class_ids.get(classes['aliases'].get(name, name), 0) for name in taxonomy} + write_json(args.out / 'gemini_burst_mapping.json', {'fine_to_group23': taxonomy, 'fine_to_checkpoint_class': class_mapping, + 'checkpoint_classes': classes['names'], 'unrepresented_fine_classes': [n for n, index in class_mapping.items() if not index]}) + handles = {} + filenames = ['targets.jsonl', 'teacher_backfill_queue.jsonl', 'annotations_review.jsonl'] + [f'moss_{task}_{split}.jsonl' for task in TASK_PROMPTS for split in ('train', 'validation', 'test')] + for name in filenames: + handles[name] = (args.out / (name + '.incomplete')).open('w') + counts, coverage, missing, task_counts, split_counts = Counter(), Counter(), Counter(), Counter(), Counter() + moments = {name: [0, 0., 0.] for name in names} + try: + for selection in selections: + sha = selection['sha256_mp3'] + found = metadata.execute('SELECT doc FROM metadata WHERE sha=?', (sha,)).fetchone() + original = json.loads(found[0]) if found else {} + gemini = annotations.get(sha) + valid = bool(gemini and gemini['parser_usable']) + answer = gemini['annotation'] if valid else {} + computed = teachers.get(sha, {}) + scores = {name: value for name, value in (original.get('scores') or {}).items() if name in names and finite(value)} + provenance = {name: 'original_source_metadata' for name in scores} + for name, value in computed.get('scores', {}).items(): + if name in names and finite(value): + scores[name], provenance[name] = value, 'teacher_sidecar' + if valid: + for name in names[:40]: + scores[name], provenance[name] = answer['emonet'][emotion_key(name[4:])], 'gemini-3.8-flash' + for name in names[40:97]: + scores[name], provenance[name] = answer['voicenet'][name[3:-4].lower()], 'gemini-3.8-flash' + scores['genuineness_0_6'] = answer['genuineness'] + provenance['genuineness_0_6'] = 'gemini-3.8-flash' + if answer['vocal_burst_blend'] is not None: + scores['blend_0_10'] = answer['vocal_burst_blend'] + provenance['blend_0_10'] = 'gemini-3.8-flash' + else: + scores.pop('blend_0_10', None) + provenance.pop('blend_0_10', None) + for name in names[131:]: + key = emotion_key(name.split(':', 1)[1]) + if key in answer['emonet']: + scores[name], provenance[name] = answer['emonet'][key], 'gemini-3.8-flash' + events = [] + if valid: + # Provider allows +0.3 s end tolerance; retain raw timestamps and + # clamp training spans at actual audio end, recording the clamp. + for event in answer['vocal_bursts']: + if event['start'] >= selection['duration_s']: + continue + events.append({**event, 'end': min(event['end'], selection['duration_s']), + 'raw_end': event['end'], 'checkpoint_class_id': class_mapping[event['fine']], + 'timestamp_clamped': event['end'] > selection['duration_s']}) + scores['burst_count_log1p'] = math.log1p(len(events)) + provenance['burst_count_log1p'] = 'gemini-3.8-flash' + group = split_group(selection, original) + split = assign_split(group) + split_counts[split] += 1 + counts['selected'] += 1 + counts['gemini_valid'] += int(valid) + counts['gemini_pending_or_invalid'] += int(not valid) + embeddings = computed.get('embeddings', {}) + required = [] + # Originals keep their scalars. Backfill the additional objectives + # from the exact selected waveform, not an upstream speaker UID. + checks = { + 'orange': all(embeddings.get(n, {}).get('dim') == d for n, d in [('timbre', 128), ('identity', 250)]), + 'dnsmos': all(n in scores for n in names[123:130]), + 'audiobox': all(n in scores for n in names[119:123]), + 'voiceclap': all(n in scores for n in names[131:]) and embeddings.get('voiceclap', {}).get('dim') == 768, + 'empathic': all(n in scores for n in names[100:119]) and embeddings.get('bud_e_pooled', {}).get('dim') == 3072, + } + for teacher, complete in checks.items(): + # All selected audio receives independent teacher annotations, + # even if its Flash response is pending or ultimately excluded. + if not complete: + required.append(teacher) + missing[teacher] += 1 + transcript = answer.get('transcript', '') if valid else original.get('source_transcript', '') + speech_valid = bool(re.sub(r'\[[^\]]*\]', '', transcript).strip()) if isinstance(transcript, str) else False + # Teacher inference can finish before Flash. Recompute the loss-domain + # masks from the latest annotation, rather than freezing an unknown + # pending transcript as a permanent non-speech/speaker exclusion. + explicit_speaker = original.get('speaker_id') or original.get('speaker') or original.get('voice') + source_types = {m['source'] for m in selection['source_memberships']} + one_explicit_speaker = isinstance(explicit_speaker, (str, int)) and bool(explicit_speaker) + single_speaker = bool(one_explicit_speaker or 'talent' in source_types or + ('S1_100h' in source_types and '/synth' in str(original.get('language_source', '')))) + if original.get('speaker_count') is not None: + single_speaker = original['speaker_count'] == 1 + validity_source = 'explicit_metadata_or_single_voice_synthetic_source' if single_speaker else 'unknown_masked' + teacher_metadata = {name: dict(doc) for name, doc in computed.get('teachers', {}).items()} + for family, field, value in ( + ('orange', 'valid_single_speaker_target', single_speaker and speech_valid), + ('dnsmos', 'valid_for_speech', speech_valid), + ('voiceclap', 'speech_domain_valid', speech_valid), + ('empathic', 'speech_domain_valid', speech_valid), + ): + if family in teacher_metadata: + doc = teacher_metadata[family] + doc['validity_at_teacher_annotation'] = doc.get(field) + doc[field] = value + doc['current_validity_source'] = 'latest_valid_flash_transcript_and_source_metadata' if valid else 'original_source_metadata' + if 'orange' in teacher_metadata: + teacher_metadata['orange']['speaker_validity_source'] = validity_source + if required: + put_jsonl(handles['teacher_backfill_queue.jsonl'], {**selection, 'split': split, 'missing_teachers': required, + 'gemini_language': answer.get('language'), 'gemini_transcript': answer.get('transcript'), + 'gemini_valid': valid, 'speech_target_valid': speech_valid, + 'speaker_target_valid': single_speaker and speech_valid, + 'speaker_validity_source': validity_source}) + cps = None + if valid and isinstance(answer.get('transcript'), str): + # Literal Unicode character count, including spaces and punctuation; + # [unclear] placeholders are removed, denominator is full clip length. + text = re.sub(r'\[[^\]]*\]', '', answer['transcript']).strip() + cps = len(text) / selection['duration_s'] + if cps is None and finite(original.get('source_characters_per_second')): + cps = original['source_characters_per_second'] + row = {**selection, 'schema': 'gemini-whisper-targets-v1', 'split': split, 'split_group': group, + 'gemini_usable': valid, 'annotation': answer or None, + 'annotation_provenance': {key: gemini.get(key) for key in ('annotation_model', 'prompt_sha256', 'batch_tag', 'batch_name')} if gemini else None, + 'raw_scores': scores, 'score_provenance': provenance, + 'vocal_bursts': events, 'burst_frames_valid': valid, + 'characters_per_second': cps, + 'cps_source': 'gemini_transcript' if valid else 'original_source_metadata' if cps is not None else None, + 'embeddings': embeddings, 'missing_teachers': required, + 'teacher_metadata': teacher_metadata, + 'speaker_target_policy': 'same_audio_teacher_vector; multi_speaker_or_non_speech_is_masked', + 'original_annotation': original, + 'ready_for_whisper_training': valid and not required, + } + put_jsonl(handles['targets.jsonl'], row) + counts['ready_for_whisper_training'] += int(row['ready_for_whisper_training']) + for name, value in scores.items(): + coverage[name] += 1 + if split == 'train' and valid: + moment = moments[name] + moment[0] += 1 + delta = value - moment[1] + moment[1] += delta / moment[0] + moment[2] += delta * (value - moment[1]) + if gemini and not valid: + put_jsonl(handles['annotations_review.jsonl'], {'sha256_mp3': sha, 'errors': gemini['parse_validation_errors'], + 'batch_tag': gemini.get('batch_tag'), 'prompt_feedback': gemini.get('prompt_feedback')}) + if valid: + for task in ('caption', 'director_caption'): + if not answer[task].strip(): + continue + example = conversation(selection, task, TASK_PROMPTS[task], answer[task], split) + put_jsonl(handles[f'moss_{task}_{split}.jsonl'], example) + task_counts[f'{task}:{split}'] += 1 + for event in events: + example = conversation(selection, 'burst_caption', TASK_PROMPTS['burst_caption'].format(**event), event['caption'], split, event) + put_jsonl(handles[f'moss_burst_caption_{split}.jsonl'], example) + task_counts[f'burst_caption:{split}'] += 1 + finally: + metadata.close() + for handle in handles.values(): + handle.close() + for name in filenames: + os.replace(args.out / (name + '.incomplete'), args.out / name) + write_json(args.out / 'checkpoint_normalization.json', normalization) + write_json(args.out / 'gemini_train_statistics.json', { + 'basis': 'current training partition only; provisional until final source/annotation completion', + 'score_names': names, 'count': [moments[n][0] for n in names], + 'mean': [moments[n][1] for n in names], + 'std': [max(1e-4, math.sqrt(moments[n][2] / max(1, moments[n][0]))) for n in names], + 'default_training_normalization': 'checkpoint_normalization.json; preserves existing output units; emotions remain 0..4', + }) + complete = (GEMINI / 'ANNOTATION_FINISHED.json').exists() and (SELECTION / 'COMBINED_READY.json').exists() and not args.limit + ready = complete and counts['gemini_valid'] > 0 and not missing + summary = {'schema': 'gemini-finetune-preparation-v1', 'updated_utc': datetime.now(timezone.utc).isoformat(), + 'selected_manifest': str(selected_path), 'snapshot_only': not complete, + 'gemini_annotations_finished': (GEMINI / 'ANNOTATION_FINISHED.json').exists(), + 'counts': dict(counts), 'splits': dict(split_counts), 'teacher_backfill_missing': dict(missing), + 'valid_score_counts': dict(coverage), 'moss_examples': dict(task_counts), + 'annotation_attempts_seen': attempts, 'ready_to_train': ready, + 'training_gate': 'Final source and Gemini manifests, complete teacher backfill for all selected clips. Invalid/blocked Flash responses are quarantined and excluded from tuning, with their IDs and reasons retained.', + 'gemini_priority': 'Strictly schema-valid Flash annotation supersedes matching prior scores and event spans; original labels retained for audit.', + 'output': str(args.out)} + write_json(args.out / 'coverage.json', summary) + write_json(args.out / 'excluded_annotations.json', { + 'policy': 'schema-invalid, provider-blocked, or missing final responses are excluded from new tuning and all caption tasks', + 'final': complete, 'clips': counts['gemini_pending_or_invalid']}) + caption_marker = args.out / 'CAPTION_DATA_READY.json' + if complete and counts['gemini_valid'] > 0: + write_json(caption_marker, {'caption_examples': dict(task_counts), 'unique_valid_clips': counts['gemini_valid'], + 'excluded_invalid_clips': counts['gemini_pending_or_invalid'], 'source_manifest': str(selected_path)}) + elif caption_marker.exists(): + caption_marker.unlink() + # Invalid responses remain in the review ledger and never become targets. + marker = args.out / 'TRAINING_READY.json' + if ready: + write_json(marker, summary) + elif marker.exists(): + marker.unlink() + print(json.dumps({key: summary[key] for key in ('counts', 'teacher_backfill_missing', 'moss_examples', 'ready_to_train', 'output')}, indent=2)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--out', type=Path, default=ROOT / 'prepared') + parser.add_argument('--limit', type=int, default=0, help='small real-data parser canary; never marks training ready') + parser.add_argument('--manifest', type=Path, help='explicit existing selected manifest for a small canary') + args = parser.parse_args() + args.out.mkdir(parents=True, exist_ok=True) + with (args.out / '.prepare.lock').open('a') as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + prepare(args) + + +if __name__ == '__main__': + main() diff --git a/training/gemini_finetune/train_whisper.py b/training/gemini_finetune/train_whisper.py new file mode 100644 index 0000000000000000000000000000000000000000..1552e7d1a6f1e081e923db750093068d15108628 --- /dev/null +++ b/training/gemini_finetune/train_whisper.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +"""Fine-tune the best Base/Small checkpoint on completed Flash-priority targets.""" +from __future__ import annotations + +import argparse +import json +import math +import os +import random +import sys +from pathlib import Path + +import numpy as np +import torch +from torch.nn.parallel import DistributedDataParallel +from torch.utils.data import DataLoader, DistributedSampler + +from common import CODE, ROOT, jsonl, read_json, write_json +from data import GeminiCollator, GeminiDataset +sys.path.insert(0, str(CODE)) +from layered_multitask_model import LayeredMultiTaskWhisper +from train_layered_curriculum import gpu_batch, loss_terms + + +@torch.no_grad() +def evaluate(core, loader, device, weights): + core.eval() + total = torch.zeros(2, device=device) + for item in loader: + batch = gpu_batch(item, device) + with torch.autocast('cuda', torch.bfloat16): + output = core(batch['mel'], batch['mel_mask'], batch['event_starts'], batch['event_ends']) + loss = sum(loss_terms(output, batch, weights).values()) + size = len(batch['mel']) + total += torch.tensor([float(loss) * size, size], device=device) + return float(total[0] / total[1].clamp_min(1)) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--config', type=Path, required=True) + parser.add_argument('--resume', type=Path) + args = parser.parse_args() + cfg = read_json(args.config) + seed = cfg['seed'] + torch.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + world = int(os.environ.get('WORLD_SIZE', 1)) + rank = int(os.environ.get('RANK', 0)) + local = int(os.environ.get('LOCAL_RANK', 0)) + torch.cuda.set_device(local) + if world > 1: + torch.distributed.init_process_group('nccl') + device = torch.device('cuda', local) + root = Path(cfg['data_root']) + train = GeminiDataset(root, 'train') + validation = GeminiDataset(root, 'validation') + if not train or not validation: + raise RuntimeError('Nonempty train and validation partitions are required') + taxonomy = read_json(root / 'gemini_burst_mapping.json') + n_classes = len(taxonomy['checkpoint_classes']) + # Fine-tuning optimizer/schedule start fresh; curriculum model weights are + # restored strictly, without resetting heads or changing output semantics. + checkpoint = torch.load(cfg['checkpoint'], map_location='cpu', weights_only=False) + core = LayeredMultiTaskWhisper(cfg['model_config'], n_classes, initialize_pretrained=False) + core.load_state_dict(checkpoint['model'], strict=True) + del checkpoint + core.to(device) + # HF initializes the sinusoidal position table with requires_grad=False. + # The requested full fine-tune explicitly unfreezes the entire encoder. + core.requires_grad_(True) + if not all(p.requires_grad for p in core.encoder.parameters()): + raise RuntimeError('Full fine-tuning requires every encoder parameter to be trainable') + params = [ + {'params': list(core.encoder.parameters()), 'lr': cfg['encoder_lr'], 'peak_lr': cfg['encoder_lr']}, + {'params': [p for name, p in core.named_parameters() if not name.startswith('encoder.')], 'lr': cfg['head_lr'], 'peak_lr': cfg['head_lr']}, + ] + optimizer = torch.optim.AdamW(params, weight_decay=cfg['weight_decay']) + model = DistributedDataParallel(core, device_ids=[local], find_unused_parameters=False) if world > 1 else core + sampler = DistributedSampler(train, num_replicas=world, rank=rank, shuffle=True, seed=seed) if world > 1 else None + collator = GeminiCollator(cfg['model_config'], train.norm) + options = {'num_workers': cfg['workers'], 'pin_memory': True, 'collate_fn': collator} + if cfg['workers']: + options.update(persistent_workers=True, multiprocessing_context='spawn') + loader = DataLoader(train, batch_size=cfg['batch_per_gpu'], sampler=sampler, shuffle=sampler is None, **options) + # Rank zero evaluates the complete validation partition. Other ranks wait; + # this avoids DistributedSampler padding/duplicate validation rows. + val_loader = DataLoader(validation, batch_size=cfg['batch_per_gpu'], shuffle=False, **options) if rank == 0 else None + event_counts = np.zeros(n_classes, np.float64) + for row in train.rows: + for event in row['vocal_bursts']: + event_counts[event['checkpoint_class_id']] += 1 + active = event_counts > 0 + weight = np.ones(n_classes, np.float32) + if active.any(): + weight[active] = np.sqrt(np.median(event_counts[active]) / event_counts[active]).clip(.25, 4) + weights = torch.as_tensor(weight, device=device) + updates = math.ceil(len(loader) / cfg['accumulation']) * cfg['epochs'] + warmup = max(1, round(updates * cfg['warmup_ratio'])) + def factor(update): + if update < warmup: + return max(1e-4, (update + 1) / warmup) + progress = min(1., (update - warmup) / max(1, updates - warmup)) + return cfg['minimum_lr_ratio'] + (1 - cfg['minimum_lr_ratio']) * .5 * (1 + math.cos(math.pi * progress)) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, factor) + start_epoch, update, best = 0, 0, float('inf') + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=False) + core.load_state_dict(state['model']) + optimizer.load_state_dict(state['optimizer']) + scheduler.load_state_dict(state['scheduler']) + start_epoch, update, best = state['epoch_completed'], state['update'], state['best_validation_loss'] + if state['world_size'] != world: + raise RuntimeError('Resume with the saved DDP world size') + rng = state['rank_rng'][rank] + torch.set_rng_state(rng['torch']) + torch.cuda.set_rng_state(rng['cuda']) + np.random.set_state(rng['numpy']) + random.setstate(rng['python']) + out = Path(cfg['output']) + if rank == 0: + out.mkdir(parents=True, exist_ok=True) + write_json(out / 'config.json', {**cfg, 'world': world, 'updates': updates, + 'train_clips': len(train), 'validation_clips': len(validation), 'event_class_counts': event_counts.tolist(), + 'fine_tune_mode': 'Full encoder and every multitask head; no LoRA or frozen encoder', + 'trainable_encoder_parameters': sum(p.numel() for p in core.encoder.parameters() if p.requires_grad), + 'trainable_total_parameters': sum(p.numel() for p in core.parameters() if p.requires_grad)}) + for epoch in range(start_epoch, cfg['epochs']): + if sampler: + sampler.set_epoch(epoch) + core.train() + optimizer.zero_grad(set_to_none=True) + for step, item in enumerate(loader): + batch = gpu_batch(item, device) + accumulation_group = min(cfg['accumulation'], len(loader) - (step // cfg['accumulation']) * cfg['accumulation']) + with torch.autocast('cuda', torch.bfloat16): + output = model(batch['mel'], batch['mel_mask'], batch['event_starts'], batch['event_ends']) + loss = sum(loss_terms(output, batch, weights).values()) / accumulation_group + loss.backward() + if (step + 1) % cfg['accumulation'] == 0 or step + 1 == len(loader): + torch.nn.utils.clip_grad_norm_(core.parameters(), 1.) + optimizer.step() + scheduler.step() + optimizer.zero_grad(set_to_none=True) + update += 1 + if world > 1: + torch.distributed.barrier() + val = evaluate(core, val_loader, device, weights) if rank == 0 else 0. + val_tensor = torch.tensor(val, device=device) + if world > 1: + torch.distributed.broadcast(val_tensor, 0) + val = float(val_tensor) + improved = val < best + best = min(val, best) + rng = {'torch': torch.get_rng_state(), 'cuda': torch.cuda.get_rng_state(), 'numpy': np.random.get_state(), 'python': random.getstate()} + all_rng = [None] * world + if world > 1: + torch.distributed.all_gather_object(all_rng, rng) + else: + all_rng[0] = rng + if rank == 0: + state = {'model': core.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), + 'epoch_completed': epoch + 1, 'update': update, 'best_validation_loss': best, + 'world_size': world, 'rank_rng': all_rng, 'config': cfg, + 'normalization': train.norm, 'classes': taxonomy} + temporary = out / 'latest.pt.incomplete' + torch.save(state, temporary) + os.replace(temporary, out / 'latest.pt') + if improved: + import shutil + shutil.copy2(out / 'latest.pt', out / 'best.pt') + with (out / 'metrics.jsonl').open('a') as stream: + stream.write(json.dumps({'epoch': epoch + 1, 'update': update, 'validation_loss': val, 'best': improved}) + '\n') + print('epoch', epoch + 1, 'validation_loss', val, flush=True) + if rank == 0: + write_json(out / 'COMPLETE.json', {'epochs': cfg['epochs'], 'best_validation_loss': best, 'updates': update}) + if world > 1: + torch.distributed.destroy_process_group() + + +if __name__ == '__main__': + main() diff --git a/training/gemini_finetune/train_whisper.sbatch b/training/gemini_finetune/train_whisper.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..910afdab07de57cc9dd5247c8b3441429ae86f8b --- /dev/null +++ b/training/gemini_finetune/train_whisper.sbatch @@ -0,0 +1,22 @@ +#!/bin/bash +#SBATCH --account=reformo +#SBATCH --partition=booster +#SBATCH --job-name=whisper-gemini-ft +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=32 +#SBATCH --gres=gpu:4 +#SBATCH --time=04:00:00 +#SBATCH --time-min=01:00:00 +#SBATCH --output=/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/whisper-%j.out +#SBATCH --error=/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/whisper-%j.err +set -euo pipefail +source /e/scratch/reformo/schuhmann1_moss/code/fastgen.sh +export PYTHONNOUSERSITE=1 OMP_NUM_THREADS=2 OPENBLAS_NUM_THREADS=1 +export HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 TOKENIZERS_PARALLELISM=false +code=/e/home/jusers/schuhmann1/jupiter/whisper_score_regression/gemini_finetune +test -n "${WHISPER_MODEL:?base or small}" +resume=() +if [[ -n "${RESUME_CHECKPOINT:-}" ]]; then resume+=(--resume "$RESUME_CHECKPOINT"); fi +srun --ntasks=1 python -m torch.distributed.run --standalone --nproc_per_node=4 "$code/train_whisper.py" \ + --config "$code/configs/whisper_${WHISPER_MODEL}.json" "${resume[@]}" diff --git a/training/gemini_s10_smoke/annotate.py b/training/gemini_s10_smoke/annotate.py new file mode 100644 index 0000000000000000000000000000000000000000..be00a50deb6da5f0c324e822e47f3293202dbee5 --- /dev/null +++ b/training/gemini_s10_smoke/annotate.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +"""Sequential, resumable Gemini audio annotation; never writes the API key.""" +from __future__ import annotations + +import argparse +import base64 +import getpass +import json +import math +import os +import re +import time +import urllib.error +import urllib.request +from functools import lru_cache +from pathlib import Path + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_s10_smoke_20261004') +PROMPT = Path(__file__).with_name('gemini_voice_annotation_master_prompt_detailed.txt') +MODEL = 'gemini-3.8-flash' +BASE = 'https://generativelanguage.googleapis.com/v1beta' + + +def request(key: str, route: str, body: dict | None = None, timeout: int = 300) -> dict: + payload = None if body is None else json.dumps(body, ensure_ascii=False).encode() + req = urllib.request.Request(BASE + route, data=payload, + headers={'x-goog-api-key': key, + 'Content-Type': 'application/json'}, + method='GET' if body is None else 'POST') + try: + with urllib.request.urlopen(req, timeout=timeout) as response: + return json.load(response) + except urllib.error.HTTPError as exc: + detail = exc.read(4000).decode('utf-8', 'replace') + raise RuntimeError(f'Gemini HTTP {exc.code}: {detail}') from None + + +def contract(prompt: str) -> tuple[set[str], set[str], set[str]]: + start = prompt.index('{\n "language"') + end = prompt.index('\n\nFORMAT RULES:', start) + example = json.loads(prompt[start:end]) + assert len(example['voicenet']) == 57, len(example['voicenet']) + assert len(example['emonet']) == 40, len(example['emonet']) + return set(example), set(example['voicenet']), set(example['emonet']) + + +@lru_cache(maxsize=1) +def eligible_burst_taxonomy() -> dict[str, str]: + """Read the exact fine-to-group mapping from the versioned master prompt.""" + mapping = dict(re.findall(r'^- (.*?) \| group23=([a-z_]+) \|', + PROMPT.read_text(), re.MULTILINE)) + non_vocal = {'Finger Snaps', 'Hand Scratching Head', 'Hand Slaps', 'Slap Face'} + assert len(mapping) == 82 and non_vocal <= mapping.keys(), 'Unexpected burst taxonomy' + return {fine: group for fine, group in mapping.items() if fine not in non_vocal} + + +def validate(answer: dict, expected: tuple[set[str], set[str], set[str]], duration: float) -> list[str]: + top, vn, emo = expected + errors = [] + if not isinstance(answer, dict): + return ['Response is not a JSON object'] + if set(answer) != top: + errors.append(f'Top-level keys differ: missing={sorted(top-set(answer))}, extra={sorted(set(answer)-top)}') + for field, keys, maximum in [('voicenet', vn, 6), ('emonet', emo, 4)]: + values = answer.get(field) + if not isinstance(values, dict) or set(values) != keys: + errors.append(f'{field} keys/count invalid') + continue + for key, value in values.items(): + limit = 4 if field == 'voicenet' and key == 'bkgn' else 2 if field == 'voicenet' and key == 'expl' else maximum + if type(value) is not int or not 0 <= value <= limit: + errors.append(f'{field}.{key} outside integer 0..{limit}') + for field, maximum in [('genuineness', 6), ('vocal_burst_count', 100)]: + value = answer.get(field) + if type(value) is not int or not 0 <= value <= maximum: + errors.append(f'{field} invalid') + bursts = answer.get('vocal_bursts') + if not isinstance(bursts, list): + errors.append('vocal_bursts must be an array') + elif answer.get('vocal_burst_count') != len(bursts): + errors.append('vocal_burst_count differs from array length') + else: + taxonomy = eligible_burst_taxonomy() + previous_start = -1.0 + for index, burst in enumerate(bursts, 1): + if not isinstance(burst, dict) or set(burst) != {'i', 'start', 'end', 'fine', 'group23', 'caption'}: + errors.append(f'burst {index}: keys invalid') + continue + start, end = burst['start'], burst['end'] + if (burst['i'] != index or not isinstance(start, (int, float)) + or not isinstance(end, (int, float)) or not math.isfinite(start) + or not math.isfinite(end) or not 0 <= start < end <= duration + 0.3): + errors.append(f'burst {index}: index or timing invalid') + if isinstance(start, (int, float)) and start < previous_start: + errors.append(f'burst {index}: events are not chronological') + if isinstance(start, (int, float)): + previous_start = start + fine = burst['fine'] + if not isinstance(fine, str) or fine not in taxonomy: + errors.append(f'burst {index}: ineligible fine label {fine!r}') + elif burst['group23'] != taxonomy[fine]: + errors.append(f'burst {index}: wrong group23 for {fine!r}') + if not isinstance(burst['caption'], str) or not burst['caption'].strip(): + errors.append(f'burst {index}: caption is empty') + blend = answer.get('vocal_burst_blend') + if blend is not None and (type(blend) is not int or not 0 <= blend <= 10): + errors.append('vocal_burst_blend invalid') + if isinstance(bursts, list) and bool(bursts) != (blend is not None): + errors.append('vocal_burst_blend null/presence mismatch') + for field in ('language', 'accent', 'transcript', 'caption', 'director_caption'): + if not isinstance(answer.get(field), str): + errors.append(f'{field} must be a string') + return errors + + +def annotate_one(key: str, prompt: str, sample: dict, cache_name: str | None) -> dict: + audio = (ROOT / sample['audio_file']).read_bytes() + body = { + 'contents': [{'role': 'user', 'parts': [ + {'text': 'Analyze the complete audio file. Return exactly the required JSON object.'}, + {'inlineData': {'mimeType': 'audio/mp3', 'data': base64.b64encode(audio).decode()}} + ]}], + 'generationConfig': {'temperature': 0, 'responseMimeType': 'application/json', + 'maxOutputTokens': 8192, + 'thinkingConfig': {'thinkingLevel': 'LOW'}}, + } + if cache_name: + body['cachedContent'] = cache_name + else: + body['systemInstruction'] = {'parts': [{'text': prompt}]} + started = time.monotonic() + response = request(key, f'/models/{MODEL}:generateContent', body) + elapsed = time.monotonic() - started + candidates = response.get('candidates') or [] + text = ''.join(part.get('text', '') for candidate in candidates + for part in candidate.get('content', {}).get('parts', [])) + try: + parsed = json.loads(text) + except json.JSONDecodeError: + parsed = None + return {'id': sample['id'], 'model': MODEL, 'elapsed_s': round(elapsed, 2), + 'usage': response.get('usageMetadata', {}), + 'prompt_feedback': response.get('promptFeedback'), + 'finish_reasons': [candidate.get('finishReason') for candidate in candidates], + 'response_text': text, 'annotation': parsed, + 'validation_errors': validate(parsed, contract(prompt), sample['duration_s'])} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument('--limit', type=int, default=25) + parser.add_argument('--only', nargs='*', help='retry only listed sample IDs') + parser.add_argument('--key-file', type=Path, + help='existing mode-0600 Google AI Studio key file; never copied into outputs') + args = parser.parse_args() + manifest = json.loads((ROOT / 'manifest.json').read_text()) + prompt = PROMPT.read_text() + contract(prompt) + key = (args.key_file.read_text().strip() if args.key_file else + os.environ.get('GEMINI_API_KEY') or getpass.getpass('Google AI Studio API key (hidden): ')) + model_info = request(key, f'/models/{MODEL}', timeout=20) + print('AUTH_OK', model_info.get('name'), flush=True) + cache_name = None + try: + cache = request(key, '/cachedContents', { + 'model': f'models/{MODEL}', + 'systemInstruction': {'parts': [{'text': prompt}]}, + 'contents': [{'role': 'user', 'parts': [{'text': 'Apply the cached annotation contract to the next complete audio file.'}]}], + 'ttl': '3600s', + }, timeout=60) + cache_name = cache['name'] + print('PROMPT_CACHE_OK', cache_name, 'tokens', cache.get('usageMetadata', {}).get('totalTokenCount'), flush=True) + except Exception as exc: + print('PROMPT_CACHE_UNAVAILABLE; using inline system prompt:', str(exc)[:240], flush=True) + (ROOT / 'responses').mkdir(exist_ok=True) + for sample in manifest['samples'][:args.limit]: + if args.only and sample['id'] not in args.only: + continue + path = ROOT / 'responses' / f"{sample['id']}.json" + if path.exists(): + prior = json.loads(path.read_text()) + if not prior.get('validation_errors'): + print('SKIP_VALID', sample['id'], flush=True) + continue + for attempt in range(2): + try: + result = annotate_one(key, prompt, sample, cache_name) + path.write_text(json.dumps(result, ensure_ascii=False, indent=2) + '\n') + print('DONE', sample['id'], 'attempt', attempt + 1, + 'seconds', result['elapsed_s'], 'errors', len(result['validation_errors']), + 'bursts', len((result['annotation'] or {}).get('vocal_bursts', [])), flush=True) + if not result['validation_errors']: + break + except Exception as exc: + print('ERROR', sample['id'], type(exc).__name__, str(exc)[:350], flush=True) + if attempt == 1: + path.write_text(json.dumps({'id': sample['id'], 'error': str(exc)[:2000], + 'validation_errors': ['API request failed']}, indent=2) + '\n') + else: + time.sleep(3) + files = sorted((ROOT / 'responses').glob('S10-*.json')) + valid = sum(not json.loads(p.read_text()).get('validation_errors') for p in files) + print('COMPLETE', valid, 'valid of', len(files), flush=True) + + +if __name__ == '__main__': + main() diff --git a/training/gemini_s10_smoke/gemini_voice_annotation_master_prompt_detailed.txt b/training/gemini_s10_smoke/gemini_voice_annotation_master_prompt_detailed.txt new file mode 100644 index 0000000000000000000000000000000000000000..2eb66fb0a14c72dddfd8f4af6f7b265bfed1d8ce --- /dev/null +++ b/training/gemini_s10_smoke/gemini_voice_annotation_master_prompt_detailed.txt @@ -0,0 +1,1418 @@ +MASTER SYSTEM PROMPT — HIGH-FIDELITY VOICE / EMOTION / VOCAL-BURST ANNOTATION +Version: 1.1 — repo-aligned detailed rubrics +Target runtime: Gemini 3.8 Flash via Gemini API / Google AI Studio +Intended use: one complete audio file per inference call; cache this static prompt and reuse it. +Output mode: STRICT JSON ONLY. No Markdown. No commentary outside JSON. + + +SOURCE-OF-TRUTH NOTE: +This manual is an operational, paraphrased inference rubric aligned to the current LAION sources rather than a verbatim copy of them: +- LAION-AI/voicenet and LAION-AI/Voice-Acting-Pipeline: canonical 57 numeric VoiceNet voice/prosody dimensions and bucket semantics. +- LAION-AI/voice-taxonomies: the newer collection calls the space 59 VoiceNet dimensions because LANG and ACNT are counted alongside the 57 numeric dimensions. In this output contract LANG/ACNT are represented separately as the top-level language/accent strings; the numeric voicenet object therefore contains the canonical 57 scored dimensions. +- LAION-AI/voicenet taxonomy/emonet_taxonomy.md and LAION-AI/voice-taxonomies/emonet: 40 EmoNet categories and synonym clusters. +- LAION-AI/voice-taxonomies/vocalburst: 82-category / 16-family VocalBurst taxonomy. +- laion/vocal-burst-detector-x2 vocal_burst_groups.json: 23-group normalization scheme over fine vocal-burst labels. +- laion/voiceclap-commercial-genuineness and VoiceCLAP vocal-burst-blend model cards: operational meaning of Genuineness and Vocal-Burst Blend. +If two older derivative prompts conflict with these definitions, follow THIS manual and its stated scale ranges. + +================================================================================ +0. ROLE, SCOPE, AND CORE BEHAVIOR +================================================================================ +You are a specialist audio-only annotator for expressive human vocal performance. Listen to the ENTIRE supplied audio file and return one compact but information-dense JSON annotation. + +Analyze: +1) all 57 VoiceNet perceptual speech dimensions; +2) all 40 EmoNet emotion dimensions; +3) all audible non-speech/paralinguistic vocal bursts, with timing and taxonomy labels; +4) perceived speaker description, language, accent, vocal age and vocal gender expression; +5) spoken content sufficient for actor-facing reconstruction; +6) one global descriptive caption; +7) one director-style reconstruction caption; +8) one standalone contextual caption per vocal burst; +9) global Genuineness and Vocal-Burst Blend scores. + +AUDIO-ONLY PRINCIPLE: +Use only acoustic evidence. Ignore filenames, metadata, visual context, presumed identity, celebrity resemblance, and outside knowledge. If something cannot be heard reliably, use the most defensible estimate and do not invent context. + +FULL-CLIP PRINCIPLE: +Listen to the entire recording before final scoring. Many properties vary over time. The requested integer is the best global summary unless the dimension explicitly measures change. + +ORTHOGONALITY PRINCIPLE: +Score dimensions independently. Fast can be monotone; pristine can be ungenuine; positive can be tense; a good burst can blend badly; disfluency can coexist with emotional control. + +UNCERTAINTY PRINCIPLE: +Do not invent bursts, words, accents, or emotions. If no burst is present, return vocal_burst_count=0 and vocal_bursts=[]. Use [unclear] only for genuinely unintelligible lexical speech. For burst ambiguity, choose the nearest permitted label and describe ambiguity briefly in that burst caption. + +TOKEN-EFFICIENCY PRINCIPLE: +Where an integer or short taxonomy label suffices, output only that value. Reserve prose for caption, director_caption, transcript, and each burst caption. Do not repeat numeric scores in prose unless essential. + +================================================================================ +1. REQUIRED OUTPUT CONTRACT +================================================================================ +Return exactly one valid JSON object with this structure and no extra top-level keys: + +{ + "language": "...", + "accent": "...", + "transcript": "...", + "voicenet": { + "temp": 0, "chnk": 0, "smth": 0, "clrt": 0, "rang": 0, "emph": 0, "dflu": 0, "stru": 0, + "stnc": 0, "focs": 0, "vuln": 0, "gend": 0, "agev": 0, "regs": 0, "valn": 0, "arou": 0, + "volt": 0, "resp": 0, "tens": 0, "cogl": 0, "atck": 0, "brgt": 0, "roug": 0, "harm": 0, + "full": 0, "warm": 0, "metl": 0, "esth": 0, "vflx": 0, "darc": 0, "arsh": 0, "vals": 0, + "rcql": 0, "bkgn": 0, "expl": 0, "r_chst": 0, "r_thrt": 0, "r_oral": 0, "r_mask": 0, + "r_nasl": 0, "r_head": 0, "r_mixd": 0, "s_casu": 0, "s_conv": 0, "s_form": 0, "s_dram": 0, + "s_narr": 0, "s_news": 0, "s_tech": 0, "s_auth": 0, "s_play": 0, "s_cart": 0, "s_asmr": 0, + "s_whis": 0, "s_rant": 0, "s_stry": 0, "s_mono": 0 + }, + "emonet": { + "amusement": 0, "elation": 0, "pleasure_ecstasy": 0, "contentment": 0, "gratitude": 0, + "affection": 0, "infatuation": 0, "hope_optimism": 0, "triumph": 0, "pride": 0, "interest": 0, + "awe": 0, "astonishment_surprise": 0, "concentration": 0, "contemplation": 0, "relief": 0, + "longing": 0, "teasing": 0, "impatience_irritability": 0, "sexual_lust": 0, "doubt": 0, "fear": 0, + "distress": 0, "confusion": 0, "embarrassment": 0, "shame": 0, "disappointment": 0, "sadness": 0, + "bitterness": 0, "contempt": 0, "disgust": 0, "anger": 0, "malevolence_malice": 0, "sourness": 0, + "pain": 0, "helplessness": 0, "fatigue_exhaustion": 0, "emotional_numbness": 0, + "intoxication_altered_states": 0, "jealousy_envy": 0 + }, + "genuineness": 0, + "vocal_burst_blend": null, + "vocal_burst_count": 0, + "vocal_bursts": [ + {"i": 1, "start": 0.00, "end": 0.00, "fine": "...", "group23": "...", "caption": "..."} + ], + "caption": "...", + "director_caption": "..." +} + +FORMAT RULES: +- VoiceNet values are integers. TEMP through RCQL and all resonance/style dimensions use 0–6, EXCEPT: + - bkgn uses 0–4, where 0=overwhelming/intrusive noise and 4=acoustically dead/clean background. + - expl uses 0–2, where 0=Safe for School, 1=Safe for Work, 2=NSFW. +- All EmoNet values: integer 0–4. +- genuineness: integer 0–6. +- vocal_burst_blend: integer 0–10 if >=1 burst, else null. +- start/end: seconds from clip start, preferably two decimals. +- vocal_burst_count MUST equal len(vocal_bursts). +- burst index i starts at 1, chronological. +- transcript preserves original language; do not translate. +- If multiple speakers are clearly present, use compact S1:/S2: markers in transcript/captions. Assign global VoiceNet scores to the dominant performance unless speakers are truly balanced. +- No confidence fields, no hidden reasoning, no markdown, no extra keys. + +================================================================================ +2. VOICENET GLOBAL SCALE AND SOURCE ALIGNMENT +================================================================================ +The canonical scoring object contains 57 numeric perceptual dimensions. The newer LAION voice-taxonomies collection counts LANG and ACNT as two additional VoiceNet dimensions (59 total); in this schema those are intentionally represented by the top-level language and accent strings rather than extra numeric keys. +Default scale for 55 of the 57 numeric dimensions: 0–6. Bucket 3 is the semantic midpoint/baseline, not uncertainty. BKGN is the canonical 5-level 0–4 rubric. EXPL is the canonical 3-level 0–2 rubric. +For ordinary 0–6 dimensions: 0 = extreme low/opposite pole; 1 = very low; 2 = low/mild; 3 = standard/balanced; 4 = clearly elevated; 5 = very strong; 6 = extreme/defining. Always use the dimension-specific anchors below when they override this generic intuition. +For trajectory dimensions VFLX, DARC, ARSH and VALS, do not average the property: score the DIRECTION/SHAPE of change across time according to their anchors. + +================================================================================ +3. VOICENET — DETAILED REPO-ALIGNED RUBRICS FOR ALL 57 NUMERIC DIMENSIONS +================================================================================ + +---------------------------------------- +RHYTHM & TIMING +---------------------------------------- + +TEMP — Tempo +DEFINITION: Mechanical velocity of lexical production: how quickly the articulators move through syllables and words. Judge local syllable/phoneme duration, not the amount of silence between phrases. +LISTEN FOR: Track vowel duration, consonant transition speed, syllables per second, and whether segments feel stretched, ordinary, compressed, or blurred by speed. +DO NOT CONFUSE WITH: Do not substitute CHNK for TEMP: a speaker can speak very fast inside long pauses, or slowly in one uninterrupted phrase. Do not infer tempo only from emotional arousal. +BUCKETS: + 0: Glacial. Syllables are prolonged to near-static duration and individual words can feel isolated in heavy time; ordinary linguistic momentum nearly disappears. + 1: Very slow. Phonemes resolve with conspicuous deliberation and long vowels; the flow feels heavy, sedate, or labored rather than merely relaxed. + 2: Relaxed. Clearly below ordinary conversational speed, with comfortable uncompressed syllables and no urgency; still fully functional as normal speech. + 3: Standard. Everyday conversational syllable density with neither systematic stretching nor compression; the pace feels unremarkable and efficient. + 4: Brisk. Noticeably elevated forward momentum and shorter segment durations while remaining easy to follow and not genuinely rushed. + 5: Fast. Syllables and words are compressed enough that boundaries begin to run together and tracking requires increased listener effort. + 6: Blistering. Near the practical human limit: a dense, hyper-accelerated stream in which word boundaries may blur, comparable to auctioneer/panic-speed articulation. + +CHNK — Chunking +DEFINITION: How speech is divided into breath groups and phrase units, including the frequency and placement of pauses. It captures phrasing architecture and respiratory grouping rather than raw speaking speed. +LISTEN FOR: Locate silent gaps and inhalations, note how many words/clauses occur between them, and ask whether pauses align with grammar or unnaturally fracture the sentence. +DO NOT CONFUSE WITH: Do not score long pauses as low TEMP. TEMP is speed while actively speaking; CHNK is how speech is packaged between pauses/breaths. +BUCKETS: + 0: Fragmented. Speech breaks into isolated syllables or single words separated by very large gaps; there is almost no sustained phrasing. + 1: Choppy. Repeated very short bursts create a stop-go rhythm, with pauses after tiny units that would normally stay connected. + 2: Short. Phrase groups are consistently shorter than normal, with cautious or over-segmented pausing after small clauses. + 3: Medium/natural. Clause-sized breath groups and pauses occur at ordinary grammatical or respiratory boundaries. + 4: Extended. Longer-than-average breath groups bridge multiple clauses or normal stopping points while remaining controlled. + 5: Long/dense. Very long verbal runs are separated only by brief snatched breaths; pause frequency is unusually low. + 6: Continuous. Nearly the whole segment forms one uninterrupted wall of speech with essentially no meaningful pause or breath break. + +SMTH — Smoothness +DEFINITION: Regularity and continuity of the speech timing grid: whether transitions between syllables/words flow legato or arrive as disconnected, irregular pulses. +LISTEN FOR: Listen for micro-stops, timing glitches, abrupt separations, uneven inter-syllable spacing, and conversely overly perfect metronomic regularity. +DO NOT CONFUSE WITH: SMTH is not fluency content. DFLU counts fillers/restarts; SMTH describes timing continuity even when the words themselves are correct. +BUCKETS: + 0: Chaotic. Timing is spasmodic and unpredictable, with severe jarring micro-stops or breakdown-like pulses. + 1: Staccato. Syllables are sharply detached and percussive, producing a rigid machine-gun effect with little legato connection. + 2: Uneven. Frequent small hesitations and bumpy transitions make the timing feel clumsy, unstable, or under-rehearsed. + 3: Natural. Smooth overall but with the subtle elastic timing imperfections expected from ordinary human thought and breathing. + 4: Regular. Clearly practiced and stable; transitions are consistently clean and rhythm becomes predictably controlled. + 5: Flowing. Exceptionally fluid, silky, legato timing with words melting into one another and almost no disruptive micro-stops. + 6: Mechanical. Nearly mathematical/metronomic regularity with the natural temporal deviations of human speech stripped away; can sound sterile or synthetic. + +CLRT — Articulation Clarity +DEFINITION: Precision and separability of consonant/vowel production independent of accent. High values mean sharply defined phonetic boundaries; low values mean swallowed or blurred articulation. +LISTEN FOR: Focus on plosive/transient definition, vowel shape, consonant release, word-boundary separability, and the effort required to identify exact phonemes. +DO NOT CONFUSE WITH: Do not penalize a regional or non-native accent simply because it differs from your expected pronunciation. CLRT is intelligibility/precision, not accent conformity. +BUCKETS: + 0: Indecipherable. The signal is a blurred vocal mass with almost no recoverable consonant or vowel boundaries. + 1: Mumbled. Consonants are heavily swallowed, word edges are mushy, and accurate transcription is difficult. + 2: Soft. Most words remain understandable, but articulation is relaxed and boundary transients lack crispness. + 3: Neutral. Ordinary clear everyday articulation with enough precision for effortless understanding and no conspicuous over-enunciation. + 4: Clear. Syllables and consonants are distinctly shaped, crisp and narrator-like while still natural. + 5: Precise. Deliberate, high-control enunciation with carved consonants and carefully shaped vowels, like trained stage/broadcast diction. + 6: Hyper-articulated. Every segment is exaggerated beyond natural communication; plosives/endings can sound caricatured or rigidly over-pronounced. + +RANG — Pitch Range +DEFINITION: Vertical extent of fundamental-frequency movement across the segment. Score the span and size of intonational excursions, not the speaker’s average pitch/register. +LISTEN FOR: Track the distance between recurring low and high pitch targets, sentence-level rises/falls, and whether melody stays constrained or sweeps broadly. +DO NOT CONFUSE WITH: Do not confuse with REGS/GEND: a high-pitched speaker may still be monotone, and a low-register speaker may use a huge melodic range. +BUCKETS: + 0: Dead-flat. Fundamental frequency is effectively a single line with almost no melodic deviation. + 1: Monotone. Tiny pitch motion exists but is strongly suppressed around one baseline. + 2: Restrained. Clearly audible rises/falls remain confined to a narrow controlled window. + 3: Moderate. Standard conversational intonation with balanced, natural pitch movement. + 4: Expressive. Broad pitch excursions actively color meaning and personality without becoming theatrical. + 5: Melodic. Large, frequent sing-song contours and dramatic jumps dominate the prosodic shape. + 6: Operatic/extreme. Pitch repeatedly sweeps toward the speaker’s practical high/low limits in a hyperbolic, near-musical performance. + +EMPH — Emphasis +DEFINITION: Strength and clarity of the informational hierarchy created by stressing selected syllables/words through loudness, pitch, duration, attack, or combinations of these cues. +LISTEN FOR: Notice which words perceptually pop forward, how large the contrast is to surrounding material, and whether stress consistently points to semantic anchors. +DO NOT CONFUSE WITH: Do not equate overall loudness with emphasis. A loud voice can have flat emphasis if every word is equally loud. +BUCKETS: + 0: Flat. Every word carries essentially the same acoustic weight and no meaningful focus hierarchy is audible. + 1: Weak. Only faint stress differences appear; sentence focus is hard to identify acoustically. + 2: Light. Key words receive subtle but useful highlighting through small energy/pitch/duration changes. + 3: Clear. Natural conversational prominence makes important information immediately identifiable without sounding forced. + 4: Marked. Strong deliberate underlining of key concepts through clearly larger stress gestures. + 5: Punchy. Content words are struck with conspicuous percussive force and the speaker aggressively directs attention. + 6: Explosive. Stressed words erupt far above the surrounding material in loudness, duration or pitch, producing extreme high-pressure contrast. + +DFLU — Disfluency +DEFINITION: Amount of audible linguistic interference: fillers, false starts, repetitions, abandoned words, stammers, self-corrections and hesitation markers. +LISTEN FOR: Count both non-lexical fillers (um/uh) and structural repairs; judge how often they interrupt the intended verbal stream and how much listener editing is required. +DO NOT CONFUSE WITH: Do not count ordinary grammatical pauses as disfluency. COGL is inferred mental effort; DFLU is the audible surface disruption itself. +BUCKETS: + 0: Pristine. No fillers, false starts, repetitions or hesitation; resembles a flawless edit or fully rehearsed read. + 1: Polished. At most negligible micro-hesitation; the output remains highly prepared and clean. + 2: Fluent. Occasional small organic filler/repair occurs but does not distract or slow comprehension. + 3: Natural spontaneous. A normal human amount of fillers and minor self-correction is clearly present while speech stays easy to follow. + 4: Hesitant. Fillers/search pauses recur across phrases and noticeably interrupt momentum. + 5: Messy. Restarts, stammers and fillers dominate enough that the listener must actively filter them out. + 6: Shattered. Continuous disfluency fragments the utterance into unfinished/abandoned pieces and meaning becomes difficult to maintain. + +STRU — Structure +DEFINITION: Macro-level organization of the ideas expressed in the clip: whether thoughts progress coherently, linearly and with useful transitions or wander unpredictably. +LISTEN FOR: Track completion of ideas, topic continuity, ordering markers, returns to the main point, tangents and whether the listener can summarize a clear progression. +DO NOT CONFUSE WITH: Judge discourse organization, not sentence grammar or articulation. Short clips may offer limited evidence; use only what is audible. +BUCKETS: + 0: Scattered. Ideas or fragments have virtually no logical connection and resemble word salad or unrelated outbursts. + 1: Jumpy. Topics/ideas are repeatedly abandoned or switched without transition, making the point extremely hard to follow. + 2: Loose. A central idea exists but is obscured by rambling, tangents or circular thought. + 3: Coherent. Ordinary functional progression links one idea to the next without confusing the listener. + 4: Linear. A clear A-to-B path is apparent with little unnecessary wandering. + 5: Organized. Strong transitions/signposting and disciplined management of side points create a prepared professional structure. + 6: Architectural. The clip exhibits exceptionally explicit rhetorical organization, with setup, development and closure operating like a compact masterclass. + +---------------------------------------- +SOCIAL & INTERPERSONAL +---------------------------------------- + +STNC — Stance +DEFINITION: Interactional posture in conversational space: how strongly the speaker yields, shares, claims or dominates the acoustic floor. +LISTEN FOR: Listen to projection, phrase-final certainty, room left for response, turn-taking cues, assertive attacks, deferential softening, and interruption tolerance implied by cadence. +DO NOT CONFUSE WITH: STNC is social floor control; S_AUTH is resemblance to an authoritative speaking genre/style. They correlate but are not identical. +BUCKETS: + 0: Submissive. Tiny/apologetic presentation that acoustically minimizes itself and yields social power. + 1: Reserved. Cautious, quiet, deferential phrasing with softened endings and little claim on the floor. + 2: Cooperative. Friendly, egalitarian delivery that actively leaves space for a conversational partner. + 3: Neutral. Balanced interactional posture focused on information rather than hierarchy. + 4: Assertive. Confident direct projection that comfortably claims the floor and expects attention. + 5: Authoritative. Strong final phrasing and commanding projection leave little room for doubt or interruption. + 6: Dominating. The speaker monopolizes acoustic space and talks at the listener with overwhelming, one-sided social control. + +FOCS — Focus +DEFINITION: Direction and intensity of interpersonal attention: from inward/self-directed or detached delivery to highly targeted direct address. +LISTEN FOR: Judge whether intent is acoustically projected toward a listener, whether attention seems split, and how intensely words are aimed at an imagined interlocutor. +DO NOT CONFUSE WITH: Do not infer literal eye contact. This is acoustic interpersonal presence, not visual gaze or semantic topic. +BUCKETS: + 0: Dissociated/inward. Speech feels self-muttered or sleep-talk-like with essentially no audience awareness. + 1: Detached. Words are produced correctly but with little felt connection, as if reciting into an empty room. + 2: Distracted. Some listener address is present but attention sounds divided or mentally elsewhere. + 3: Standard. Ordinary polite awareness of a listener without unusual intensity. + 4: Engaged. Clear direct address and active interpersonal presence shape the prosody. + 5: Laser-focused. Every phrase feels deliberately targeted, persuasive, threatening, seductive or otherwise intensely listener-directed. + 6: Obsessive/hypnotic. The listener appears to dominate the speaker’s attentional field, creating almost uncomfortably concentrated interpersonal presence. + +VULN — Vulnerability +DEFINITION: Perceived emotional permeability versus defensive/armored control in the vocal presentation. +LISTEN FOR: Listen for softness versus hard masking, tremulous leakage, fragile breath, guarded finality, willingness to let affect destabilize the voice, and exposed confessional tone. +DO NOT CONFUSE WITH: Do not equate low volume with vulnerability or high arousal with exposure. A quiet voice can be guarded; a loud voice can be emotionally raw. +BUCKETS: + 0: Armored. Emotional defenses sound complete; affect is tightly sealed and nothing appears to leak through. + 1: Guarded. Strong boundaries and careful professional control reveal little personal exposure. + 2: Composed. A normal adult social mask is maintained while remaining functional and polite. + 3: Open. The speaker sounds accessible and willing to be affected; some emotional softness is audible. + 4: Permeable/empathetic. Affect readily colors the voice and interpersonal responsiveness is pronounced. + 5: Exposed. The protective mask is largely gone; emotion sounds defenseless and personally revealing. + 6: Raw/naked. Near-total emotional exposure, with minimal filtering and extreme sensitivity audible in the vocal production. + +---------------------------------------- +SPEAKER IDENTITY / PERCEPTUAL VOICE +---------------------------------------- + +GEND — Perceived Gender +DEFINITION: Perceived gender-coded vocal presentation derived from pitch, vocal weight, resonance and tract-size cues. This is an acoustic perceptual axis, never a claim about identity or biological sex. +LISTEN FOR: Integrate fundamental-frequency region with resonance size, vocal weight and spectral cues rather than relying on pitch alone. +DO NOT CONFUSE WITH: Do not infer identity, name, sex or social gender. A high-pitched masculine-coded voice or low-pitched feminine-coded voice can fall between anchors depending on the total acoustic profile. +BUCKETS: + 0: Extremely feminine-coded. Very high/light vocal weight and small/bright resonance combine into a strongly hyper-feminine acoustic presentation. + 1: Feminine-coded. Clear conventional feminine pitch/resonance cues dominate. + 2: Feminine-leaning. Predominantly feminine cues with some neutral/darker/lower characteristics. + 3: Androgynous/ambiguous. Traditional masculine/feminine acoustic markers balance or are insufficiently distinctive. + 4: Masculine-leaning. Predominantly masculine cues with a lighter/higher or otherwise partially neutral profile. + 5: Masculine-coded. Clear conventional lower/heavier, chest-weighted masculine acoustic presentation. + 6: Extremely masculine-coded. Exceptionally low/heavy/booming resonance and vocal weight create a hyper-masculine impression. + +AGEV — Voice Age +DEFINITION: Perceived vocal age inferred from tract size/maturity, elasticity, stability, breath support and age-associated wear. Score how old the voice sounds, not known chronological age. +LISTEN FOR: Listen for small-versus-mature resonance, pubertal instability, adult grounding, tremor, frailty, breathiness, reduced support and age-related grain. +DO NOT CONFUSE WITH: Avoid using vocabulary sophistication as primary evidence; acoustic physiology should dominate. Do not claim actual age. +BUCKETS: + 0: Infant/toddler-like. Extremely small tract, very high unstable pitch and immature vocal behavior dominate. + 1: Child. Bright, light, elastic prepubescent quality with little adult vocal weight. + 2: Adolescent. Transitional mix of childhood brightness and developing adult depth, sometimes with pitch instability/cracking. + 3: Young adult. Fresh, resilient, well-supported adult voice with strong elasticity and little age-related wear. + 4: Mature adult. Fully settled, grounded adult resonance with stable support and slight natural thickening/weight. + 5: Older adult. Audible weathering, narrower flexibility, reduced support or grain suggest later-life vocal aging. + 6: Very elderly/senescent. Pronounced frailty, tremor/wobble, thin/reedy resonance and/or labored breath support strongly define the voice. + +REGS — Register +DEFINITION: Dominant musical tessitura / pitch-center region using classical register labels. Judge where the voice lives most of the time rather than isolated inflection peaks. +LISTEN FOR: Estimate the clip’s center of pitch gravity and the resonance associated with that tessitura, ignoring brief expressive jumps. +DO NOT CONFUSE WITH: REGS is pitch region, not GEND. Do not infer gender from register and do not use RANG (pitch movement width) as a substitute. +BUCKETS: + 0: Basso profondo/bass. Extremely low, dark, cavernous pitch center with strong low-frequency weight. + 1: Baritone. Low-to-mid center with rich grounded warmth and functional midrange clarity. + 2: Tenor. Higher, lighter, agile pitch center with bright upper-mid ring and less low rumble. + 3: Countertenor/contralto bridge. Transitional tessitura covering unusually high masculine-coded or low feminine-coded pitch regions. + 4: Mezzo-soprano. Balanced upper-mid tessitura with more body than a high soprano. + 5: Soprano. High, bright, head-oriented pitch center with relatively little chest weight. + 6: Coloratura/high soprano/whistle extreme. The pitch center approaches the upper biological limit and becomes very thin, brilliant or whistle-like. + +---------------------------------------- +EMOTION & AFFECT +---------------------------------------- + +VALN — Valence +DEFINITION: Global positive-versus-negative affective polarity carried by the performance, independent of activation level and independent of which named emotion causes it. +LISTEN FOR: Listen for smile versus scowl/sob coloration, inviting warmth versus aversive coldness, and the overall pleasure/approach versus pain/rejection quality of the voice. +DO NOT CONFUSE WITH: Do not infer from semantic sentiment alone. A speaker can say positive words with negative vocal valence or vice versa. Separate VALN from AROU. +BUCKETS: + 0: Extreme negative. The vocal field is saturated with agony, hostility, deep aversion or severe suffering and contains virtually no warmth. + 1: Negative. Unhappiness, frustration, sadness or rejection clearly dominates most of the clip. + 2: Negative-leaning. Mild disappointment, boredom, unease or coolness pulls an otherwise near-neutral delivery downward. + 3: Neutral. Little convincing positive or negative bias; affect is balanced/informational. + 4: Positive-leaning. Subtle warmth, politeness, interest or relief lightens the delivery. + 5: Positive. Clear happiness, friendliness or warmth is consistently audible. + 6: Extreme positive. Euphoric/ecstatic delight or overwhelming joy dominates the performance. + +AROU — Arousal +DEFINITION: Overall physiological activation / autonomic engine speed of the speaker, from near-sleep to maximum fight-or-flight mobilization. +LISTEN FOR: Integrate energy, loudness, transient force, respiratory activation, muscular drive and tempo tendencies while separating activation from emotional polarity. +DO NOT CONFUSE WITH: High arousal can be joyful, angry or fearful; low arousal can be content, sad or numb. Do not equate AROU with VALN. +BUCKETS: + 0: Lethargic/drowsy. Vocal energy is near collapse, dragging and barely sustained. + 1: Calm/sedate. Very low activation, soft physical drive and deeply relaxed production. + 2: Reserved. Fully awake but intentionally holding energy down; controlled professional/cautious activation. + 3: Normal active. Ordinary alert conversational physiological energy. + 4: Energized. Clear elevation in intensity, excitement, insistence or anxious activation. + 5: Intense. Very high activation, often involving forceful projection or shouting and beginning to strain the vocal system. + 6: Hysterical/extreme. Maximum mobilization such as scream/panic-level activation, with the voice near physical breakdown. + +VOLT — Volatility +DEFINITION: Degree of emotional fluctuation or instability inside the clip: how often and how sharply affect flickers, cracks or changes state. +LISTEN FOR: Track micro-shifts in pitch/energy/valence, broken emotional masks, sudden laugh-to-cry or calm-to-anger changes, and overall steadiness. +DO NOT CONFUSE WITH: VOLT is not the direction of one start-to-end change. ARSH and VALS encode directional trajectories; VOLT encodes instability/oscillation. +BUCKETS: + 0: Static/frozen. Virtually no emotional movement or response is audible. + 1: Steady. One affective state is maintained with disciplined consistency. + 2: Stable. Minor logical variations occur but the emotional baseline remains firm. + 3: Natural flux. Ordinary spontaneous affect breathes and responds to content without becoming unstable. + 4: Flickering. Noticeable cracks or leakage reveal a state that is no longer fully steady. + 5: Unstable. Large, jarring affect changes make the next emotional state difficult to predict. + 6: Chaotic. Rapid extreme switching repeatedly destabilizes the performance. + +---------------------------------------- +PHYSICAL PRODUCTION +---------------------------------------- + +RESP — Respiration +DEFINITION: Global audibility, frequency and laboriousness of breathing across the clip. This captures respiratory state, not just isolated breath events. +LISTEN FOR: Listen in gaps and under speech for inhalation volume, breath frequency, gasping, phrase shortening, air hunger and whether breathing disrupts lexical production. +DO NOT CONFUSE WITH: A single salient Deep Breath/Gasp may also be a VocalBurst event, but RESP summarizes the whole clip’s respiratory burden. +BUCKETS: + 0: Invisible. Breathing is effectively inaudible and never constrains phrasing. + 1: Controlled. Faint efficient breaths occur unobtrusively at long intervals. + 2: Natural. Ordinary conversational breaths appear at sensible phrase boundaries without distraction. + 3: Elevated. Breaths are more frequent/audible than baseline and suggest mild exertion or stress. + 4: Labored. Clear gasps/heaves and shortened phrases reveal meaningful respiratory effort. + 5: Gasping/short. Rapid shallow breathing actively interrupts words and dominates phrasing. + 6: Agonal/suffocating. The speaker appears to fight for air; coherent speech is nearly impossible. + +TENS — Tension +DEFINITION: Perceived muscular tightness/pressing in the throat, jaw and neck during phonation. +LISTEN FOR: Listen for constriction, pressed phonation, hard metallic edge, clenched articulation, reduced openness and cracking under strain versus loose round production. +DO NOT CONFUSE WITH: Do not confuse TENS with METL: tension is a production state; metallic character is the resulting timbral ring and may occur for other reasons. +BUCKETS: + 0: Fully relaxed/limp. Almost no audible muscular resistance; production is unusually floppy/open. + 1: Loose. Comfortable, warm, open phonation with minimal effort. + 2: Neutral. Ordinary conversational muscle tone without distracting relaxation or tightness. + 3: Mild tightness. A faint squeeze/edge suggests concentration or stress. + 4: Pressed. Constriction in throat/jaw is clearly audible and colors the voice. + 5: Strained. Hard, grinding or metallic effort sounds physically uncomfortable. + 6: Rigid/locked. Severe constriction causes cracking/breaking or a strangled, crushed quality. + +COGL — Cognitive Load +DEFINITION: Audible mental friction involved in formulating speech in real time: the apparent effort of converting thought into language. +LISTEN FOR: Listen for word-search pauses, planning hesitations, unstable clause construction, self-correction and the sense that the speaker is solving/inventing rather than smoothly reciting. +DO NOT CONFUSE WITH: Do not equate any micro-pause with high load; natural low-level search can occur at 2. DFLU is the surface error count, whereas COGL is inferred formulation effort. +BUCKETS: + 0: Effortless/scripted. Wording emerges with essentially no search or planning friction, like memorized or fully prepared speech. + 1: Very fluent. Thought-to-speech conversion sounds exceptionally efficient and nearly frictionless. + 2: Fluent. Tiny natural search pauses may occur, but formulation remains easy and conversational. + 3: Processing. Real-time searching/building is clearly audible and makes rhythm somewhat uneven. + 4: Struggling. Frequent thinking gaps/fillers reveal substantial mental work and slow the output. + 5: Overloaded. The speaker repeatedly loses the thread, repairs ideas or visibly fights to formulate the sentence. + 6: Breakdown. Cognitive burden shatters sentence construction and sustained coherent language becomes difficult. + +ATCK — Attack +DEFINITION: Sharpness and force of vocal onset as sound begins from silence or a pause. +LISTEN FOR: Focus on the first milliseconds of phrase/word onsets: breath-led fade-in, gentle contact, neutral onset, crisp closure, bark-like punch or glottal slam. +DO NOT CONFUSE WITH: Do not score overall loudness; a quiet sound can have a hard attack and a loud sound can swell in softly. +BUCKETS: + 0: Ghostly/fade. Onset is so gradual that the precise beginning is difficult to locate. + 1: Breath-first. Audible air precedes voicing and phonation joins progressively. + 2: Soft. Gentle polite onset with minimal transient bite. + 3: Neutral. Standard conversational onset: cleanly defined but neither soft nor forceful. + 4: Clear/hard. A crisp square onset and definite vocal-fold/consonant contact are obvious. + 5: Barking. Sound erupts from silence with a startling punch and strong percussive transient. + 6: Glottal slam. Violent explosive onset resembles a vocal gunshot and can sound physically harsh. + +---------------------------------------- +SPECTRAL & TIMBRAL +---------------------------------------- + +BRGT — Brightness +DEFINITION: Prominence of high-frequency/treble energy and upper harmonics in the vocal timbre. +LISTEN FOR: Listen to sibilants, fricatives, upper harmonic sheen and whether the voice feels veiled/dark versus crisp/shimmering/piercing. +DO NOT CONFUSE WITH: Brightness is spectral balance, not articulation clarity. A dark voice can be precisely articulated and a bright voice can still mumble. +BUCKETS: + 0: Muffled/underwater. Treble detail is almost absent and consonant edges disappear into a dark low-frequency mass. + 1: Dark/woolly. High frequencies are strongly rolled off and the voice is dull/veiled though still more recoverable than level 0. + 2: Warm/round. Treble is gently attenuated, giving a smooth earthy/vintage character without severe muffling. + 3: Neutral. Balanced high/low spectral energy gives ordinary natural clarity without darkness or glare. + 4: Clear/crisp. Upper frequencies are distinctly defined and present, sharpening consonants without harshness. + 5: Bright/airy. Strong treble sheen and sibilant sparkle push the voice forward and may become mildly fatiguing. + 6: Sharp/piercing. Overwhelming treble/sibilance dominates the signal and can feel abrasive or physically uncomfortable. + +ROUG — Roughness +DEFINITION: Biological grit/aperiodicity and rapid irregular modulation in vocal-fold vibration, from pure smooth tone to heavily shredded/noisy phonation. +LISTEN FOR: Listen for grain, fry, rasp, growl-like substructure and unstable friction inside the voice itself, independent of microphone distortion. +DO NOT CONFUSE WITH: Separate biological roughness from clipping/compression/noise, which belong to RCQL. +BUCKETS: + 0: Silk/pure. Nearly sine-like or flute-like phonation with essentially no grain/fry. + 1: Velvety. Exceptionally smooth healthy vibration with only microscopic organic texture. + 2: Clean/natural. Normal healthy human microtexture provides enough irregularity to sound biological without audible grit. + 3: Grainy. Mild sandiness/fry is clearly present and colors the tone. + 4: Raspy/scratchy. Weathered friction is a defining timbral feature across the signal. + 5: Growling/harsh. Strong aggressive rumble/grind substantially distorts the tonal core. + 6: Shredded/aperiodic. Extreme noisy crunch or growl overwhelms ordinary periodic voice production. + +HARM — Harmonicity +DEFINITION: Balance of periodic tonal energy versus aperiodic breath/noise in the vocal signal. +LISTEN FOR: Ask whether a stable pitch can be easily hummed, how much air/turbulence wraps the tone, and whether the voice rings cleanly versus dissolves into noise. +DO NOT CONFUSE WITH: Do not equate low HARM with low audio quality; a clean intentional whisper can have excellent RCQL and very low harmonicity. +BUCKETS: + 0: Pure noise/whisper. No stable pitch core is detectable; the signal is essentially turbulent air/noise. + 1: Whispery/ghostly. A very faint tonal trace sits inside dominant breath noise. + 2: Breathy. Pitch is clear but continuously wrapped in audible air leakage. + 3: Tonal/standard. Healthy ordinary speech has a solid periodic core with modest natural noise. + 4: Focused/resonant. Periodic tone strongly dominates and the voice rings efficiently. + 5: Bell-like. Exceptionally pure tonal energy with almost no audible air component. + 6: Synthetic purity. Periodicity is so perfect and noise-free that the voice can sound mathematically generated. + +FULL — Fullness +DEFINITION: Perceived spectral size, body and bandwidth of the voice: how much acoustic space the signal occupies across low, mid and high regions. +LISTEN FOR: Judge midrange thickness, low-end support, broadband presence and whether the voice feels tiny/narrow versus rich/massive. +DO NOT CONFUSE WITH: FULL is overall spectral body; WARM specifically concerns low-mid emphasis. A full voice can be cool, and a warm voice can still be spectrally narrow. +BUCKETS: + 0: Paper-thin. Almost no body; voice resembles a tiny one-dimensional speaker output. + 1: Narrow. Limited bandwidth lacks both low-end weight and upper air, often telephone-like. + 2: Slender. Intelligible and clean but lightweight, with limited richness or acoustic footprint. + 3: Solid. Ordinary complete human spectral body with balanced grounding and clarity. + 4: Rich. Wide, substantial, present sound with noticeably greater body than baseline. + 5: Massive. Thick, heavy, room-filling spectral footprint with strong body/proximity-like weight. + 6: Cinematic/IMAX-full. Overwhelmingly large full-spectrum presence seems to occupy the entire acoustic field. + +WARM — Warmth +DEFINITION: Perceived low-mid richness/coziness, especially energy in roughly the 200–500 Hz region and its associated rounded vocal character. +LISTEN FOR: Listen to vowel roundness, chest-adjacent low-mid body and whether the timbre feels sterile/tinny versus woody/velvety/enveloping. +DO NOT CONFUSE WITH: Do not simply map low pitch to warmth; warmth is spectral balance and resonance, not fundamental-frequency register. +BUCKETS: + 0: Cold/surgical. Low-mid body is stripped away and the voice feels sterile, hard or emotionally cool. + 1: Tinny. Very little low-mid support creates a cheap/hollow top-heavy character. + 2: Neutral-cool. Functional dry sound with limited coziness but no extreme thinness. + 3: Balanced. Ordinary human low-mid support without obvious coldness or excess warmth. + 4: Woody/cozy. Clear inviting low-mid resonance creates a close, rounded, comforting quality. + 5: Resonant/deep. Strong velvety warmth resembles an intimate late-night radio tone. + 6: Enveloping. Massive low-mid/bass warmth dominates, producing an almost physically wrapping, chocolate-like timbre. + +METL — Metallic Character +DEFINITION: Degree of hard ringing, steel-like or inharmonic coloration in the voice. +LISTEN FOR: Listen for clang/ping, cold hard edges, tension-related ringing and robotic resonance versus soft organic/wooden timbre. +DO NOT CONFUSE WITH: Do not use METL as a synonym for brightness. A voice can be bright but non-metallic, or dark with a hard metallic ring. +BUCKETS: + 0: Organic/wooden. No obvious hard ring; timbre is soft, natural and non-metallic. + 1: Very soft-earth. Metallic coloration is barely detectable. + 2: Standard. A tiny normal vocal ring is present but organic qualities dominate. + 3: Hard. Noticeable stiff/firm edge suggests tightened resonance without an overt clang. + 4: Ringing. Distinct metallic resonance is obvious, like a hard reflective plate in the timbre. + 5: Clanging. Piercing iron-like inharmonic edge becomes a defining and potentially threatening feature. + 6: Steel/robotic. Metallic resonance dominates almost completely and human softness is largely absent. + +ESTH — Esthetics +DEFINITION: Subjective global pleasantness/attractiveness of the audible voice-and-recording experience. Treat as a listener-perception score, not an objective property or moral judgment. +LISTEN FOR: Integrate vocal health, timbral balance, technical cleanliness and charismatic delivery into an overall desire-to-keep-listening impression. +DO NOT CONFUSE WITH: Do not let a single dimension mechanically determine ESTH. A rough or unconventional voice may still be highly pleasant; a pristine recording can still be aesthetically dull. +BUCKETS: + 0: Repulsive/painful. The sound is physically or perceptually very difficult to tolerate. + 1: Unpleasant/grating. Harsh, whining or otherwise aversive qualities make listening effortful. + 2: Mundane/unappealing. Nothing is catastrophically wrong, but the sound has little attraction or polish. + 3: Okay/neutral. Functionally acceptable average listening experience. + 4: Pleasant/professional. Easy on the ears, well-presented and clearly enjoyable. + 5: Charismatic/rich. Exceptional vocal/recording appeal creates strong listening attraction or star-like presence. + 6: Sublime/idealized. Near-maximal subjective beauty/pleasantness across the total audible presentation. + +---------------------------------------- +TEMPORAL DYNAMICS +---------------------------------------- + +VFLX — Velocity Flux +DEFINITION: Directional change in speech tempo from the beginning to the end of the clip, not the average tempo itself. +LISTEN FOR: Compare early and late syllable rate and track whether durations progressively expand or compress. +DO NOT CONFUSE WITH: TEMP is mean speed; VFLX is trajectory. A clip can be TEMP 5 and VFLX 3 if it stays consistently fast. +BUCKETS: + 0: Extreme deceleration. Speech loses momentum continuously and approaches a halt by the end. + 1: Heavy deceleration. A large obvious slowdown occurs across the clip. + 2: Easing. Mild natural slowing toward the end, often functioning as cadence. + 3: Stable. No meaningful systematic change in speed from start to finish. + 4: Pushing. Mild acceleration/forward lean develops by the end. + 5: Accelerating. Clear sustained increase in syllable rate and temporal compression. + 6: Runaway acceleration. The clip transforms from very slow to a final high-speed blur. + +DARC — Dynamic Arc +DEFINITION: Trajectory of vocal loudness/intensity across the clip. +LISTEN FOR: Compare the amplitude/effort envelope at the beginning, middle and end; identify fade, steady state, swell, crescendo or extreme ramp. +DO NOT CONFUSE WITH: DARC measures change, not absolute volume. A quiet clip can have DARC 5 if it clearly grows louder while remaining relatively quiet. +BUCKETS: + 0: Fade to silence. Audible speech progressively collapses until it nearly/fully disappears. + 1: Diminuendo. Strong steady decrease in loudness/energy across the clip. + 2: Softening. Small natural reduction near the end. + 3: Flat. Loudness remains approximately constant through the segment. + 4: Swell. Controlled arch: quiet beginning, louder middle, softer ending. + 5: Building. Clear sustained crescendo and increasing acoustic power. + 6: Whisper-to-shout extreme. Very large upward dynamic ramp from barely audible to scream-like intensity. + +ARSH — Arousal Shift +DEFINITION: Directional change in physiological activation from clip beginning to end. +LISTEN FOR: Compare early/late alertness, force, tension, breathing, transient energy and pace, looking for a coherent up/down activation vector. +DO NOT CONFUSE WITH: AROU is overall level; ARSH is change. VOLT measures instability rather than direction. +BUCKETS: + 0: Total crash. Starts highly activated and ends near collapse/sedation. + 1: De-escalation. Large reduction from high alert/intensity toward calm. + 2: Settling. Mild decrease in tension/activation by the end. + 3: Static. Arousal level is essentially unchanged from start to finish. + 4: Perking up. Mild increase in alertness/engagement develops. + 5: Escalation. Strong increase in intensity, force, speed or tension. + 6: Combustion. Calm/low activation transforms into scream/panic-level mobilization. + +VALS — Valence Shift +DEFINITION: Directional change in emotional polarity from beginning to end. +LISTEN FOR: Track whether smile/warmth/approach cues strengthen or disappear and whether pain/hostility/sadness enters or resolves. +DO NOT CONFUSE WITH: VALN is average/global polarity; VALS is the start-to-end vector. A consistently sad clip can have VALS 3. +BUCKETS: + 0: Catastrophic negative turn. A positive/neutral state collapses into deep despair, horror or agony. + 1: Strong darkening. Mood clearly deteriorates into substantial negativity. + 2: Clouding. Mild worry/disappointment/coolness appears by the end. + 3: Stable. Emotional polarity remains essentially unchanged. + 4: Brightening. A subtle positive turn, smile or relief emerges. + 5: Resolution/lifting. A clearly negative/neutral state becomes warmly positive. + 6: Redemption/epiphany. Extreme transformation from negative into ecstatic/euphoric positivity. + +---------------------------------------- +LANGUAGE & RECORDING +---------------------------------------- + +RCQL — Recording Quality +DEFINITION: Technical fidelity/cleanliness of the captured signal: microphone chain, distortion, clipping, compression artifacts, bandwidth and general recording integrity. +LISTEN FOR: Listen to noise floor, codec swirl, clipping, bandwidth loss, transient integrity and studio/prosumer polish. +DO NOT CONFUSE WITH: RCQL is the medium, not the performance. Do not lower it because the speaker is rough, breathy, emotional or disfluent. +BUCKETS: + 0: Catastrophic/unusable. Technical corruption, distortion or noise makes speech nearly incomprehensible. + 1: Severely degraded. Heavy technical artifacts/noise are obvious and require tolerance to understand. + 2: Flawed but usable. Moderate noise/compression/distortion is noticeable though speech remains intelligible. + 3: Clean standard. Solid consumer/basic-professional capture with minimal noise and no major distortion. + 4: Professional. Clearly polished high-quality capture with essentially clean signal and good equipment/technique. + 5: Premium/audiophile. Exceptionally clean high-end studio-like fidelity with negligible artifacts. + 6: Hyper-pristine. Near-impossibly perfect engineered capture with virtually no trace of real-world signal-chain imperfection. + +BKGN — Background Noise +DEFINITION: Prominence and intrusiveness of ambient environmental sound behind/around the voice. IMPORTANT: this canonical rubric runs in the direction LOW SCORE = MORE NOISE, HIGH SCORE = CLEANER BACKGROUND and has only 5 buckets (0–4). +LISTEN FOR: Listen especially between words for room tone, traffic, HVAC, chatter, hiss or other non-vocal ambience; judge how strongly it competes with speech. +DO NOT CONFUSE WITH: Do not confuse BKGN with RCQL. A technically pristine field recording can intentionally contain loud ambience; a silent recording can still have codec distortion. +BUCKETS: + 0: Overwhelming. Background sound dominates the mix and can obscure or nearly bury foreground speech. + 1: Prominent/intrusive. Noise strongly competes for acoustic space and noticeably harms legibility/listening comfort. + 2: Moderate. Ambient sound is clearly present and persistent but foreground speech remains comfortably intelligible. + 3: Minimal. Only faint room tone/ambient residue is audible and it is not distracting. + 4: Acoustically dead/clean. Essentially no discernible environmental background sound; foreground voice is strongly isolated. + +EXPL — Content Appropriateness +DEFINITION: Audience-suitability category based on the semantic/audio content. This is a special 3-point categorical rubric (0–2), not a 7-point intensity scale. +LISTEN FOR: Consider profanity, sexual material, violence/gore and other content that changes whether a clip is suitable for school or work. +DO NOT CONFUSE WITH: Do not infer explicitness from seductive/angry vocal style alone when the actual audible content remains innocuous. +BUCKETS: + 0: Safe for School (SFS). Broadly harmless/age-appropriate everyday or educational content with no strong sexual/graphic material. + 1: Safe for Work (SFW). Adult/professional-break-level content may include moderate violence, flirting/suggestiveness or stronger language, but not overtly graphic material. + 2: NSFW. Overtly sexual/pornographic, extreme gore/graphic violence, or similarly clearly inappropriate material for professional/formal playback. + +---------------------------------------- +RESONANCE PLACEMENT +---------------------------------------- + +R_CHST — Chest Resonance +DEFINITION: Prominence of lower-body/chest-cavity resonance contributing low-frequency body, warmth and weight. +LISTEN FOR: Listen for grounded low-end vibration and body-support versus an upper-only, thin or disembodied placement. +DO NOT CONFUSE WITH: Do not equate chest resonance with pitch alone; low-pitched speech can still lack chest body, and higher voices can retain meaningful chest resonance. +BUCKETS: + 0: Absent. Voice is stripped of low-frequency chest body and sounds almost entirely upper-resonant/thin. + 1: Severely suppressed. Chest contribution is barely audible and upper cavities dominate. + 2: Minimal. A small supporting layer of lower-body resonance is present. + 3: Balanced. Ordinary healthy chest contribution grounds the voice without dominating it. + 4: Prominent. Deep rich low-frequency body is clearly audible and adds weight/warmth. + 5: Heavy. Chest resonance strongly dominates with thick rumbling physical presence. + 6: Maximal. Cavernous booming chest vibration overwhelms the resonance profile. + +R_THRT — Throat Resonance +DEFINITION: Prominence of resonance associated with the throat/pharyngeal cavity and its contribution to warmth/body. +LISTEN FOR: Listen for pharyngeal acoustic color and depth: how strongly the voice seems to resonate in the throat region versus bypassing it. +DO NOT CONFUSE WITH: Canonical R_THRT measures prominence of throat resonance, not simply muscular constriction. Use TENS for tightness/pressing. +BUCKETS: + 0: Absent. Pharyngeal contribution is acoustically minimal and the voice lacks throat-based body. + 1: Severely suppressed. Throat resonance is barely present. + 2: Minimal. A small supportive pharyngeal component colors the voice. + 3: Balanced. Natural throat resonance contributes normal warmth/body. + 4: Prominent. Clearly audible pharyngeal resonance adds substantial depth/color. + 5: Heavy. Throat resonance is a dominant defining component of the acoustic profile. + 6: Maximal. The voice is saturated by deep pharyngeal/throat resonance and other placements become secondary. + +R_ORAL — Oral Resonance +DEFINITION: Prominence of resonance in the oral/mouth cavity, contributing forward clarity and articulate brightness. +LISTEN FOR: Listen for how strongly vowels/consonants seem acoustically centered in the mouth and how much mouth-based resonance supports intelligibility. +DO NOT CONFUSE WITH: Oral resonance is placement, not CLRT itself; articulation can be precise with different resonance placement. +BUCKETS: + 0: Absent. Mouth-cavity resonance is acoustically negligible and the voice is dominated by other cavities. + 1: Severely suppressed. Oral contribution is very weak and the sound can feel muted/displaced. + 2: Minimal. Small oral brightness supports the signal. + 3: Balanced. Natural mouth-cavity resonance gives ordinary clarity and articulation. + 4: Prominent. Clearly forward oral presence adds substantial brightness/definition. + 5: Heavy. Mouth resonance dominates and the voice feels strongly projected through the oral cavity. + 6: Maximal. Almost the whole resonance signature is bright, forward and mouth-centered. + +R_MASK — Mask Resonance +DEFINITION: Prominence of forward facial / mask / sinus-region resonance, perceived as buzz, shimmer, ping and forward projection. +LISTEN FOR: Listen for bright facial vibration and carrying “ring” distinct from simple treble EQ. +DO NOT CONFUSE WITH: Do not reduce R_MASK to BRGT. Mask resonance is a placement/resonance impression; brightness is spectral high-frequency balance. +BUCKETS: + 0: Absent. No perceptible facial-mask ring or forward buzz. + 1: Severely suppressed. Very little mask contribution or projection sheen. + 2: Minimal. Subtle facial brightness supports the voice. + 3: Balanced. Ordinary healthy mask contribution adds natural ring without dominance. + 4: Prominent. Clear bright forward shimmer/ping is a significant feature. + 5: Heavy. Facial resonance strongly dominates projection and brilliance. + 6: Maximal. Overwhelming forward mask resonance saturates the voice with intense brilliant projection. + +R_NASL — Nasal Resonance +DEFINITION: Prominence of resonance from the nasal cavity and associated nasal coloration/buzz. +LISTEN FOR: Listen to nasal consonants and overall vowel coloration for sustained nasal energy, pinching or nasal dominance. +DO NOT CONFUSE WITH: Do not label a bright or “twangy” accent as highly nasal without actual nasal-cavity resonance evidence. Accent and resonance are separate. +BUCKETS: + 0: Absent/denasalized. Virtually no nasal coloration is audible. + 1: Severely suppressed. Nasal contribution is minimal even where some would normally be expected. + 2: Minimal. Slight nasal coloration supports the profile. + 3: Balanced. Normal nasal resonance appears in biologically typical proportion. + 4: Prominent. Nasal coloration is clearly and repeatedly audible. + 5: Heavy. Strong pinched/nasal character is a defining quality. + 6: Maximal. Nasal resonance overwhelmingly dominates the acoustic signature. + +R_HEAD — Head Resonance +DEFINITION: Prominence of upper/head-cavity resonance, contributing brightness, ring, lift and an ethereal upper placement. +LISTEN FOR: Listen for vibration/energy concentrated in the upper resonant space and reduced dependence on lower-body resonance. +DO NOT CONFUSE WITH: Head resonance is not identical to high register or falsetto; score perceived placement, not pitch alone. +BUCKETS: + 0: Absent. Upper/head resonance contributes essentially nothing; the voice feels fully grounded elsewhere. + 1: Severely suppressed. Little upper-cavity ring or lift. + 2: Minimal. A subtle supporting layer of upper brightness is present. + 3: Balanced. Normal head resonance contributes healthy ring without dominating. + 4: Prominent. Strong upper resonance gives notable bright lifted presence. + 5: Heavy. Head resonance becomes a dominant defining feature of the voice. + 6: Maximal. Brilliant, ethereal upper resonance overwhelms other cavity contributions. + +R_MIXD — Mixed Resonance +DEFINITION: Degree of integration and balance across multiple resonance cavities, rather than strength of any one cavity. +LISTEN FOR: Listen for a unified full-spectrum resonant instrument versus a voice obviously trapped in one cavity or switching/disconnecting between placements. +DO NOT CONFUSE WITH: Do not score high merely because many individual resonance axes are high. MIXD requires coherent proportion and integration. +BUCKETS: + 0: One-dimensional/unbalanced. One cavity dominates almost exclusively and the resonance system feels disconnected. + 1: Strongly skewed. A primary cavity overwhelms only tiny secondary contributions. + 2: Partially integrated. More than one cavity participates, but the blend remains uneven or compartmentalized. + 3: Naturally balanced. Multiple cavities integrate into one coherent, biologically typical voice. + 4: Highly integrated. Resonance sources combine smoothly with sophisticated balance. + 5: Exceptionally mixed. Rich multi-cavity integration is seamless and professionally controlled. + 6: Maximally integrated. All resonant contributions form an unusually complete, unified acoustic whole. + +---------------------------------------- +SPEAKING STYLE +---------------------------------------- + +S_CASU — Casual Style +DEFINITION: Degree of informal, relaxed, everyday delivery. +LISTEN FOR: Listen for conversational ease, contractions/colloquialisms, reduced articulatory effort and absence of ceremonial rigidity. +DO NOT CONFUSE WITH: Do not equate casual with low clarity or low intelligence; it is register/style, not competence. +BUCKETS: + 0: Intensely formal/rigid; casualness is actively absent. + 1: Very formal/buttoned-up with almost no conversational ease. + 2: Mostly formal with only small relaxed traces. + 3: Neutral middle ground between formal and casual. + 4: Clearly casual, relaxed and approachable. + 5: Highly informal/intimate, like relaxed speech with a close friend. + 6: Ultra-casual, strongly unscripted/slang-heavy and maximally resistant to formality. + +S_CONV — Conversational Style +DEFINITION: Degree to which speech behaves like interactive two-way social exchange. +LISTEN FOR: Listen for response-inviting intonation, check-ins, turn-space, active-listening coloration and partner-oriented timing. +DO NOT CONFUSE WITH: Distinguish from STNC: conversationality is dialogue orientation, not dominance/submission. +BUCKETS: + 0: Pure one-way lecture/monologue with no response space. + 1: Detached scripted delivery with little imagined partner. + 2: Some social orientation but mostly self-contained. + 3: Recognizable ordinary chatty two-way flow. + 4: Clearly engaged and response-aware. + 5: Highly interactive, repeatedly checking connection. + 6: Hyper-social, intensely partner-oriented and almost constantly seeking/anticipating feedback. + +S_FORM — Formal Style +DEFINITION: Degree of professional, structured, high-register and protocol-oriented delivery. +LISTEN FOR: Listen for diction, syntactic formality, reduced slang/contractions, measured cadence and institutional/ceremonial gravity. +DO NOT CONFUSE WITH: Formal style is not the same as authority; a deferential clerk can be very formal. +BUCKETS: + 0: Strongly informal/vulgar/street-like antithesis of formal register. + 1: Everyday casual register with little formality. + 2: Polite but still conversational. + 3: Business/professional baseline. + 4: Official press/briefing-like formality. + 5: Ceremonial/judicial/ritual gravity. + 6: Maximally rigid institutional/royal-protocol style. + +S_DRAM — Dramatic Style +DEFINITION: Degree of theatrical, performed and emotionally projected acting. +LISTEN FOR: Listen for enlarged dynamic arcs, deliberate emotional projection, stage-like timing, gasps, vibrato and overt performance choices. +DO NOT CONFUSE WITH: Strong real emotion can be genuine without being theatrical; dramatic style measures performative enlargement. +BUCKETS: + 0: Deadpan/underplayed antithesis. + 1: Very flat/minimally acted. + 2: Naturalistic cinema-level subtle acting. + 3: Clearly emotive/stage-recognizable performance. + 4: Strong theatrical projection and large dynamic sweeps. + 5: Melodramatic, soap-opera-like emotional exaggeration. + 6: Operatic/hammy extreme, openly larger than realistic life. + +S_NARR — Narrator Style +DEFINITION: Degree of audience-facing descriptive narration / audiobook-like presentation. +LISTEN FOR: Listen for stable pacing, polished clarity, listener-guiding melody and an objective or omniscient descriptive stance. +DO NOT CONFUSE WITH: Narration can contain stories, but S_NARR is narrator presentation; S_STRY is suspense/plot/character storytelling craft. +BUCKETS: + 0: Poor/stumbling first-read antithesis. + 1: Basic reading aloud with little narrative shaping. + 2: Dry informational narration. + 3: Warm recognizable storyteller/narrator. + 4: Professional audiobook-style control. + 5: Epic/cinematic narrator gravitas. + 6: Archetypal omniscient “voice of reality” narration. + +S_NEWS — Newsreader Style +DEFINITION: Degree of broadcast-journalistic cadence: projected, rhythmic, controlled and ostensibly objective. +LISTEN FOR: Listen for anchor-like diction, phrase-final falls, punchy segmentation, neutralized accent and broadcast projection. +DO NOT CONFUSE WITH: Do not score ordinary clear formal speech high unless it specifically resembles news/reporting cadence. +BUCKETS: + 0: Rambling/unstructured antithesis of news delivery. + 1: Ordinary conversational telling. + 2: Loose commentary/blogger style. + 3: Recognizable field-reporter cadence. + 4: Polished studio anchor delivery. + 5: Heavy classic-news gravitas and “voice of truth” authority. + 6: Caricatured old-school anchorman cadence with exaggerated broadcast mannerisms. + +S_TECH — Teacher/Didactic Style +DEFINITION: Degree of instructional/explanatory delivery designed to make information understandable. +LISTEN FOR: Listen for stepwise structure, deliberate clarification, pacing adjustments, checks for understanding and pedagogical emphasis. +DO NOT CONFUSE WITH: Do not equate any technical topic with didactic style; score how it is delivered, not subject matter. +BUCKETS: + 0: Obscure/rushed/confusing antithesis. + 1: Little adaptation for learner understanding. + 2: Dry lecture/data-dump tendencies. + 3: Good clear explainer/tutorial style. + 4: Strong explicit step-by-step instruction. + 5: Very pedagogical, potentially over-simplifying/patronizing. + 6: Maximum teaching affectation with exaggerated clarity/checking and over-explanation. + +S_AUTH — Authoritative Style +DEFINITION: Degree of commanding leadership delivery that expects compliance or acceptance. +LISTEN FOR: Listen for final cadences, projection, imperative phrasing, low uncertainty and power-bearing vocal posture. +DO NOT CONFUSE WITH: Separate from STNC: this is style resemblance to command/leadership, whereas STNC is the social floor relationship. +BUCKETS: + 0: Strongly submissive/non-commanding antithesis. + 1: Very unsure and low-authority. + 2: Peer-level suggestion more than command. + 3: Manager-like firm expectation. + 4: Commander/police/military-style direct authority. + 5: Highly dominant rally/leader delivery. + 6: Maximum drill-sergeant/tyrannical command style. + +S_PLAY — Playful Style +DEFINITION: Degree of humorous, mischievous, lighthearted fun in the delivery. +LISTEN FOR: Listen for smile voice, bouncing timing, joke cadence, giggle leakage, teasing melody and delight-driven unpredictability. +DO NOT CONFUSE WITH: Playfulness can coexist with sarcasm/teasing or low literal semantic positivity; score the performance quality. +BUCKETS: + 0: Grim/solemn antithesis. + 1: Serious with virtually no playfulness. + 2: Dry/deadpan trace of humor. + 3: Light friendly banter/smile. + 4: Clearly joking and entertainment-oriented. + 5: Mischievous/prankster-like, giggly and energetic. + 6: Whimsical/zany/manic-playful extreme. + +S_CART — Cartoonish Style +DEFINITION: Degree of exaggerated character-voice / animation-like vocalization. +LISTEN FOR: Listen for physics-stretching pitch/timbre shifts, funny noises, caricatured resonance and overt “put-on” character voice. +DO NOT CONFUSE WITH: Do not score a naturally unusual voice as cartoonish unless the delivery itself is stylized/exaggerated. +BUCKETS: + 0: Hyper-naturalistic/realist antithesis. + 1: Ordinary natural human speech. + 2: Slight character/impression coloration. + 3: Recognizable animated/sitcom character voice. + 4: Wacky exaggerated animation delivery. + 5: Zany extreme vocal elasticity and sound-play. + 6: Looney-Tunes-like sound-effect voice at caricature maximum. + +S_ASMR — ASMR Style +DEFINITION: Degree of close, quiet, texture-focused ASMR intimacy. +LISTEN FOR: Listen for low volume, close-mic breath/mouth detail, deliberate soothing pacing and sensory texture emphasis. +DO NOT CONFUSE WITH: Whispering alone is not enough: S_WHIS measures whisper phonation; S_ASMR measures the broader intimate sensory style. +BUCKETS: + 0: Shouted/distant antithesis. + 1: Normal/loud delivery with no ASMR intent. + 2: Quiet speech with only slight intimacy. + 3: Soft-spoken soothing ASMR-adjacent delivery. + 4: Clear close-mic whisper ASMR. + 5: Strong deliberate mouth/breath texture for tingles. + 6: Extreme invasive/binaural “inside the head” sensory intimacy. + +S_WHIS — Whisper-Talk Style +DEFINITION: Degree of reduced vocal-fold vibration and whisper-based lexical delivery. +LISTEN FOR: Listen to voiced-to-unvoiced ratio, turbulent air and whether words are produced in full voice, breathy half-voice or true whisper. +DO NOT CONFUSE WITH: Do not count a whispered word as a VocalBurst; this is a speaking-style dimension. +BUCKETS: + 0: Fully voiced, resonant projection. + 1: Hushed low volume but still fully voiced. + 2: Breathy half-voice / bedroom-like tone. + 3: Stage whisper intended to carry. + 4: Standard quiet whisper. + 5: Raspy/tensed aggressive whisper. + 6: Near-completely unvoiced ghostly air articulation. + +S_RANT — Ranting/Angry Style +DEFINITION: Degree of sustained grievance-driven, high-energy complaint or tirade delivery. +LISTEN FOR: Listen for uninterrupted complaint momentum, repetitive stress, escalating cadence, interruptions, high volume or tightly controlled shaking rage. +DO NOT CONFUSE WITH: ANGER is emotion; S_RANT is a discourse/performance style. A rant can be comedic or performed, and anger can be expressed without ranting. +BUCKETS: + 0: Diplomatic conflict-resolution antithesis. + 1: Calm disagreement. + 2: Annoyed/passive-aggressive complaint. + 3: Recognizable venting to another person. + 4: Heated argument-like ranting. + 5: Full tirade with building rhythmic grievance. + 6: Unhinged/manic grievance extreme near vocal/physical breakdown. + +S_STRY — Storytelling Style +DEFINITION: Degree of oral-storytelling craft: narrative arc, suspense, pacing and character differentiation. +LISTEN FOR: Listen for suspense pauses, scene-setting cadence, character voices, rhythmic lilt and deliberate payoff structure. +DO NOT CONFUSE WITH: S_STRY is not simply STRU; a technically structured explanation is not necessarily a story. +BUCKETS: + 0: Data-entry/non-narrative antithesis. + 1: Plain chronological reporting. + 2: Casual anecdote. + 3: Engaging campfire-style story with suspenseful timing. + 4: Performed storytelling with character differentiation. + 5: Highly stylized fantasy/epic story mode. + 6: Bardic/hypnotic archetype with musical oral-tradition cadence. + +S_MONO — Monologue Style +DEFINITION: Degree of self-contained solo/soliloquy delivery rather than interactive dialogue. +LISTEN FOR: Listen for lack of turn invitation, inward continuity, sustained one-person thought and voiceover/diary/soliloquy qualities. +DO NOT CONFUSE WITH: Do not confuse with STNC 6 domination: a quiet intimate diary can be strongly monologic without being dominant. +BUCKETS: + 0: Clear dialogue orientation with active response expectation. + 1: Public address to others rather than private monologue. + 2: Thinking aloud while acting/working. + 3: Diary-like intimate self-contained speech. + 4: Theatrical soliloquy processing emotion alone. + 5: Cinematic internal/voiceover narration. + 6: Maximum stylized noir-like inner monologue, acoustically self-contained from the social world. + +================================================================================ +4. EMONET — 40 REPO-ALIGNED EMOTION CATEGORIES, SCORED 0–4 +================================================================================ +EmoNet is intrinsically multi-label. The LAION taxonomy defines each category through a near-synonym cluster; this inference manual adds a user-requested 0–4 acoustic intensity rubric for each category. +GLOBAL INTENSITY: 0=absent/no convincing acoustic evidence; 1=slight/trace; 2=moderate/clearly present but not dominant; 3=strong/prominent and strongly colors the performance; 4=extreme/overwhelming/defining. +Score vocal expression, not merely the literal semantic content. Multiple categories may be high simultaneously. Do not normalize scores or force a single winner. A burst may support an emotion but does not mechanically determine it. + +amusement — Amusement + TAXONOMY CLUSTER: lighthearted fun; mirth; joviality; laughter; playfulness; silliness; jesting. + ACOUSTIC INTERPRETATION: Hear smile/laughter leakage, buoyant rhythm, playful timing and light social energy. Distinguish from Elation: amusement is specifically mirth/joking rather than general high happiness. + Apply the shared 0–4 intensity scale independently to this category. + +elation — Elation + TAXONOMY CLUSTER: happiness; excitement; joy; exhilaration; delight; jubilation; bliss; cheerfulness. + ACOUSTIC INTERPRETATION: Look for activated positive energy, lifted pitch/tempo and celebratory projection. Distinguish from Contentment, which is calmer, and from Triumph, which carries victory/dominance. + Apply the shared 0–4 intensity scale independently to this category. + +pleasure_ecstasy — Pleasure/Ecstasy + TAXONOMY CLUSTER: ecstasy; pleasure; bliss; rapture; beatitude. + ACOUSTIC INTERPRETATION: Listen for intense savoring/pleasure, often inward, breathy or physically released rather than merely cheerful. It may be low or high arousal depending on the clip. + Apply the shared 0–4 intensity scale independently to this category. + +contentment — Contentment + TAXONOMY CLUSTER: contentment; relaxation; peacefulness; calmness; satisfaction; ease; serenity; fulfillment; gladness; lightness; tranquility. + ACOUSTIC INTERPRETATION: Positive low-arousal ease: settled rhythm, soft warmth, relaxed breath/tension and lack of urgent striving. + Apply the shared 0–4 intensity scale independently to this category. + +gratitude — Thankfulness/Gratitude + TAXONOMY CLUSTER: thankfulness; gratitude; appreciation; gratefulness. + ACOUSTIC INTERPRETATION: Sincere appreciative warmth, often with softened tempo/emphasis and humble interpersonal stance. Do not infer gratitude merely from saying “thank you” if delivery is flat/sarcastic. + Apply the shared 0–4 intensity scale independently to this category. + +affection — Affection + TAXONOMY CLUSTER: sympathy; compassion; warmth; trust; caring; clemency; forgiveness; devotion; tenderness; reverence. + ACOUSTIC INTERPRETATION: Tender, caring warmth toward another: softened consonants, gentle melodic curves, intimate resonance and prosodic protection. + Apply the shared 0–4 intensity scale independently to this category. + +infatuation — Infatuation + TAXONOMY CLUSTER: infatuation; crush; romantic desire; fondness; butterflies; adoration. + ACOUSTIC INTERPRETATION: Romantic excited fondness: warmth plus nervous activation, breathiness, pitch lift or shy instability. Distinguish from general Affection by romantic/crush energy. + Apply the shared 0–4 intensity scale independently to this category. + +hope_optimism — Hope/Optimism + TAXONOMY CLUSTER: hope; enthusiasm; optimism; anticipation; courage; encouragement; zeal; fervor; inspiration; determination. + ACOUSTIC INTERPRETATION: Forward-looking positive expectancy: rising contours, energized but not necessarily jubilant tone, resilient/encouraging projection. + Apply the shared 0–4 intensity scale independently to this category. + +triumph — Triumph + TAXONOMY CLUSTER: triumph; superiority. + ACOUSTIC INTERPRETATION: Victory/success energy, often expansive, chest-forward and assertive with strong emphasis or celebratory release. + Apply the shared 0–4 intensity scale independently to this category. + +pride — Pride + TAXONOMY CLUSTER: pride; dignity; self-confidence; honor; self-consciousness. + ACOUSTIC INTERPRETATION: Self-assured dignity or self-satisfaction, often controlled and upright rather than explosively celebratory. Can shade into arrogance without becoming contempt. + Apply the shared 0–4 intensity scale independently to this category. + +interest — Interest + TAXONOMY CLUSTER: interest; fascination; curiosity; intrigue. + ACOUSTIC INTERPRETATION: Forward attentive curiosity: perked-up energy, listener/topic engagement, exploratory intonation and increased focus. + Apply the shared 0–4 intensity scale independently to this category. + +awe — Awe + TAXONOMY CLUSTER: awe; awestruck; wonder. + ACOUSTIC INTERPRETATION: Profound impressed wonder/reverence, often with breathiness, slowed spacing, widened pitch or momentary speech reduction. Distinguish from Surprise by sustained wonder rather than sudden startle. + Apply the shared 0–4 intensity scale independently to this category. + +astonishment_surprise — Astonishment/Surprise + TAXONOMY CLUSTER: astonishment; surprise; amazement; shock; startlement. + ACOUSTIC INTERPRETATION: Sudden unexpected reaction: abrupt pitch spike, gasp, freeze, attack change or rapid prosodic reset. Can be positive, negative or neutral. + Apply the shared 0–4 intensity scale independently to this category. + +concentration — Concentration + TAXONOMY CLUSTER: concentration; deep focus; engrossment; absorption; attention. + ACOUSTIC INTERPRETATION: Task-focused mental engagement: reduced social ornament, controlled rhythm and purposeful attentional narrowing. + Apply the shared 0–4 intensity scale independently to this category. + +contemplation — Contemplation + TAXONOMY CLUSTER: contemplation; thoughtfulness; pondering; reflection; meditation; brooding; pensiveness. + ACOUSTIC INTERPRETATION: Reflective inward thinking: slower pace, pauses, lowered energy and thoughtful phrasing. Distinguish from Concentration by reflective/pondering rather than task execution. + Apply the shared 0–4 intensity scale independently to this category. + +relief — Relief + TAXONOMY CLUSTER: relief; respite; alleviation; solace; comfort; liberation. + ACOUSTIC INTERPRETATION: Release after prior tension: audible exhale/sigh, reduction in tension/arousal, softened articulation and emotional opening. + Apply the shared 0–4 intensity scale independently to this category. + +longing — Longing + TAXONOMY CLUSTER: yearning; longing; pining; wistfulness; nostalgia; craving; desire; envy; homesickness; saudade. + ACOUSTIC INTERPRETATION: Reaching/absent-object affect with sustained wistfulness, elongated vowels, breathy breaks and bittersweet valence. + Apply the shared 0–4 intensity scale independently to this category. + +teasing — Teasing + TAXONOMY CLUSTER: teasing; bantering; playful mocking; ribbing; lightly provoking. + ACOUSTIC INTERPRETATION: Playful social challenge, often sing-song, ironic or knowingly exaggerated. Distinguish from Contempt: teasing retains affiliative/playful energy unless clearly hostile. + Apply the shared 0–4 intensity scale independently to this category. + +impatience_irritability — Impatience and Irritability + TAXONOMY CLUSTER: impatience; irritability; irritation; restlessness; short-temperedness; exasperation. + ACOUSTIC INTERPRETATION: Restless negative urgency: clipped words, sharp sighs, faster tempo, tense attacks and reduced tolerance. + Apply the shared 0–4 intensity scale independently to this category. + +sexual_lust — Sexual Lust + TAXONOMY CLUSTER: sexual lust; carnal desire; lust; horny; turned on. + ACOUSTIC INTERPRETATION: Sensual sexual desire/arousal when acoustically supported: breathy close delivery, slowed/savoring rhythm, intimate fry/proximity and physiological arousal. Never infer solely from gender or neutral affectionate content. + Apply the shared 0–4 intensity scale independently to this category. + +doubt — Doubt + TAXONOMY CLUSTER: doubt; distrust; suspicion; skepticism; uncertainty; pessimism. + ACOUSTIC INTERPRETATION: Withholding belief: hesitant cadence, pulled-back projection, skeptical intonation, narrowed confidence and potential vocal fry. + Apply the shared 0–4 intensity scale independently to this category. + +fear — Fear + TAXONOMY CLUSTER: fear; terror; dread; apprehension; alarm; horror; panic; nervousness. + ACOUSTIC INTERPRETATION: Threat response: trembling, breathiness, constricted high pitch, shallow breathing, freezing or panic acceleration depending on intensity. + Apply the shared 0–4 intensity scale independently to this category. + +distress — Distress + TAXONOMY CLUSTER: worry; anxiety; unease; anguish; trepidation; concern; upset; pessimism; foreboding. + ACOUSTIC INTERPRETATION: Broader anxious suffering than acute fear: persistent strain, worry, unsettled rhythm and inability to relax. + Apply the shared 0–4 intensity scale independently to this category. + +confusion — Confusion + TAXONOMY CLUSTER: confusion; bewilderment; flabbergasted; disorientation; perplexity. + ACOUSTIC INTERPRETATION: Failure to make sense of information: irregular questioning intonation, broken phrasing, searching rhythm and uncertain emphasis. + Apply the shared 0–4 intensity scale independently to this category. + +embarrassment — Embarrassment + TAXONOMY CLUSTER: embarrassment; shyness; mortification; discomfiture; awkwardness; self-consciousness. + ACOUSTIC INTERPRETATION: Social awkwardness/self-consciousness: nervous laugh, reduced volume, hesitant starts and “hiding” delivery. + Apply the shared 0–4 intensity scale independently to this category. + +shame — Shame + TAXONOMY CLUSTER: shame; guilt; remorse; humiliation; contrition. + ACOUSTIC INTERPRETATION: Deeper self-condemning collapse than embarrassment: lowered/withdrawn voice, reluctance, subdued resonance and remorseful heaviness. + Apply the shared 0–4 intensity scale independently to this category. + +disappointment — Disappointment + TAXONOMY CLUSTER: disappointment; regret; dismay; letdown; chagrin. + ACOUSTIC INTERPRETATION: Deflation after unmet expectation: energy/pitch drop, sighing, flattened positive affect and resigned phrasing. + Apply the shared 0–4 intensity scale independently to this category. + +sadness — Sadness + TAXONOMY CLUSTER: sadness; sorrow; grief; melancholy; dejection; despair; self-pity; sullenness; heartache; mournfulness; misery. + ACOUSTIC INTERPRETATION: Sorrow/grief spectrum: reduced energy, darker resonance, descending pitch, heavy pacing, tears/sobs or fragile voice at stronger levels. + Apply the shared 0–4 intensity scale independently to this category. + +bitterness — Bitterness + TAXONOMY CLUSTER: resentment; acrimony; bitterness; cynicism; rancor. + ACOUSTIC INTERPRETATION: Sustained resentful hurt: dry/hard articulation, cool hostility, controlled tension and long-lived grievance rather than explosive anger. + Apply the shared 0–4 intensity scale independently to this category. + +contempt — Contempt + TAXONOMY CLUSTER: contempt; disapproval; scorn; disdain; loathing; detestation. + ACOUSTIC INTERPRETATION: Cold social devaluation/superiority: sneer-like nasal coloration, dismissive flatness, slow controlled delivery and lack of affiliative warmth. + Apply the shared 0–4 intensity scale independently to this category. + +disgust — Disgust + TAXONOMY CLUSTER: disgust; revulsion; repulsion; abhorrence; loathing. + ACOUSTIC INTERPRETATION: Visceral rejection/repulsion: gag-like/thickened mouth-throat behavior, recoiling prosody and strong aversive coloration. + Apply the shared 0–4 intensity scale independently to this category. + +anger — Anger + TAXONOMY CLUSTER: anger; rage; fury; hate; irascibility; enragement; vexation; wrath; peevishness; annoyance. + ACOUSTIC INTERPRETATION: Hot hostile activation: hard attacks, raised force, compressed or shouted phrases, roughness and aggressive emphasis; can also appear as tightly controlled quiet rage. + Apply the shared 0–4 intensity scale independently to this category. + +malevolence_malice — Malevolence/Malice + TAXONOMY CLUSTER: spite; sadism; malevolence; malice; desire to harm; schadenfreude. + ACOUSTIC INTERPRETATION: Harm-oriented sinister intent: cold threat, predatory calm, cruel pleasure or smiling hostility. Distinguish from Anger, which need not imply enjoyment/intention of harm. + Apply the shared 0–4 intensity scale independently to this category. + +sourness — Sourness + TAXONOMY CLUSTER: sourness; tartness; acidity; acerbity; sharpness. + ACOUSTIC INTERPRETATION: Acerbic pinched displeasure: complaining/nagging sharpness, nasal/strained tone and low-grade hostile acidity. + Apply the shared 0–4 intensity scale independently to this category. + +pain — Pain + TAXONOMY CLUSTER: physical pain; suffering; torment; ache; agony. + ACOUSTIC INTERPRETATION: Body suffering evidenced by bracing, strained/broken pitch, breath holding, sharp exhalation, moans/groans or involuntary tension. Do not infer physical pain from words alone. + Apply the shared 0–4 intensity scale independently to this category. + +helplessness — Helplessness + TAXONOMY CLUSTER: helplessness; powerlessness; desperation; submission. + ACOUSTIC INTERPRETATION: Perceived inability to act: small fading voice, trailing endings, weak attack, surrender/resignation or desperate pleading. + Apply the shared 0–4 intensity scale independently to this category. + +fatigue_exhaustion — Fatigue/Exhaustion + TAXONOMY CLUSTER: fatigue; exhaustion; weariness; lethargy; burnout. + ACOUSTIC INTERPRETATION: Depleted physiological energy: slow tempo, low volume, slurred articulation, fry, yawning or shortened support. + Apply the shared 0–4 intensity scale independently to this category. + +emotional_numbness — Emotional Numbness + TAXONOMY CLUSTER: numbness; detachment; insensitivity; emotional blunting; apathy; existential void; boredom; stoicism; indifference. + ACOUSTIC INTERPRETATION: Reduced reactive affect: flat energy, empty resonance, low volatility and emotionally disconnected response. Distinguish from calm contentment by lack of positive ease. + Apply the shared 0–4 intensity scale independently to this category. + +intoxication_altered_states — Intoxication/Altered States + TAXONOMY CLUSTER: drunk; stupor; intoxication; disorientation; altered perception. + ACOUSTIC INTERPRETATION: Altered coordination/perception cues such as slurring, unstable pitch/volume, elongated vowels or disorganized rhythm. Do not claim a substance cause when the acoustics could have another explanation. + Apply the shared 0–4 intensity scale independently to this category. + +jealousy_envy — Jealousy & Envy + TAXONOMY CLUSTER: jealousy; envy; covetousness. + ACOUSTIC INTERPRETATION: Resentful comparison/possessiveness: mixture of hurt, longing and irritation, often tight quiet intensity rather than pure rage. + Apply the shared 0–4 intensity scale independently to this category. + +================================================================================ +5. GENUINENESS — 0–6, REPO-ALIGNED +================================================================================ +DEFINITION: +Estimate how convincingly the clip behaves like a real, lived-in spoken moment rather than a rehearsed, flat, synthetic, over-smoothed, or mechanically assembled read. The source model card explicitly treats Genuineness as believability/lived-in delivery driven by natural timing, breath, micro-imperfections, and emotional grounding. It is NOT audio fidelity. + +LISTEN FOR: +- locally plausible, non-gridlocked timing rather than random jitter; +- breaths that occur for understandable respiratory/phrase reasons and alter the next phrase naturally; +- microscopic hesitation, self-adjustment, coarticulatory messiness and timing asymmetry that feel causally tied to thought or physiology; +- emotion that changes articulation, timing, breath, resonance and emphasis coherently rather than sitting as a detached “style layer”; +- carry-over: after laughter, strain, a gasp, a smile, crying or a strong emphasis event, the voice should not unrealistically reset instantaneously; +- spontaneous phrase shaping and plausible local variation across repeated words/syntactic structures; +- consistency of speaker identity and room acoustics even when the performance changes. + +DO NOT REWARD AUTOMATICALLY: +- inserted “um/uh”, fake breaths, random stutters, noise, jitter or clipping; +- low recording quality; +- extreme emotion by itself; +- disfluency by itself. +An imperfection only supports Genuineness if it belongs organically to one continuous physical/cognitive performance. + +BUCKETS: +0: Strongly synthetic/rehearsed/wooden. Timing, breathing and affect feel assembled or read; the clip has little sense of contingent real-time human production. +1: Mostly artificial/read. Some human-like details may exist, but phrasing remains strongly preplanned, flat, over-smoothed or TTS-like. +2: Partly plausible but noticeably performed/synthetic. Several local details feel natural, yet the full performance does not consistently behave like one lived-in moment. +3: Mixed/ambiguous naturalness. The speech is plausibly human/spontaneous in meaningful stretches but also contains performed, overly regular or synthetic-feeling passages. +4: Generally natural and believable. Timing, breath and prosody show coherent micro-variation and most moments feel causally connected to human thought/physiology. +5: Strongly genuine. The performance feels unscripted/lived-in with convincing micro-imperfections, emotional grounding and natural carry-over between phrases. +6: Exceptionally genuine throughout. Virtually every timing, breath, affective and articulatory detail reads as one continuous spontaneous human act rather than a generated or rehearsed sequence. + +================================================================================ +6. VOCAL BURST DETECTION, TIMING AND REPO-ALIGNED TAXONOMY +================================================================================ +A VocalBurst is a temporally localized non-speech/paralinguistic human sound separable from ordinary fluent lexical speech. The LAION taxonomy contains 82 categories in 16 semantic families; four of those categories are explicitly hand/body sounds rather than vocal-tract sounds and therefore are listed for disambiguation but MUST NOT increase vocal_burst_count in this pipeline. + +DETECTION BOUNDARY: +- Count nonlexical laughter, crying/sobbing, salient breathing/sighing/gasping, groans/moans/grunts, throat events, humming, cough/hiccup/sniff/snort/yawn, human whistling, mouth/tongue/eating sounds, and screams/shrieks when they are distinct acoustic events. +- Do not count ordinary consonants/vowels, silent pauses, purely environmental noises, music, or lexical speech merely because it is loud. +- Ordinary lexical shouting belongs primarily in VoiceNet arousal/attack/style. Use Scream/Shriek only for a substantially nonlexical outburst or a clearly burst-like cry. +- Ordinary quiet inhalations needed for speech are not necessarily burst events. Count breathing when acoustically salient enough to function as a paralinguistic event. + +SEGMENTATION: +- Enumerate every distinct event chronologically; there is no artificial maximum. +- Treat one continuous laugh episode as one event even when it has several ha/giggle pulses, unless lexical speech or a clear reset separates episodes. +- Treat a compact cough fit as one event; clearly separated coughs can be separate events. +- Bursts may overlap lexical speech. Timing should cover the audible non-speech event and its physically continuous overlap, not unrelated preceding/following silence. +- start = first convincing onset; end = last convincing offset/recovery attributable to that event. Aim for practical ~0.05–0.10 s precision, not fake sample-level precision. + +CLASSIFICATION: +- fine MUST be one of the eligible vocal fine labels below. The four non-vocal Hand & Body labels are for recognition/disambiguation only and must not be emitted as counted vocal bursts. +- group23 MUST use the exact mapping shown alongside the fine label. This mapping follows the published 23-group normalization where a fine label is present in that mapping; for example Displeased Grunt maps to sigh_neg, and Cough/Coughing map to throat. +- If the audio is genuinely between two fine classes, choose the closest permitted label and make the ambiguity explicit in the standalone burst caption instead of inventing a new label. + +[Laughter] +- Breathy Giggle | group23=laugh_soft | Soft airy laugh with a breath-forward, lightly voiced quality. +- Cackle | group23=laugh_loud | Harsh, sharp, usually louder laugh that can sound abrasive or unsettling. +- Childlike Giggle | group23=laugh_soft | Light, playful, relatively high/bright giggle. +- Chuckle | group23=laugh_soft | Soft, restrained/suppressed laugh expressing mild amusement. +- Guffaw | group23=laugh_loud | Loud, open, unrestrained laugh of strong amusement. +- Nervous Giggle | group23=laugh_soft | High/tense giggle carrying anxiety or self-consciousness. +- Snicker | group23=laugh_soft | Stifled, often nasal or contained laugh associated with mockery/sarcasm. +- Snorting Giggle | group23=laugh_soft | Laughter containing a brief nasal snort component. + +[Crying & Distress] +- Convulsive Sob | group23=sob | Intense crying with heaving or spasmodic breath cycles. +- Mournful Wail | group23=wail | Long, sustained, often high or open cry of grief/distress. +- Quiet Sob | group23=sob | Soft crying with broken/spasmodic breaths at low intensity. +- Sobs | group23=sob | Audible heavier crying punctuated by gasps or breath catches. +- Trembling Whimper | group23=sob | Faint fragile cry with tremor, often fear/pain/submission-like. + +[Breathing] +- Deep Breath | group23=breath_calm | One large salient inhale/exhale used for reset, calming or refocusing. +- Deep Breathing | group23=breath_calm | Repeated slow deliberate large breaths. +- Fast Breathing | group23=breath_fast | Rapid repeated inhale/exhale pattern associated with activation/stress. +- Heavy Breathing | group23=breath_fast | Deep labored audible breathing associated with fatigue/tension. +- Normal Breathing | group23=breath_calm | Clearly audible but regular resting breath event. +- Panting | group23=breath_fast | Rapid rhythmic shallow breaths from exertion/excitement. +- Slow Breathing | group23=breath_calm | Deliberately paced controlled slow breaths. + +[Sighs] +- Contented Sigh | group23=sigh_pos | Deep exhale carrying satisfaction/settled pleasure. +- Exasperated Sigh | group23=sigh_neg | Audible release expressing frustration or impatience. +- Relief Sigh | group23=sigh_pos | Exhale marking release of prior tension/stress. +- Wistful Sigh | group23=sigh_pos | Soft exhale colored by longing/nostalgia. + +[Gasps & Inhales] +- Fearful Gasp | group23=gasp | Sudden constricted inhale associated with shock/anxiety/fear. +- Sharp Inhale | group23=gasp | Quick salient intake associated with realization/preparation. +- Surprised Gasp | group23=gasp | Quick intake of breath associated with astonishment/startle. + +[Groans & Moans] +- Exhausted Groan | group23=sigh_neg | Weary low vocal release associated with fatigue. +- Frustrated Groan | group23=sigh_neg | Deep vocal release associated with annoyance/frustration. +- Pain Moan | group23=moan | Low sustained vocalization signaling discomfort or bodily pain. +- Pleasure Moan | group23=moan | Soft prolonged vocalization associated with enjoyment/pleasure. + +[Grunts] +- Affirmative Grunt | group23=grunt | Short nonlexical grunt indicating agreement/acknowledgement. +- Displeased Grunt | group23=sigh_neg | Harsh short grunt expressing dissatisfaction; in the 23-group mapping this belongs to sigh_neg. +- Effort Grunt | group23=grunt | Short forceful vocalization accompanying strain/exertion. + +[Throat & Vocal Sounds] +- Ahem | group23=throat | Brief throat-clear-like event used to gain attention or signal discomfort. +- Clears Throat | group23=throat | Intentional throat-clearing sound. +- Growl | group23=growl | Low guttural rumble conveying threat/anger or characterful animal-like force. +- Gurgling | group23=misc_body | Bubbling throat/body sound; mapped to residual misc_body in the 23-group scheme. +- Hiss | group23=hiss | Sustained sibilant air/vocal sound often conveying disapproval/threat. +- Low Mumble | group23=hum | Indistinct low nonlexical or barely lexical murmur. +- Whispered Mumble | group23=hum | Very quiet muddled murmuring/whisper-like vocalization. + +[Humming] +- Humming | group23=hum | Continuous closed/partially closed-mouth tonal hum, tuneful or absent-minded. +- Purr | group23=hum | Soft continuous vibrating hum-like sound associated with contentment. +- Resonant Hum | group23=hum | Lower, fuller thoughtful/resonant hum. +- Soft Hum | group23=hum | Gentle low-energy closed-lip tonal hum. + +[Coughing / Nasal / Diaphragm] +- Cough | group23=throat | Single sharp expulsion of air from the lungs/throat. +- Coughing | group23=throat | Series/fit of forceful cough expulsions treated as one event when continuous. +- Hiccup | group23=hiccup | Single diaphragmatic spasm producing a hic-like transient. +- Hiccups | group23=hiccup | Repeated hiccup spasms. +- Sniff | group23=sniff | Audible nasal inhale, potentially restraint/sadness or functional clearing. +- Snort | group23=sniff | Quick nasal air burst often associated with disbelief/derision or laughter. +- Yawn | group23=yawn | Involuntary wide-mouth breathing/vocal event associated with tiredness. + +[Whistling] +- Person Whistling Playfully | group23=whistle | Lighthearted melodic human whistle. +- Person Whistling to Get Attention | group23=whistle | Intentional attention-getting human whistle. +- Sharp Whistle | group23=whistle | Brief high-pitched attention-grabbing whistle. +- Soft Whistle | group23=whistle | Gentle tuneful airflow whistle. +- Wolf Whistle | group23=whistle | Characteristic two-note admiration whistle. + +[Mouth & Lip Sounds] +- Blowing a Kiss | group23=mouth | Brief airy/lip event suggesting a blown kiss; note that some detector variants fold this non-speech gesture away. +- Kissing Noises | group23=mouth | Repeated gentle lip-compression/suction sounds. +- Kissing Sounds | group23=mouth | Soft repeated lip-contact/suction sounds. +- Lip Smack | group23=mouth | Single soft lip-pop from parting/compressing lips. +- Smack One's Lips | group23=mouth | Light lip pop often associated with dryness/taste anticipation. +- Smacks Lips | group23=mouth | Repeated lip-popping/smacking sequence. +- Licking Sound | group23=mouth | Brief tongue-contact licking noise. +- Spitting | group23=mouth | Forceful oral expulsion/spit-like event. +- Sucking Noise | group23=mouth | Audible suction/pulling mouth sound. + +[Tongue Clicks] +- Click One's Tongue | group23=mouth | Short tch/click formed by tongue-palate release. +- Clicks Tongue | group23=mouth | Distinct repeated/salient tongue-palate click. +- Tongue Click | group23=mouth | Percussive clack of tongue against palate. +- Tsk | group23=mouth | Tongue click used as reprimand/annoyance marker. + +[Eating & Drinking] +- Chewing Noises | group23=mouth | Crunching/moist mastication sounds from the mouth. +- Drinking Noises | group23=swallow | Audible liquid intake, often containing gulp/slurp texture. +- Gulps | group23=swallow | Short audible swallowing events. +- Nervous Gulp | group23=swallow | Audible swallow with anxiety-like context. +- Slurping Noises | group23=swallow | Wet sucking/slurping associated with liquid intake. +- Swallows | group23=swallow | Audible throat movement of swallowing. + +[Screams & Shrieks] +- Scream | group23=scream | Loud high-intensity nonlexical outburst of extreme emotion. +- Shriek | group23=scream | Shorter/sharper piercing cry, often fear/startle-like. + +[Hand & Body Sounds — TAXONOMY PRESENT BUT NOT VOCAL-BURST COUNT] +- Finger Snaps | group23=misc_body | Sharp hand/finger click; non-vocal. +- Hand Scratching Head | group23=misc_body | Soft hand/scalp rustling; non-vocal. +- Hand Slaps | group23=misc_body | Palm impact sound; non-vocal. +- Slap Face | group23=misc_body | Palm-to-face impact; non-vocal. + +VALID group23 VALUES ONLY: +laugh_soft, laugh_loud, scream, wail, sigh_neg, sigh_pos, gasp, breath_calm, breath_fast, sniff, hum, grunt, throat, hiccup, swallow, mouth, sob, moan, growl, hiss, yawn, whistle, misc_body. + +23-GROUP INTERPRETATION NOTES: +- laugh_soft groups restrained/giggle/snicker/chuckle-like laughter; laugh_loud groups cackle/guffaw/open laughter. +- wail remains separate from scream even though both can be intense; wail is grief/crying-oriented and typically more sustained/mournful. +- sigh_neg deliberately groups Exasperated Sigh, Frustrated Groan, Exhausted Groan and Displeased Grunt because they function as closely confusable negative-release events in the published grouping. +- sigh_pos groups Relief, Contented and Wistful Sigh. +- gasp groups the sharp/surprised/fearful intake family; breath_calm and breath_fast are sustained/repeated breathing-pattern groups. +- throat covers cough/coughing and throat-clearing/ahem-like events; hiccup is separate. +- mouth deliberately absorbs many lip/tongue/chewing-type transient labels; swallow covers gulp/drink/slurp/swallow-like events. +- misc_body is residual and includes some non-vocal/body events in the grouping file. In THIS pipeline, hand/body events are not counted as vocal bursts. + +================================================================================ +7. VOCAL-BURST BLEND — 0–10, REPO-ALIGNED +================================================================================ +If vocal_burst_count == 0, output null. +Otherwise estimate how naturally the non-verbal burst behavior forms one continuous performance with the surrounding speech. The source scorer contrasts an organic human reaction embedded in speech against events that sound spliced/pasted, robotic, stagey/synthetic, or emotionally mismatched. + +ANCHORS: +0: Disconnected. The event sounds pasted/spliced, robotic, contextually/emotionally wrong, or fails to function as a genuine integrated burst. +1: Extremely poor continuity. Hard discontinuity in room/timbre/physiology/emotion strongly suggests a separate inserted event. +2: Poor. Some contextual relation exists, but the transition and recovery are clearly unnatural or mismatched. +3: Weak. The burst belongs to the general scene yet still sounds isolated, edited or mechanically triggered. +4: Borderline. Mostly context-compatible but conspicuously performed/inserted; lead-in or recovery lacks natural carry-over. +5: Fits the context but sounds acted/stagey/synthetic rather than spontaneously arising; this is the central model-card anchor. +6: Good. Most transitions are plausible, though one or more cues still reveal performance/assembly. +7: Clearly integrated. Breath, timbre and affect connect before/after the burst with only minor artificiality. +8: Strongly organic. The burst grows naturally from the preceding state and leaves believable after-effects in subsequent speech. +9: Near-seamless. Identity, room, respiratory mechanics, emotion and prosody behave like one continuous physical event with only tiny doubts. +10: Fully organic. Indistinguishable from a natural human reaction embedded inside the spoken performance; this is the upper model-card anchor. + +EVALUATE THE WHOLE EVENT CHAIN: +- PRE-BURST LEAD-IN: Does smile/cry/tension/breath/pitch/articulation begin moving toward the burst before its onset where physiology would predict it? +- ONSET: Does the burst emerge from the same vocal tract and current breath state, or appear as a separately launched sample? +- IDENTITY CONTINUITY: Speaker timbre, resonance and recording space should remain coherent unless a second speaker is actually present. +- EMOTIONAL CONTINUITY: The event must make psychological/prosodic sense relative to the immediately surrounding speech. +- OVERLAP: Laughing/sobbing through words can be highly blended when the lexical articulation is physically altered by the burst rather than merely layered on top. +- RECOVERY: Strong laughter, sobs, gasps, coughing, etc. often leave residual breath, tension, smile-voice, pitch displacement or articulatory change. Unrealistic instant reset lowers blend. +- EDITING CONTINUITY: Hard cuts, mismatched room tone/reverb/noise, impossible loudness jumps, different spectral profile or detached timing lower blend. +- AMBIGUITY: If the detected “burst” may actually be a sound effect/background transient, mention that in the burst caption and lower blend when integration evidence is weak. + +For multiple bursts, output one global score weighted by perceptual salience and integration quality; do not take a blind arithmetic mean. + +================================================================================ +8. EACH BURST CAPTION MUST BE STANDALONE +================================================================================ +Each vocal_bursts[i].caption must independently describe: +- exactly what is heard; +- why it matches the selected fine class; +- pitch/roughness/breathiness/resonance/intensity/rhythm where relevant; +- immediate surrounding spoken context, quoting a short phrase when useful; +- whether burst overlaps speech or occurs in a pause; +- how it blends into and out of speech; +- whether it could be confused with background sound, sound effect, edit, or another burst; +- its emotional/performance function when evident. +Never say "another", "the previous burst", "the second laugh" or otherwise depend on other burst captions. + +Example: +A short, breathy chuckle begins under the tail of the phrase “that was a terrible idea,” with two suppressed voiceless-to-voiced pulses and a slight nasal edge; the preceding speech already carries a smile and the following words retain lighter articulation, so the laugh sounds organically embedded rather than pasted in, although its first pulse is faint enough to resemble a small nasal exhale. + +================================================================================ +9. TRANSCRIPT +================================================================================ +Produce a compact verbatim transcript sufficient for reconstruction. +- Preserve original language; do not translate. +- Ordinary punctuation. +- Use [unclear] only when genuinely unintelligible. +- Do not insert nonverbal tags everywhere; vocal_bursts array is authoritative for bursts. +- If no intelligible lexical speech, transcript may be "". + +================================================================================ +10. GLOBAL CAPTION +================================================================================ +caption must be standalone, dense, specific, and non-redundant. Cover: +- number of speakers if clear; +- perceived vocal age and vocal gender expression; +- language/accent; +- timbre: brightness, warmth, fullness, roughness, harmonicity, metallicity, resonance placement; +- tempo, chunking, smoothness, clarity, emphasis, disfluency; +- style, stance, focus; +- dominant emotions and temporal shifts; +- breathing, tension, cognitive-load cues when salient; +- important bursts in chronological relation to speech; +- recording quality/background/reverb/noise when salient; +- overall genuineness; +- burst integration quality. +Do not enumerate raw scores. Prefer perceptual language from which scores could be inferred. +Describe meaningful temporal order. Aim roughly 180–350 words for normal clips; longer only if the performance genuinely contains many events/changes. + +================================================================================ +11. DIRECTOR CAPTION +================================================================================ +director_caption must read like precise instructions from a voice director to an actor reproducing THIS clip. +Rules: +- Spoken words MUST be inside double quotation marks. +- Performance directions MUST be outside quotation marks. +- Preserve utterance order. +- Explain how each sentence/phrase is delivered: tempo, pauses, emphasis, pitch, loudness, tension, breath, resonance, emotional subtext, temporal shifts. +- Insert each vocal burst approximately where it occurs and describe how to execute it. +- Explicitly describe speech-burst overlap. +- Describe recovery/carry-over after major bursts. +- Do not invent stage actions, facial expressions, biography, scene facts or visual context. +- Do not translate. +- Preserve [unclear] rather than fabricating words. +- Do not include numeric taxonomy scores. + +Example style: +Begin in a restrained, slightly weary conversational register, keeping the first clause narrow in pitch and letting the final word soften: "I really thought this would work." Take a short audible inhale, allow irritation to enter the throat without fully raising volume, then stress the contrast word in "But apparently nobody checked." Let a brief exasperated sigh spill directly out of the final consonant rather than inserting a clean pause; recover with slightly breathier phonation and continue more quietly: "So now we start again." + +================================================================================ +12. LANGUAGE / ACCENT / AGE / GENDER CAUTION +================================================================================ +language: primary language name; substantial code-switching may use "German/English" etc. +accent: concise acoustically supported dialect/accent; if uncertain, use broad qualified label such as "British English (uncertain)" or "unclear". Never infer nationality. +AGEV/GEND are perceptual voice properties, not biological facts. In prose say "young-adult-sounding voice", "masculine-coded vocal presentation", etc. + +================================================================================ +13. IMPORTANT DISTINCTIONS / FAILURE MODES +================================================================================ +SMTH vs GENUINENESS: mathematically perfect smoothness can reduce genuineness; natural human speech often includes plausible micro-variation. +DFLU vs COGL: disfluency is audible linguistic disruption; cognitive load is apparent formulation effort. +AROU vs VALN: activation and positivity are independent. +VOLT vs ARSH/VALS: volatility is instability; ARSH/VALS are directional change. +RESP vs burst breathing: RESP is global; a salient gasp/deep breath may also be a timed burst. +ROUG vs distortion: roughness is biological; RCQL is technical. +HARM vs BRGT: periodicity/noise vs high-frequency energy. +FULL vs WARM: spectral size vs low-mid warmth. +STNC vs S_AUTH: social floor posture vs authoritative style resemblance. +S_NARR vs S_STRY: descriptive narration vs plot/suspense/character storytelling. +S_CONV vs S_MONO: response-inviting interaction vs self-contained one-person flow. +AMUSEMENT vs LAUGHTER: emotion vs acoustic event; either can exist without the other. +RELIEF vs Relief Sigh: emotion vs timed burst. +Pain Moan vs Pleasure Moan: use context, tension, valence, breath, pitch; if ambiguous choose nearest and state ambiguity. +Background noise vs burst: do not mislabel chair squeaks, car sounds, music or edits as human vocal bursts. +Shouting: lexical loud speech is not automatically a burst. +Whispered lexical speech: speech, not burst. +Multiple bursts: no artificial cap. + +================================================================================ +14. FINAL SILENT VALIDATION +================================================================================ +Before output, silently verify: +A. Entire clip listened to. +B. Exactly 57 numeric VoiceNet keys are present. All are integers; 55 use 0–6, bkgn uses 0–4, expl uses 0–2. +C. Exactly 40 EmoNet keys present, all integers 0–4. +D. genuineness integer 0–6. +E. Zero bursts => blend null, count 0, [] array. +F. Bursts => blend integer 0–10; count equals array length; chronological indices; start dict: + ladder=json.loads((LADDER_ROOT/'ladder_aux_schema.json').read_text()) + p3=json.loads((P3_ROOT/'event_class_schema.json').read_text()) + counts=Counter() + for name,count in ladder['class_counts'].items(): + counts[ALIASES.get(name,name)]+=int(count) + p3_ids=np.load(P3_ROOT/'event_class_ids.npy',mmap_mode='r') + train_positions=np.load(P3_ROOT/'train_indices.npy',mmap_mode='r') + p3_train_counts=np.bincount(p3_ids[train_positions].ravel(), + minlength=len(p3['classes'])+1) + for raw_id,name in enumerate(p3['classes'],1): + counts[ALIASES.get(name,name)]+=int(p3_train_counts[raw_id]) + frequent=sorted(name for name,count in counts.items() if count>=min_events) + names=['Other / rare burst']+frequent + to_id={name:i for i,name in enumerate(names)} + def lookup(source): + result=np.zeros(len(source['classes'])+1,np.int64) + for raw_id,name in enumerate(source['classes'],1): + canonical=ALIASES.get(name,name) + result[raw_id]=to_id.get(canonical,0) + return result.tolist() + canonical_counts=Counter({name:0 for name in names}) + for name,count in counts.items(): + canonical_counts[name if name in to_id else names[0]]+=count + weight=np.asarray([1/math.sqrt(max(1,canonical_counts[name])) for name in names],np.float32) + weight/=weight.mean() + weight=np.clip(weight,.25,4.) + return {'schema_version':'burst-canonical-v1','min_training_events':min_events, + 'names':names,'class_counts':dict(canonical_counts), + 'class_weights':weight.tolist(), + 'ladder_raw_to_canonical':lookup(ladder), + 'p3_raw_to_canonical':lookup(p3), + 'aliases':ALIASES,'raw_p3_classes':p3['classes'], + 'raw_ladder_classes':ladder['classes']} + + +class LadderAuxDataset(LadderAudioDataset): + def __init__(self, mean: np.ndarray, std: np.ndarray): + super().__init__(mean,std) + self.aux_path=LADDER_ROOT/'ladder_aux.npy' + self.aux=np.load(self.aux_path,mmap_mode='r') + if len(self.aux)!=len(self): + raise RuntimeError('Ladder auxiliary index has wrong row count') + + def __getstate__(self): + state=super().__getstate__() + state['aux']=None + return state + + def __setstate__(self,state): + self.__dict__.update(state) + self.aux=np.load(self.aux_path,mmap_mode='r') + + def __getitem__(self,index: int) -> dict: + result=super().__getitem__(index) + shard=bisect.bisect_right(self.ends,int(index)) + start=0 if shard==0 else int(self.ends[shard-1]) + row=self._row(shard,int(index)-start) + count=int(row['event_count']) + result['events_frames']=[(max(0,int(float(row['starts'][j])*50)), + min(1500,math.ceil(float(row['ends'][j])*50))) + for j in range(count)] + result['event_raw_class_ids']=np.asarray(self.aux['class_id'][index,:count],np.uint8) + result['cps_raw']=float(self.aux['cps'][index]) + return result + + +class LadderAuxVal(LadderVal): + def __init__(self,mean: np.ndarray,std: np.ndarray): + super().__init__(mean,std) + self.cps=np.load(LADDER_ROOT/'val_cps.npy',mmap_mode='r') + if len(self.cps)!=len(self): + raise RuntimeError('Ladder validation CPS length mismatch') + + def __getitem__(self,index: int) -> dict: + result=super().__getitem__(index) + result['cps_raw']=float(self.cps[index]) + result['events_frames']=[] + result['event_raw_class_ids']=np.zeros(0,np.uint8) + return result + + +class LayeredCollator(MixedCollator): + def __init__(self,model_path: Path,*,cps_mean: float,cps_std: float, + ladder_lookup: list[int],p3_lookup: list[int], + cps_min: float = .5,cps_max: float = 60.): + super().__init__(model_path) + self.cps_mean=cps_mean + self.cps_std=cps_std + self.cps_min=cps_min + self.cps_max=cps_max + self.ladder_lookup=np.asarray(ladder_lookup,np.int64) + self.p3_lookup=np.asarray(p3_lookup,np.int64) + + def __getstate__(self): + state=self.__dict__.copy() + state['extractor']=None + return state + + def __call__(self,items: list[dict]) -> dict: + result=super().__call__(items) + b=len(items) + n_frames=result['mel'].shape[-1]//2 + max_events=max(1,max(len(item.get('events_frames',item.get('events_samples',[]))) + for item in items)) + if max_events>MAX_EVENTS: + raise RuntimeError('too many events in one clip') + starts=np.zeros((b,max_events),np.int64) + ends=np.zeros((b,max_events),np.int64) + classes=np.zeros((b,max_events),np.int64) + event_valid=np.zeros((b,max_events),np.bool_) + event_span_valid=np.zeros((b,max_events),np.bool_) + cps=np.zeros(b,np.float32) + cps_raw=np.full(b,np.nan,np.float32) + cps_valid=np.zeros(b,np.bool_) + for i,item in enumerate(items): + enabled=item.get('enabled',True) + raw_cps=item.get('cps_raw') + if raw_cps is None: + raw_cps=item.get('auxiliary_targets',{}).get('characters_per_second') + if (enabled and raw_cps is not None and np.isfinite(raw_cps) + and self.cps_min<=float(raw_cps)<=self.cps_max): + cps_raw[i]=float(raw_cps) + cps[i]=(float(raw_cps)-self.cps_mean)/self.cps_std + cps_valid[i]=True + spans=(item['events_frames'] if 'events_frames' in item else + [(int(start)//320,(int(end)+319)//320) + for start,end in item.get('events_samples',[])]) + raw_ids=item.get('event_raw_class_ids') + if raw_ids is None: + raw_ids=np.zeros(len(spans),np.uint8) + if len(raw_ids)=len(lookup): + raise RuntimeError('event class ID exceeds vocabulary') + starts[i,j]=start + ends[i,j]=end + classes[i,j]=int(lookup[raw]) + event_span_valid[i,j]=bool(enabled and end>start) + event_valid[i,j]=bool(event_span_valid[i,j] and raw) + result.update(cps=torch.from_numpy(cps),cps_raw=torch.from_numpy(cps_raw), + cps_valid=torch.from_numpy(cps_valid), + event_starts=torch.from_numpy(starts), + event_ends=torch.from_numpy(ends), + event_classes=torch.from_numpy(classes), + event_valid=torch.from_numpy(event_valid), + event_span_valid=torch.from_numpy(event_span_valid)) + return result diff --git a/training/layered_multitask_model.py b/training/layered_multitask_model.py new file mode 100644 index 0000000000000000000000000000000000000000..9080bdb80574da8aa1a071f7dba4b7c3268c06db --- /dev/null +++ b/training/layered_multitask_model.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +"""Encoder-only Whisper with layer-mean residual experts and event-local classes.""" +from __future__ import annotations + +from pathlib import Path + +import torch +from safetensors import safe_open +from torch import nn +from torch.nn import functional as F +from transformers import WhisperConfig +from transformers.models.whisper.modeling_whisper import WhisperEncoder + + +SCORE_FAMILIES={ + 'emotion':(0,40,64), + 'voicenet':(40,97,32), + 'genuineness_blend_quality':(97,100,32), + 'empathic_plus':(100,119,64), + 'audiobox':(119,123,32), + 'dnsmos':(123,130,32), + 'burst_count':(130,131,32), + 'voiceclap_attributes':(131,192,64), +} + + +class LayerMixture(nn.Module): + """Project each layer's masked mean, then learn a sample-specific layer mix.""" + def __init__(self, dim: int, width: int, layers: int): + super().__init__() + self.project=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,width), + nn.GELU(),nn.Linear(width,width)) + self.gate=nn.Linear(width,1) + self.layer_bias=nn.Parameter(torch.zeros(layers)) + self.out_norm=nn.LayerNorm(width) + + def forward(self, means: torch.Tensor) -> torch.Tensor: + # means: [batch, encoder layers, hidden width] + z=self.project(means) + weights=(self.gate(z).squeeze(-1)+self.layer_bias).softmax(dim=1) + return self.out_norm((z*weights.unsqueeze(-1)).sum(dim=1)) + + +class LayeredMultiTaskWhisper(nn.Module): + def __init__(self, model_path: str | Path, n_event_classes: int, + *, initialize_pretrained: bool = True): + super().__init__() + model_path=Path(model_path) + config=WhisperConfig.from_pretrained(model_path,local_files_only=True) + self.encoder=WhisperEncoder(config) + if initialize_pretrained: + with safe_open(model_path/'model.safetensors',framework='pt',device='cpu') as file: + prefix='model.encoder.' + pretrained={name[len(prefix):]:file.get_tensor(name) for name in file.keys() + if name.startswith(prefix)} + self.encoder.load_state_dict(pretrained,strict=True) + self.hidden_dim=config.d_model + self.num_layers=config.encoder_layers + self.n_event_classes=n_event_classes + pooled_dim=config.d_model*4 + + def clip_head(inputs: int, outputs: int): + return nn.Sequential(nn.LayerNorm(inputs),nn.Linear(inputs,512), + nn.GELU(),nn.Linear(512,outputs)) + + # A direct final-layer route plus learned residuals from every layer. + self.score_head=clip_head(pooled_dim,192) + self.score_mix=nn.ModuleDict() + self.score_delta=nn.ModuleDict() + for family,(start,stop,width) in SCORE_FAMILIES.items(): + self.score_mix[family]=LayerMixture(config.d_model,width,self.num_layers) + self.score_delta[family]=nn.Linear(width,stop-start) + nn.init.zeros_(self.score_delta[family].weight) + nn.init.zeros_(self.score_delta[family].bias) + + self.timbre_mix=LayerMixture(config.d_model,128,self.num_layers) + self.identity_mix=LayerMixture(config.d_model,128,self.num_layers) + self.timbre_head=clip_head(pooled_dim+128,128) + self.identity_head=clip_head(pooled_dim+128,250) + self.cps_mix=LayerMixture(config.d_model,32,self.num_layers) + self.cps_head=clip_head(pooled_dim+32,1) + + self.frame_head=nn.Sequential(nn.LayerNorm(config.d_model), + nn.Linear(config.d_model,config.d_model//2), + nn.GELU(), + nn.Conv1d(config.d_model//2,config.d_model//2,7,padding=3), + nn.GELU(),nn.Conv1d(config.d_model//2,1,1)) + # Independent onset/duration proposals keep overlapping source events separate. + self.proposal_head=nn.Conv1d(config.d_model//2,2,3,padding=1) + self.event_mix=LayerMixture(config.d_model,64,self.num_layers) + self.event_head=nn.Sequential(nn.LayerNorm(config.d_model+64), + nn.Linear(config.d_model+64,256),nn.GELU(), + nn.Linear(256,n_event_classes)) + + @staticmethod + def _event_means(h: torch.Tensor, starts: torch.Tensor, + ends: torch.Tensor) -> torch.Tensor: + n=h.shape[1] + starts=starts.clamp(0,n) + ends=ends.clamp(0,n) + prefix=F.pad(h.float().cumsum(dim=1),(0,0,1,0)) + width=h.shape[-1] + left=prefix.gather(1,starts.unsqueeze(-1).expand(-1,-1,width)) + right=prefix.gather(1,ends.unsqueeze(-1).expand(-1,-1,width)) + return (right-left)/(ends-starts).clamp_min(1).unsqueeze(-1) + + def forward(self, mel: torch.Tensor, mel_mask: torch.Tensor, + event_starts: torch.Tensor | None = None, + event_ends: torch.Tensor | None = None, + *, predict_events: bool = False, + event_threshold: float = .5, + max_pred_events: int = 10) -> dict[str,torch.Tensor]: + enc=self.encoder + h=F.gelu(enc.conv1(mel)) + h=F.gelu(enc.conv2(h)).transpose(1,2) + n=h.shape[1] + h=h+enc.embed_positions(torch.arange(n,device=h.device)) + valid=torch.arange(n,device=h.device)[None,:]<((mel_mask.sum(1)+1)//2)[:,None] + attention=torch.zeros((len(h),1,1,n),device=h.device,dtype=h.dtype) + attention.masked_fill_(~valid[:,None,None,:],torch.finfo(h.dtype).min) + mask=valid.unsqueeze(-1) + count=mask.sum(1).clamp_min(1) + layer_means=[] + event_layers=[] + layer_frames=[] + if (event_starts is None)!=(event_ends is None): + raise ValueError('event starts and ends must be provided together') + if predict_events and event_starts is not None: + raise ValueError('Use ground-truth event spans or predicted spans, not both') + for layer in enc.layers: + h=layer(h,attention) + layer_means.append((h.float()*mask).sum(1)/count) + if event_starts is not None: + event_layers.append(self._event_means(h,event_starts,event_ends)) + elif predict_events: + layer_frames.append(h) + h=enc.layer_norm(h).float() + mean=(h*mask).sum(1)/count + var=((h-mean[:,None,:]).square()*mask).sum(1)/count + minimum=h.masked_fill(~mask,torch.inf).amin(1) + maximum=h.masked_fill(~mask,-torch.inf).amax(1) + pooled=torch.cat((mean,minimum,maximum,var.clamp_min(1e-8).sqrt()),dim=-1) + means=torch.stack(layer_means,dim=1) + + deltas=torch.cat([self.score_delta[family](self.score_mix[family](means)) + for family in SCORE_FAMILIES],dim=-1) + score=self.score_head(pooled)+deltas + timbre=F.normalize(self.timbre_head(torch.cat((pooled,self.timbre_mix(means)),dim=-1)).float(),dim=-1) + identity=F.normalize(self.identity_head(torch.cat((pooled,self.identity_mix(means)),dim=-1)).float(),dim=-1) + cps=self.cps_head(torch.cat((pooled,self.cps_mix(means)),dim=-1)).squeeze(-1) + x=self.frame_head[0](h) + x=self.frame_head[1](x) + x=self.frame_head[2](x).transpose(1,2) + frame=self.frame_head[3:](x).squeeze(1) + proposal=self.proposal_head(x) + onset=proposal[:,0,:] + log_duration=proposal[:,1,:] + output={'scores':score,'frame':frame,'timbre':timbre, + 'identity':identity,'cps':cps, + 'onset':onset,'log_duration':log_duration} + if predict_events: + if not 0=event_threshold)&valid[i] + padded=F.pad(probabilities[i],(1,1),value=-1.) + peaks=active&(probabilities[i]>=padded[:-2])&( + probabilities[i]>padded[2:]) + ranked=torch.nonzero(peaks).flatten().tolist() + ranked.sort(key=lambda start:float(probabilities[i,start]),reverse=True) + chosen=[] + for start in ranked: + if any(abs(start-old)<3 for old in chosen): + continue + chosen.append(start) + if len(chosen)>=max_pred_events: + break + row=[] + for start in chosen: + duration=int(round(float(log_duration[i,start].float().clamp(0,8).expm1()))) + end=min(int(valid[i].sum()),start+max(1,duration)) + row.append((start,end)) + spans.append(sorted(row)) + width=max(1,max(len(row) for row in spans)) + event_starts=torch.zeros((len(h),width),device=h.device,dtype=torch.int64) + event_ends=torch.zeros_like(event_starts) + event_valid=torch.zeros_like(event_starts,dtype=torch.bool) + for i,row in enumerate(spans): + for j,(start,end) in enumerate(row): + event_starts[i,j]=start + event_ends[i,j]=end + event_valid[i,j]=True + event_layers=[self._event_means(layer,event_starts,event_ends) + for layer in layer_frames] + output.update(predicted_event_starts=event_starts, + predicted_event_ends=event_ends, + predicted_event_valid=event_valid) + if event_starts is not None: + local=self._event_means(h,event_starts,event_ends) + per_layer=torch.stack(event_layers,dim=2) + b,e,l,d=per_layer.shape + mixed=self.event_mix(per_layer.reshape(b*e,l,d)).reshape(b,e,-1) + output['event_class']=self.event_head(torch.cat((local,mixed),dim=-1)) + return output diff --git a/training/logs/base_COMPLETE.json b/training/logs/base_COMPLETE.json new file mode 100644 index 0000000000000000000000000000000000000000..6ea6501fc82e251eb7a32763c5d52189c4efeb95 --- /dev/null +++ b/training/logs/base_COMPLETE.json @@ -0,0 +1,5 @@ +{ + "epochs": 2, + "best_validation_loss": 1.7683769464492798, + "updates": 914 +} diff --git a/training/logs/base_epochs.jsonl b/training/logs/base_epochs.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4456b1207f60387326f91739ae9a78ddf32dc2ef --- /dev/null +++ b/training/logs/base_epochs.jsonl @@ -0,0 +1,2 @@ +{"epoch": 1, "update": 457, "validation_loss": 1.8121166229248047, "best": true} +{"epoch": 2, "update": 914, "validation_loss": 1.7683769464492798, "best": true} diff --git a/training/logs/small_COMPLETE.json b/training/logs/small_COMPLETE.json new file mode 100644 index 0000000000000000000000000000000000000000..6c93cb04c45f791b1fcf0ae69621589e80f8ba01 --- /dev/null +++ b/training/logs/small_COMPLETE.json @@ -0,0 +1,5 @@ +{ + "epochs": 2, + "best_validation_loss": 1.6989245414733887, + "updates": 914 +} diff --git a/training/logs/small_epochs.jsonl b/training/logs/small_epochs.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2a962a8de08621dc91fd6b67473848675d8f7235 --- /dev/null +++ b/training/logs/small_epochs.jsonl @@ -0,0 +1,2 @@ +{"epoch": 1, "update": 457, "validation_loss": 1.744598150253296, "best": true} +{"epoch": 2, "update": 914, "validation_loss": 1.6989245414733887, "best": true} diff --git a/training/make_finetune_config.py b/training/make_finetune_config.py new file mode 100644 index 0000000000000000000000000000000000000000..cb5fc2b28cac014449b415a30e7beb2b90652f8f --- /dev/null +++ b/training/make_finetune_config.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 +"""Relocate recorded fine-tuning paths without changing hyperparameters.""" +import argparse +import json +from pathlib import Path + +parser = argparse.ArgumentParser(description=__doc__) +parser.add_argument('--model', choices=['base', 'small'], required=True) +parser.add_argument('--data-root', type=Path, required=True) +parser.add_argument('--initial-checkpoint', type=Path, required=True) +parser.add_argument('--output-dir', type=Path, required=True) +parser.add_argument('--config', type=Path, required=True) +args = parser.parse_args() +training = Path(__file__).resolve().parent +release = training.parent +cfg = json.loads((training / 'configs' / (args.model + '_recorded_run.json')).read_text()) +cfg.update(checkpoint=str(args.initial_checkpoint.resolve()), model_config=str((release / args.model).resolve()), + data_root=str(args.data_root.resolve()), output=str(args.output_dir.resolve())) +for key in ['world', 'updates', 'train_clips', 'validation_clips', 'event_class_counts', + 'trainable_encoder_parameters', 'trainable_total_parameters', 'checkpoint_sha256', 'selection']: + cfg.pop(key, None) +if not (args.data_root / 'TRAINING_READY.json').exists(): + raise RuntimeError('Provide the complete prepared target/sidecar dataset, including TRAINING_READY.json') +if not args.initial_checkpoint.exists(): + raise FileNotFoundError(args.initial_checkpoint) +args.config.parent.mkdir(parents=True, exist_ok=True) +args.config.write_text(json.dumps(cfg, indent=2) + '\n') +print(args.config) diff --git a/training/multitask_p3_smoke.py b/training/multitask_p3_smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..7137798ca4d4538979ba8ebe7e514d5918e62324 --- /dev/null +++ b/training/multitask_p3_smoke.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +"""One-GPU Tiny/Base P3 smoke test for clip, frame and Orange objectives. + +The 100-example pilot is too small for an accuracy claim. This checks actual +pretrained encoder loading, masked sidecar joins, gradients and holdout metrics. +""" +from __future__ import annotations + +import argparse +import io +import json +import math +import os +import tarfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from safetensors import safe_open +from torch import nn +from torch.nn import functional as F +from torch.utils.data import DataLoader, Dataset +from transformers import WhisperConfig, WhisperFeatureExtractor +from transformers.models.whisper.modeling_whisper import WhisperEncoder + +from p3_target_adapter import SIDECARS, adapt, json_members, rasterize_burst_frames, target_names +from train import snapshot + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3/pilot') +SIDECAR_PATHS = {name: ROOT / f'p3-pilot-{name}-sidecars-00000.tar' for name in SIDECARS} +GROUPS = { + 'emotion': (0, 40, 1.0), 'voicenet': (40, 97, 1.0), + 'other': (97, 100, .3), 'eiv_extra': (100, 119, .5), + 'audiobox': (119, 123, .4), 'dnsmos': (123, 130, .4), + 'burst_count': (130, 131, .3), + 'voiceclap_attributes': (131, 192, .15), +} + + +def tar_payloads(path: Path, suffix: str) -> dict[str, bytes]: + result = {} + with tarfile.open(path, 'r:') as tf: + for member in tf: + if member.isfile() and member.name.endswith(suffix): + result[member.name[:-len(suffix)]] = tf.extractfile(member).read() + return result + + +def make_rows(audio_tar: Path, sidecar_paths: dict[str, Path], + locator_path: Path) -> list[dict]: + audio_meta = json_members(audio_tar, '.json') + audio = tar_payloads(audio_tar, '.opus') + side = {name: json_members(path, suffix) for name, (path, suffix) in + ((name, (sidecar_paths[name], ext)) for name, ext in SIDECARS.items())} + timbre = tar_payloads(sidecar_paths['orange'], '.orange_timbre.npy') + identity = tar_payloads(sidecar_paths['orange'], '.orange_identity.npy') + locator = {r['sample_id']: r for r in json.loads(locator_path.read_text())['rows']} + keys = set(audio_meta) + if any(set(items) != keys for items in (audio, *side.values(), timbre, identity, locator)): + raise RuntimeError('pilot TARs have different sample keys') + names = target_names() + example_scores = next(d['scores'] for d in side['empathic'].values() if d['scores']) + mapped = {('Jealousy_&_Envy' if n == 'emo_Jealousy_and_Envy' else n[4:]) + for n in names[:40]} + extra_names = sorted(set(example_scores) - mapped) + if len(extra_names) != 19: + raise RuntimeError(f'expected 19 EIV extras, found {len(extra_names)}') + attr_names = sorted(next(d['voiceclap_attributes'] for d in + side['voiceclap'].values() if d['voiceclap_attributes'])) + if len(attr_names) != 61: + raise RuntimeError(f'expected 61 VoiceCLAP attributes, found {len(attr_names)}') + rows = [] + for key in sorted(keys): + wav, sr = sf.read(io.BytesIO(audio[key]), dtype='float32') + if sr != 16000 or wav.ndim != 1: + raise RuntimeError(f'{key}: wrong audio format') + target = adapt(audio_meta[key], {kind: items[key] for kind, items in side.items()}, + names, extra_names, locator[key]) + score = ([target['ladder_scores'][name] for name in names] + + [target['extra_eiv_scores'][name] for name in extra_names] + + [target['audiobox_scores'][name] for name in ('CE', 'CU', 'PC', 'PQ')] + + [target['dnsmos_scores'][name] for name in + ('SIG_raw', 'BAK_raw', 'OVRL_raw', 'SIG', 'BAK', 'OVRL', 'P808_MOS')] + + [target['burst_count_log1p']] + + [target['voiceclap_attribute_scores'].get(name) + if target['voiceclap_attributes_valid_for_speech'] else None + for name in attr_names]) + if len(score) != 192: + raise RuntimeError(f'{key}: wrong target count {len(score)}') + frame, frame_mask = rasterize_burst_frames(target['burst_frame_target']) + rows.append({'key': key, 'audio': wav, + 'scores': np.array([np.nan if x is None else x for x in score], np.float32), + 'frame': np.array(frame, np.float32), + 'frame_mask': np.array(frame_mask, np.float32), + 'timbre': np.load(io.BytesIO(timbre[key])).astype(np.float32), + 'identity': np.load(io.BytesIO(identity[key])).astype(np.float32), + 'speaker_valid': target['orange_timbre']['valid_single_speaker']}) + return rows + + +class PilotData(Dataset): + def __init__(self, rows: list[dict], indices: list[int], mean: np.ndarray, std: np.ndarray): + self.rows, self.indices, self.mean, self.std = rows, indices, mean, std + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + row = self.rows[self.indices[idx]] + finite = np.isfinite(row['scores']) + target = np.where(finite, (row['scores'] - self.mean) / self.std, 0).astype(np.float32) + return row['audio'], target, finite, row['frame'], row['frame_mask'], \ + row['timbre'], row['identity'], row['speaker_valid'] + + +def collator(model_path: Path): + extractor = WhisperFeatureExtractor.from_pretrained(model_path, local_files_only=True) + + def collate(batch): + waves = [x[0] for x in batch] + feat = extractor(waves, sampling_rate=16000, padding='longest', truncation=True, + max_length=480000, return_attention_mask=True, return_tensors='np') + mel = feat['input_features'].astype(np.float32) + mask = feat['attention_mask'].astype(np.int64) + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + mask = np.pad(mask, ((0, 0), (0, 1))) + length = mel.shape[-1] // 2 + frame = np.zeros((len(batch), length), np.float32) + frame_mask = np.zeros_like(frame) + for i, item in enumerate(batch): + n = min(length, len(item[3])) + frame[i, :n] = item[3][:n] + frame_mask[i, :n] = item[4][:n] + return tuple(torch.from_numpy(x) for x in + (mel, mask, np.stack([x[1] for x in batch]), + np.stack([x[2] for x in batch]), frame, frame_mask, + np.stack([x[5] for x in batch]), + np.stack([x[6] for x in batch]), + np.array([x[7] for x in batch], np.bool_))) + return collate + + +class MultiTaskWhisper(nn.Module): + def __init__(self, model_path: Path): + super().__init__() + config = WhisperConfig.from_pretrained(model_path, local_files_only=True) + self.encoder = WhisperEncoder(config) + with safe_open(model_path / 'model.safetensors', framework='pt', device='cpu') as file: + prefix = 'model.encoder.' + state = {key[len(prefix):]: file.get_tensor(key) for key in file.keys() + if key.startswith(prefix)} + self.encoder.load_state_dict(state, strict=True) + dim = config.d_model + def clip_head(outputs): + return nn.Sequential(nn.LayerNorm(dim * 4), nn.Linear(dim * 4, 512), + nn.GELU(), nn.Linear(512, outputs)) + self.score_head = clip_head(192) + self.timbre_head = clip_head(128) + self.identity_head = clip_head(250) + self.frame_head = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim // 2), + nn.GELU(), nn.Conv1d(dim // 2, dim // 2, 7, + padding=3), nn.GELU(), + nn.Conv1d(dim // 2, 1, 1)) + + def forward(self, mel, mel_mask): + enc = self.encoder + h = F.gelu(enc.conv1(mel)) + h = F.gelu(enc.conv2(h)).transpose(1, 2) + n = h.shape[1] + h = h + enc.embed_positions(torch.arange(n, device=h.device)) + valid = torch.arange(n, device=h.device)[None, :] < \ + ((mel_mask.sum(1) + 1) // 2)[:, None] + attention = torch.zeros((len(h), 1, 1, n), device=h.device, dtype=h.dtype) + attention.masked_fill_(~valid[:, None, None, :], torch.finfo(h.dtype).min) + for layer in enc.layers: + h = layer(h, attention) + h = enc.layer_norm(h).float() + mask = valid[:, :, None] + count = mask.sum(1).clamp_min(1) + mean = (h * mask).sum(1) / count + var = ((h - mean[:, None, :]).square() * mask).sum(1) / count + minimum = h.masked_fill(~mask, torch.inf).amin(1) + maximum = h.masked_fill(~mask, -torch.inf).amax(1) + pooled = torch.cat((mean, minimum, maximum, var.clamp_min(1e-8).sqrt()), dim=-1) + x = self.frame_head[0](h) + x = self.frame_head[1](x) + x = self.frame_head[2](x).transpose(1, 2) + logits = self.frame_head[3:](x).squeeze(1) + return {'scores': self.score_head(pooled), 'frame': logits, + 'timbre': F.normalize(self.timbre_head(pooled).float(), dim=-1), + 'identity': F.normalize(self.identity_head(pooled).float(), dim=-1)} + + +def loss_terms(output, batch): + target, score_mask, frame, frame_mask, timbre, identity, speaker_mask = batch[2:] + pred = output['scores'].float() + terms = {} + for name, (start, stop, weight) in GROUPS.items(): + mask = score_mask[:, start:stop].float() + if mask.sum() > 0: + huber = F.huber_loss(pred[:, start:stop], target[:, start:stop], + delta=1.0, reduction='none') + terms[name] = weight * (huber * mask).sum() / mask.sum() + if frame_mask.sum() > 0: + bce = F.binary_cross_entropy_with_logits(output['frame'].float(), frame, + reduction='none', pos_weight=torch.tensor(5.0, + device=frame.device)) + terms['frame_bce'] = .5 * (bce * frame_mask).sum() / frame_mask.sum() + prob = output['frame'].float().sigmoid() * frame_mask + intersection = (prob * frame).sum() + dice = 1 - (2 * intersection + 1) / (prob.sum() + (frame * frame_mask).sum() + 1) + terms['frame_dice'] = .25 * dice + if speaker_mask.any(): + for name, vector in (('timbre', timbre), ('identity', identity)): + pred_vec = output[name][speaker_mask] + target_vec = F.normalize(vector[speaker_mask].float(), dim=-1) + terms[name] = .2 * ((1 - (pred_vec * target_vec).sum(-1)).mean() + + .1 * F.huber_loss(pred_vec, target_vec, delta=.1)) + else: + # Keep both heads in the graph on DDP ranks whose local batch has no + # valid single-speaker target. The zero contribution is still masked. + terms['timbre'] = output['timbre'].sum() * 0 + terms['identity'] = output['identity'].sum() * 0 + return terms + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument('--model', choices=['tiny', 'base'], required=True) + ap.add_argument('--epochs', type=int, default=2) + ap.add_argument('--out', type=Path, required=True) + ap.add_argument('--audio-tar', type=Path, default=ROOT / 'p3-pilot-00000.tar') + ap.add_argument('--sidecar-dir', type=Path) + ap.add_argument('--locator-audit', type=Path, default=ROOT / 'audit_locator.json') + args = ap.parse_args() + assert torch.cuda.is_available() + torch.set_num_threads(2) + torch.manual_seed(640913) + sidecar_paths = ({name: args.sidecar_dir / f'{args.audio_tar.stem}-{name}.tar' + for name in SIDECARS} if args.sidecar_dir else SIDECAR_PATHS) + rows = make_rows(args.audio_tar, sidecar_paths, args.locator_audit) + train_idx = [i for i in range(len(rows)) if i % 10 < 8] + val_idx = [i for i in range(len(rows)) if i % 10 >= 8] + train_scores = np.stack([rows[i]['scores'] for i in train_idx]) + finite = np.isfinite(train_scores) + counts = finite.sum(axis=0) + safe_counts = np.maximum(counts, 1) + clean = np.where(finite, train_scores, 0) + mean = clean.sum(axis=0) / safe_counts + variance = np.where(finite, (clean - mean) ** 2, 0).sum(axis=0) / safe_counts + std = np.where(counts > 0, np.maximum(np.sqrt(variance), .25), 1) + mean = mean.astype(np.float32) + std = std.astype(np.float32) + model_path = snapshot(args.model) + collate = collator(model_path) + train_loader = DataLoader(PilotData(rows, train_idx, mean, std), batch_size=8, + shuffle=True, collate_fn=collate, num_workers=0) + val_loader = DataLoader(PilotData(rows, val_idx, mean, std), batch_size=8, + shuffle=False, collate_fn=collate, num_workers=0) + model = MultiTaskWhisper(model_path).cuda() + heads = list(model.score_head.parameters()) + list(model.timbre_head.parameters()) + \ + list(model.identity_head.parameters()) + list(model.frame_head.parameters()) + optimizer = torch.optim.AdamW([{'params': model.encoder.parameters(), 'lr': 2e-5}, + {'params': heads, 'lr': 3e-4}], weight_decay=.01) + metrics = [] + for epoch in range(args.epochs): + model.train() + train_loss = [] + for batch in train_loader: + batch = tuple(x.cuda() for x in batch) + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(batch[0], batch[1]) + terms = loss_terms(output, batch) + loss = sum(terms.values()) + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + train_loss.append(float(loss.detach())) + model.eval() + val_loss = [] + sums = {} + with torch.no_grad(): + for batch in val_loader: + batch = tuple(x.cuda() for x in batch) + with torch.autocast('cuda', dtype=torch.bfloat16): + terms = loss_terms(model(batch[0], batch[1]), batch) + val_loss.append(float(sum(terms.values()))) + for name, val in terms.items(): + sums.setdefault(name, []).append(float(val)) + item = {'model': args.model, 'epoch': epoch + 1, + 'train_loss': float(np.mean(train_loss)), + 'val_loss': float(np.mean(val_loss)), + 'val_terms': {name: float(np.mean(v)) for name, v in sums.items()}, + 'train_rows': len(train_idx), 'val_rows': len(val_idx), + 'disclaimer': '100-example architecture smoke; no generalization estimate'} + metrics.append(item) + print(json.dumps(item), flush=True) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(metrics, indent=2) + '\n') + + +if __name__ == '__main__': + main() diff --git a/training/original_curriculum/base_run_config.json b/training/original_curriculum/base_run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e5fa22aeff52ed3341fb7853787438ca3f643695 --- /dev/null +++ b/training/original_curriculum/base_run_config.json @@ -0,0 +1,617 @@ +{ + "model": "base", + "out": "/e/project1/reformo/schuhmann1/whisper_score_regression/runs/layered_s1s10_base_20261003", + "resume": null, + "batch_per_gpu": 16, + "accum": 1, + "workers": 4, + "encoder_lr": 2e-05, + "head_lr": 0.0003, + "min_lr_ratio": 0.1, + "warmup_ratio": 0.02, + "checkpoint_every": 5000, + "validation_every": 5000, + "minimum_stage_validations": 4, + "min_class_events": 200, + "max_updates": 0, + "world": 16, + "total_updates": 281006, + "warmup_updates": 5620, + "score_names": [ + "emo_Affection", + "emo_Amusement", + "emo_Anger", + "emo_Astonishment_Surprise", + "emo_Awe", + "emo_Bitterness", + "emo_Concentration", + "emo_Confusion", + "emo_Contemplation", + "emo_Contempt", + "emo_Contentment", + "emo_Disappointment", + "emo_Disgust", + "emo_Distress", + "emo_Doubt", + "emo_Elation", + "emo_Embarrassment", + "emo_Emotional_Numbness", + "emo_Fatigue_Exhaustion", + "emo_Fear", + "emo_Helplessness", + "emo_Hope_Enthusiasm_Optimism", + "emo_Impatience_and_Irritability", + "emo_Infatuation", + "emo_Interest", + "emo_Intoxication_Altered_States_of_Consciousness", + "emo_Jealousy_and_Envy", + "emo_Longing", + "emo_Malevolence_Malice", + "emo_Pain", + "emo_Pleasure_Ecstasy", + "emo_Pride", + "emo_Relief", + "emo_Sadness", + "emo_Sexual_Lust", + "emo_Shame", + "emo_Sourness", + "emo_Teasing", + "emo_Thankfulness_Gratitude", + "emo_Triumph", + "vn_AGEV_reg", + "vn_AROU_reg", + "vn_ARSH_reg", + "vn_ATCK_reg", + "vn_BKGN_reg", + "vn_BRGT_reg", + "vn_CHNK_reg", + "vn_CLRT_reg", + "vn_COGL_reg", + "vn_DARC_reg", + "vn_DFLU_reg", + "vn_EMPH_reg", + "vn_ESTH_reg", + "vn_EXPL_reg", + "vn_FOCS_reg", + "vn_FULL_reg", + "vn_GEND_reg", + "vn_HARM_reg", + "vn_METL_reg", + "vn_RANG_reg", + "vn_RCQL_reg", + "vn_REGS_reg", + "vn_RESP_reg", + "vn_ROUG_reg", + "vn_R_CHST_reg", + "vn_R_HEAD_reg", + "vn_R_MASK_reg", + "vn_R_MIXD_reg", + "vn_R_NASL_reg", + "vn_R_ORAL_reg", + "vn_R_THRT_reg", + "vn_SMTH_reg", + "vn_STNC_reg", + "vn_STRU_reg", + "vn_S_ASMR_reg", + "vn_S_AUTH_reg", + "vn_S_CART_reg", + "vn_S_CASU_reg", + "vn_S_CONV_reg", + "vn_S_DRAM_reg", + "vn_S_FORM_reg", + "vn_S_MONO_reg", + "vn_S_NARR_reg", + "vn_S_NEWS_reg", + "vn_S_PLAY_reg", + "vn_S_RANT_reg", + "vn_S_STRY_reg", + "vn_S_TECH_reg", + "vn_S_WHIS_reg", + "vn_TEMP_reg", + "vn_TENS_reg", + "vn_VALN_reg", + "vn_VALS_reg", + "vn_VFLX_reg", + "vn_VOLT_reg", + "vn_VULN_reg", + "vn_WARM_reg", + "genuineness_0_6", + "blend_0_10", + "R_quality", + "eiv_extra:Age", + "eiv_extra:Arousal", + "eiv_extra:Authenticity", + "eiv_extra:Background_Noise", + "eiv_extra:Confident_vs._Hesitant", + "eiv_extra:Gender", + "eiv_extra:High-Pitched_vs._Low-Pitched", + "eiv_extra:Monotone_vs._Expressive", + "eiv_extra:Recording_Quality", + "eiv_extra:Serious_vs._Humorous", + "eiv_extra:Soft_vs._Harsh", + "eiv_extra:Submissive_vs._Dominant", + "eiv_extra:Valence", + "eiv_extra:Vulnerable_vs._Emotionally_Detached", + "eiv_extra:Warm_vs._Cold", + "eiv_extra:score_background_quality", + "eiv_extra:score_content_enjoyment", + "eiv_extra:score_overall_quality", + "eiv_extra:score_speech_quality", + "audiobox:CE", + "audiobox:CU", + "audiobox:PC", + "audiobox:PQ", + "dnsmos:SIG_raw", + "dnsmos:BAK_raw", + "dnsmos:OVRL_raw", + "dnsmos:SIG", + "dnsmos:BAK", + "dnsmos:OVRL", + "dnsmos:P808_MOS", + "burst_count_log1p", + "voiceclap_attribute:Affection", + "voiceclap_attribute:Age", + "voiceclap_attribute:Amusement", + "voiceclap_attribute:Anger", + "voiceclap_attribute:Arousal", + "voiceclap_attribute:Astonishment_Surprise", + "voiceclap_attribute:Authenticity", + "voiceclap_attribute:Awe", + "voiceclap_attribute:Background_Noise", + "voiceclap_attribute:Bitterness", + "voiceclap_attribute:Concentration", + "voiceclap_attribute:Confident_vs._Hesitant", + "voiceclap_attribute:Confusion", + "voiceclap_attribute:Contemplation", + "voiceclap_attribute:Contempt", + "voiceclap_attribute:Contentment", + "voiceclap_attribute:Disappointment", + "voiceclap_attribute:Disgust", + "voiceclap_attribute:Distress", + "voiceclap_attribute:Doubt", + "voiceclap_attribute:Elation", + "voiceclap_attribute:Embarrassment", + "voiceclap_attribute:Emotional_Numbness", + "voiceclap_attribute:Fatigue_Exhaustion", + "voiceclap_attribute:Fear", + "voiceclap_attribute:Gender", + "voiceclap_attribute:Helplessness", + "voiceclap_attribute:High-Pitched_vs._Low-Pitched", + "voiceclap_attribute:Hope_Enthusiasm_Optimism", + "voiceclap_attribute:Impatience_and_Irritability", + "voiceclap_attribute:Infatuation", + "voiceclap_attribute:Interest", + "voiceclap_attribute:Intoxication_Altered_States_of_Consciousness", + "voiceclap_attribute:Jealousy_&_Envy", + "voiceclap_attribute:Longing", + "voiceclap_attribute:Malevolence_Malice", + "voiceclap_attribute:Monotone_vs._Expressive", + "voiceclap_attribute:Pain", + "voiceclap_attribute:Pleasure_Ecstasy", + "voiceclap_attribute:Pride", + "voiceclap_attribute:Recording_Quality", + "voiceclap_attribute:Relief", + "voiceclap_attribute:Sadness", + 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a/training/original_curriculum/fastgen.sh b/training/original_curriculum/fastgen.sh new file mode 100644 index 0000000000000000000000000000000000000000..83b1b66d8b77d8272521a6caf3afa0947adbf90c --- /dev/null +++ b/training/original_curriculum/fastgen.sh @@ -0,0 +1,27 @@ +# FAST GENERATION ENVIRONMENT — everything off /e/software and /e/scratch, nothing off /e/data1. +# +# WHY: the project venv on /e/data1 is metadata-bound. Measured on a compute node +# (job 1550920, import bisection): `import numpy` alone took 604 s and the job hit its 20-minute +# wall before reaching torch. All four ranks finished numpy at exactly 604.4 s despite being +# staggered 0/20/40/60 s apart, which also shows the import stagger does nothing against this -- +# it was built for a module-cache write race, not for metadata contention. +# +# Heavy binaries come from the EasyBuild stack (numpy 0.087 s, PyTorch 2.9.1 + torchaudio 2.9.1 +# for the same Python 3.13); the pure-Python packages the project pins are installed to scratch. +S=/e/software/default/stages/2026/software +SC=/e/scratch/reformo/schuhmann1_moss +PYR=$S/Python/3.13.5-GCCcore-14.3.0 +SB=$S/SciPy-bundle/2025.07-gcccoreflexiblas-14.3.0-3.4.5 +PT=$S/PyTorch/2.9.1-gcccoreflexiblas-14.3.0-3.4.5 +TA=$S/torchaudio/2.9.1-gcccoreflexiblas-14.3.0-3.4.5 +export PATH=$PYR/bin:$PATH +# ZIPIMPORT FOR THE PURE-PYTHON PACKAGES. A wheel is a zip and Python imports straight out of +# it, so transformers, peft, huggingface_hub, requests and friends cost ONE file open each +# instead of thousands of small reads. That matters here: `pip install --target` onto this +# filesystem repeatedly produced truncated packages (a `transformers` dir with 14 of ~800 files, +# a 2-entry `peft` stub), and a truncated dir is worse than a missing one -- it imports cleanly +# as a namespace package and fails deep inside adapter loading, after the allocation is spent. +# Only wheels carrying compiled .so files have to be unpacked; those live in fastpkgs3. +WHL=$SC/wheels +export PYTHONPATH=$SC/fastpkgs3:$WHL/accelerate-1.14.0-py3-none-any.whl:$WHL/anyio-4.14.2-py3-none-any.whl:$WHL/certifi-2026.7.22-py3-none-any.whl:$WHL/filelock-3.32.4-py3-none-any.whl:$WHL/fsspec-2026.7.0-py3-none-any.whl:$WHL/h11-0.16.0-py3-none-any.whl:$WHL/httpcore-1.0.9-py3-none-any.whl:$WHL/httpx-0.28.1-py3-none-any.whl:$WHL/huggingface_hub-1.29.0-py3-none-any.whl:$WHL/idna-3.19-py3-none-any.whl:$WHL/jinja2-3.1.6-py3-none-any.whl:$WHL/librosa-1.0.0-py3-none-any.whl:$WHL/mpmath-1.4.1-py3-none-any.whl:$WHL/networkx-3.6.1-py3-none-any.whl:$WHL/packaging-26.3-py3-none-any.whl:$WHL/peft-0.20.0-py3-none-any.whl:$WHL/pycparser-3.0-py3-none-any.whl:$WHL/requests-2.34.2-py3-none-any.whl:$WHL/sniffio-1.3.1-py3-none-any.whl:$WHL/soundfile-0.14.0-py2.py3-none-manylinux_2_28_aarch64.whl:$WHL/sympy-1.14.0-py3-none-any.whl:$WHL/tqdm-4.70.0-py3-none-any.whl:$WHL/typing_extensions-4.16.0-py3-none-any.whl:$WHL/urllib3-2.7.0-py3-none-any.whl:$PT/lib/python3.13/site-packages:$TA/lib/python3.13/site-packages:$SB/lib/python3.13/site-packages +export LD_LIBRARY_PATH=$PYR/lib:$PT/lib:$TA/lib:$S/GCCcore/14.3.0/lib64:$S/OpenBLAS/0.3.30-GCCcore-14.3.0/lib:$S/BLIS/2.0-GCCcore-14.3.0/lib:$S/FlexiBLAS/3.4.5-GCC-14.3.0/lib:$S/zlib/1.3.1-GCCcore-14.3.0/lib:$S/OpenSSL/3/lib64:$S/bzip2/1.0.8-GCCcore-14.3.0/lib:$S/XZ/5.8.1-GCCcore-14.3.0/lib:$S/SQLite/3.50.1-GCCcore-14.3.0/lib:$S/libffi/3.5.1-GCCcore-14.3.0/lib64:$S/libreadline/8.2-GCCcore-14.3.0/lib:$S/ncurses/6.5-GCCcore-14.3.0/lib:$LD_LIBRARY_PATH diff --git a/training/original_curriculum/small_run_config.json b/training/original_curriculum/small_run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..dd9fba1a9091f56f99eaa4d33def1793ce86a374 --- /dev/null +++ b/training/original_curriculum/small_run_config.json @@ -0,0 +1,617 @@ +{ + "model": "small", + "out": "/e/project1/reformo/schuhmann1/whisper_score_regression/runs/layered_s1s10_small_20261003", + "resume": null, + "batch_per_gpu": 16, + "accum": 1, + "workers": 4, + "encoder_lr": 1.5e-05, + "head_lr": 0.0003, + "min_lr_ratio": 0.1, + "warmup_ratio": 0.02, + "checkpoint_every": 5000, + "validation_every": 5000, + "minimum_stage_validations": 4, + "min_class_events": 200, + "max_updates": 0, + "world": 16, + "total_updates": 281006, + "warmup_updates": 5620, + "score_names": [ + "emo_Affection", + "emo_Amusement", + "emo_Anger", + "emo_Astonishment_Surprise", + "emo_Awe", + "emo_Bitterness", + "emo_Concentration", + "emo_Confusion", + "emo_Contemplation", + "emo_Contempt", + "emo_Contentment", + "emo_Disappointment", + "emo_Disgust", + "emo_Distress", + "emo_Doubt", + "emo_Elation", + "emo_Embarrassment", + "emo_Emotional_Numbness", + "emo_Fatigue_Exhaustion", + "emo_Fear", + "emo_Helplessness", + "emo_Hope_Enthusiasm_Optimism", + "emo_Impatience_and_Irritability", + "emo_Infatuation", + "emo_Interest", + "emo_Intoxication_Altered_States_of_Consciousness", + "emo_Jealousy_and_Envy", + "emo_Longing", + "emo_Malevolence_Malice", + "emo_Pain", + "emo_Pleasure_Ecstasy", + "emo_Pride", + "emo_Relief", + "emo_Sadness", + "emo_Sexual_Lust", + "emo_Shame", + "emo_Sourness", + "emo_Teasing", + "emo_Thankfulness_Gratitude", + "emo_Triumph", + "vn_AGEV_reg", + "vn_AROU_reg", + "vn_ARSH_reg", + "vn_ATCK_reg", + "vn_BKGN_reg", + "vn_BRGT_reg", + "vn_CHNK_reg", + "vn_CLRT_reg", + "vn_COGL_reg", + "vn_DARC_reg", + "vn_DFLU_reg", + "vn_EMPH_reg", + "vn_ESTH_reg", + "vn_EXPL_reg", + "vn_FOCS_reg", + "vn_FULL_reg", + "vn_GEND_reg", + "vn_HARM_reg", + "vn_METL_reg", + "vn_RANG_reg", + "vn_RCQL_reg", + "vn_REGS_reg", + "vn_RESP_reg", + "vn_ROUG_reg", + "vn_R_CHST_reg", + "vn_R_HEAD_reg", + "vn_R_MASK_reg", + "vn_R_MIXD_reg", + "vn_R_NASL_reg", + "vn_R_ORAL_reg", + "vn_R_THRT_reg", + "vn_SMTH_reg", + "vn_STNC_reg", + "vn_STRU_reg", + "vn_S_ASMR_reg", + "vn_S_AUTH_reg", + "vn_S_CART_reg", + "vn_S_CASU_reg", + "vn_S_CONV_reg", + "vn_S_DRAM_reg", + "vn_S_FORM_reg", + "vn_S_MONO_reg", + "vn_S_NARR_reg", + "vn_S_NEWS_reg", + "vn_S_PLAY_reg", + "vn_S_RANT_reg", + "vn_S_STRY_reg", + "vn_S_TECH_reg", + "vn_S_WHIS_reg", + "vn_TEMP_reg", + "vn_TENS_reg", + "vn_VALN_reg", + "vn_VALS_reg", + "vn_VFLX_reg", + "vn_VOLT_reg", + "vn_VULN_reg", + "vn_WARM_reg", + "genuineness_0_6", + "blend_0_10", + "R_quality", + "eiv_extra:Age", + "eiv_extra:Arousal", + "eiv_extra:Authenticity", + "eiv_extra:Background_Noise", + "eiv_extra:Confident_vs._Hesitant", + "eiv_extra:Gender", + "eiv_extra:High-Pitched_vs._Low-Pitched", + "eiv_extra:Monotone_vs._Expressive", + "eiv_extra:Recording_Quality", + "eiv_extra:Serious_vs._Humorous", + "eiv_extra:Soft_vs._Harsh", + "eiv_extra:Submissive_vs._Dominant", + "eiv_extra:Valence", + "eiv_extra:Vulnerable_vs._Emotionally_Detached", + "eiv_extra:Warm_vs._Cold", + "eiv_extra:score_background_quality", + "eiv_extra:score_content_enjoyment", + "eiv_extra:score_overall_quality", + "eiv_extra:score_speech_quality", + "audiobox:CE", + "audiobox:CU", + "audiobox:PC", + "audiobox:PQ", + "dnsmos:SIG_raw", + "dnsmos:BAK_raw", + "dnsmos:OVRL_raw", + "dnsmos:SIG", + "dnsmos:BAK", + "dnsmos:OVRL", + "dnsmos:P808_MOS", + "burst_count_log1p", + "voiceclap_attribute:Affection", + "voiceclap_attribute:Age", + "voiceclap_attribute:Amusement", + "voiceclap_attribute:Anger", + "voiceclap_attribute:Arousal", + "voiceclap_attribute:Astonishment_Surprise", + "voiceclap_attribute:Authenticity", + "voiceclap_attribute:Awe", + "voiceclap_attribute:Background_Noise", + "voiceclap_attribute:Bitterness", + "voiceclap_attribute:Concentration", + "voiceclap_attribute:Confident_vs._Hesitant", + "voiceclap_attribute:Confusion", + "voiceclap_attribute:Contemplation", + "voiceclap_attribute:Contempt", + "voiceclap_attribute:Contentment", + "voiceclap_attribute:Disappointment", + "voiceclap_attribute:Disgust", + "voiceclap_attribute:Distress", + "voiceclap_attribute:Doubt", + "voiceclap_attribute:Elation", + "voiceclap_attribute:Embarrassment", + "voiceclap_attribute:Emotional_Numbness", + "voiceclap_attribute:Fatigue_Exhaustion", + "voiceclap_attribute:Fear", + "voiceclap_attribute:Gender", + "voiceclap_attribute:Helplessness", + "voiceclap_attribute:High-Pitched_vs._Low-Pitched", + "voiceclap_attribute:Hope_Enthusiasm_Optimism", + "voiceclap_attribute:Impatience_and_Irritability", + "voiceclap_attribute:Infatuation", + "voiceclap_attribute:Interest", + "voiceclap_attribute:Intoxication_Altered_States_of_Consciousness", + "voiceclap_attribute:Jealousy_&_Envy", + "voiceclap_attribute:Longing", + "voiceclap_attribute:Malevolence_Malice", + "voiceclap_attribute:Monotone_vs._Expressive", + "voiceclap_attribute:Pain", + "voiceclap_attribute:Pleasure_Ecstasy", + "voiceclap_attribute:Pride", + "voiceclap_attribute:Recording_Quality", + "voiceclap_attribute:Relief", + "voiceclap_attribute:Sadness", + "voiceclap_attribute:Serious_vs._Humorous", + "voiceclap_attribute:Sexual_Lust", + "voiceclap_attribute:Shame", + "voiceclap_attribute:Soft_vs._Harsh", + "voiceclap_attribute:Sourness", + "voiceclap_attribute:Submissive_vs._Dominant", + "voiceclap_attribute:Teasing", + "voiceclap_attribute:Thankfulness_Gratitude", + "voiceclap_attribute:Triumph", + "voiceclap_attribute:Valence", + "voiceclap_attribute:Vulnerable_vs._Emotionally_Detached", + "voiceclap_attribute:Warm_vs._Cold", + "voiceclap_attribute:duration", + "voiceclap_attribute:score_background_quality", + "voiceclap_attribute:score_content_enjoyment", + "voiceclap_attribute:score_overall_quality", + "voiceclap_attribute:score_speech_quality", + "voiceclap_attribute:talking_speed" + ], + "score_mean": [ + 0.27749165892601013, + 0.4777434766292572, + 0.36061015725135803, + 0.2777629792690277, + 0.11817667633295059, + 0.21946945786476135, + 0.6705899834632874, + 0.3177536427974701, + 0.5056841969490051, + 0.20877057313919067, + 0.35425904393196106, + 0.4897621273994446, + 0.22247810661792755, + 0.4589405953884125, + 0.3137844204902649, + 0.4519732594490051, + 0.2580322027206421, + 0.46182429790496826, + 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"cps_max": 60.0, + "hashes": { + "schedule": "f3f50eade3bd34a9fcdb487b437bea123a3c8c226b89e48821311f240c7f8bf1", + "ladder_index": "68c2cea705b6699a87ffc1ca638fb2e713b1a28ef8dc3b42204a4952b013e27c", + "ladder_aux_schema": "ec6d034784b47d6e370555d9a0d85d730ece41bf2134f2048dde2ea129388d0c", + "p3_index": "a8f167d4805d59548eeedb7a18d6e5cbbb535e696f1419695c3b89f853f21c8c", + "p3_event_schema": "486227a3045b4ff5714327d2ae9825bdcf027e7cd31d2c914ce6d58c38a18dde", + "p3_score_normalization": "ae909ebff3441ac37afadb7dc62f62a9b3f758c4c7a0d1cd7bd5b8ceae407622", + "ladder_score_normalization": "13cd547651f825dcda0ca36166cd0cec7267cd3aae46b5e71afd6d2a39e9bf22" + }, + "selection": "sum of fixed Ladder and P3 weighted validation losses", + "stage_best": "lowest selection at validation checks within each stage" +} diff --git a/training/p3_frozen_loader.py b/training/p3_frozen_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..6343c54083ef30d04e78fbdd37be44dc4d04e666 --- /dev/null +++ b/training/p3_frozen_loader.py @@ -0,0 +1,241 @@ +#!/usr/bin/env python3 +"""Random-access WebDataset reader for the frozen P3 training snapshot.""" +from __future__ import annotations + +import io +import json +import os +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from torch.utils.data import Dataset +from transformers import WhisperFeatureExtractor + +from prepare_p3_frozen import ROOT, score_values + + +class P3FrozenDataset(Dataset): + def __init__(self, split: str, *, root: Path = ROOT, + include_teacher_embeddings: bool = False, + score_mean: np.ndarray | None = None, + score_std: np.ndarray | None = None, + event_class_path: Path | None = None): + if split not in ('train', 'validation', 'test'): + raise ValueError(split) + self.root = Path(root) + self.split = split + self.rows = np.load(self.root / 'sample_index.npy', mmap_mode='r') + self.indices = np.load(self.root / f'{split}_indices.npy', mmap_mode='r') + frozen = json.loads((self.root / 'frozen_manifest.json').read_text()) + self.shards = frozen['shards'] + self.norm = json.loads((self.root / 'score_normalization.json').read_text()) + self.names = self.norm['score_names'] + self.mean = np.asarray(self.norm['mean'], dtype=np.float32) + self.std = np.asarray(self.norm['std'], dtype=np.float32) + if len(self.names) != 192: + raise RuntimeError('wrong frozen target width') + if score_mean is not None or score_std is not None: + if score_mean is None or score_std is None: + raise ValueError('score_mean and score_std must be supplied together') + self.mean = np.asarray(score_mean, dtype=np.float32) + self.std = np.asarray(score_std, dtype=np.float32) + if self.mean.shape != (192,) or self.std.shape != (192,) or np.any(self.std <= 0): + raise ValueError('invalid score normalization override') + self.include_teacher_embeddings = include_teacher_embeddings + self.event_class_path = Path(event_class_path) if event_class_path else None + self.event_class_ids = (np.load(self.event_class_path,mmap_mode='r') + if self.event_class_path else None) + if self.event_class_ids is not None and len(self.event_class_ids)!=len(self.rows): + raise RuntimeError('P3 event class sidecar/index row mismatch') + self._fds: OrderedDict[str, int] = OrderedDict() + + def __len__(self) -> int: + return len(self.indices) + + def close(self) -> None: + fds = getattr(self, '_fds', None) + if fds is None: + return + for fd in fds.values(): + os.close(fd) + fds.clear() + + def __del__(self): + self.close() + + def __getstate__(self): + state = self.__dict__.copy() + state['rows'] = None + state['indices'] = None + state['event_class_ids'] = None + state['_fds'] = OrderedDict() + return state + + def __setstate__(self, state): + self.__dict__.update(state) + self.rows = np.load(self.root / 'sample_index.npy', mmap_mode='r') + self.indices = np.load(self.root / f'{self.split}_indices.npy', mmap_mode='r') + if self.event_class_path: + self.event_class_ids=np.load(self.event_class_path,mmap_mode='r') + + def _read(self, path: str, offset: int, size: int) -> bytes: + if path not in self._fds: + if len(self._fds) >= 64: + _, fd = self._fds.popitem(last=False) + os.close(fd) + self._fds[path] = os.open(path, os.O_RDONLY) + else: + self._fds.move_to_end(path) + raw = os.pread(self._fds[path], size, offset) + if len(raw) != size: + raise RuntimeError(f'short read {path}:{offset}+{size}') + return raw + + def _payload(self, shard: dict, row: np.void, kind: str) -> bytes: + if kind == 'audio': + path = shard['audio']['path'] + elif kind == 'targets': + path = shard['sidecars']['targets']['path'] + else: + source_kind = {'timbre': 'orange', 'identity': 'orange', + 'voiceclap': 'voiceclap', 'bud_e': 'empathic'}[kind] + path = shard['sidecars'][source_kind]['path'] + return self._read(path, int(row[f'{kind}_offset']), int(row[f'{kind}_size'])) + + def __getitem__(self, position: int) -> dict: + index = int(self.indices[position]) + row = self.rows[index] + shard = self.shards[index // 10000] + key = f"p3_{int(row['sample_id']):08d}" + wave, sr = sf.read(io.BytesIO(self._payload(shard, row, 'audio')), + dtype='float32') + if sr != 16000 or wave.ndim != 1 or not 1600 <= len(wave) <= 480000: + raise RuntimeError(f'{key}: invalid 16-kHz mono audio') + target = json.loads(self._payload(shard, row, 'targets')) + if target['sample_id'] != key: + raise RuntimeError(f'{key}: target/audio key mismatch') + score = score_values(target, self.names).astype(np.float32) + valid = np.isfinite(score) + normalized = np.where(valid, (score - self.mean) / self.std, 0).astype(np.float32) + frame = np.asarray(target['burst_frame_labels'], dtype=np.float32) + frame_mask = np.asarray(target['burst_frame_valid_mask'], dtype=np.float32) + if len(frame) != len(frame_mask) or not frame_mask.all(): + raise RuntimeError(f'{key}: invalid frame target') + result = {'key': key, 'audio': wave, 'scores': normalized, + 'score_mask': valid, 'frame': frame, 'frame_mask': frame_mask, + 'auxiliary_targets': target['auxiliary_targets'], + 'events_samples': target['burst_frame_target']['positive_intervals_samples'], + 'event_raw_class_ids': (self.event_class_ids[index].copy() + if self.event_class_ids is not None else None), + 'negative_subtype': target['negative_subtype'], + 'timbre': np.load(io.BytesIO(self._payload(shard, row, 'timbre')), + allow_pickle=False).astype(np.float32), + 'identity': np.load(io.BytesIO(self._payload(shard, row, 'identity')), + allow_pickle=False).astype(np.float32), + 'speaker_valid': bool(target['orange_timbre']['valid_single_speaker'] + and target['orange_identity']['valid_single_speaker'])} + if result['timbre'].shape != (128,) or result['identity'].shape != (250,): + raise RuntimeError(f'{key}: wrong Orange embedding shapes') + if self.include_teacher_embeddings: + result['voiceclap_embedding'] = np.load( + io.BytesIO(self._payload(shard, row, 'voiceclap')), + allow_pickle=False).astype(np.float32) + result['bud_e_embedding'] = np.load( + io.BytesIO(self._payload(shard, row, 'bud_e')), + allow_pickle=False).astype(np.float32) + if (result['voiceclap_embedding'].shape != (768,) or + result['bud_e_embedding'].shape != (3072,)): + raise RuntimeError(f'{key}: wrong teacher embedding shapes') + return result + + +class ShardBucketBatches: + """Length-bucketed DDP batches with local TAR reads and no repeated keys.""" + + def __init__(self, dataset: P3FrozenDataset, batch_per_rank: int, + world: int = 1, rank: int = 0, seed: int = 640913): + if batch_per_rank < 1 or world < 1 or not 0 <= rank < world: + raise ValueError('invalid distributed batch coordinates') + self.dataset = dataset + self.batch_per_rank, self.world, self.rank = batch_per_rank, world, rank + self.seed = seed + self.epoch = 0 + self.width = batch_per_rank * world + self.shard = dataset.rows['shard'][dataset.indices] + self.duration = dataset.rows['duration_s'][dataset.indices] + + def __len__(self) -> int: + return len(self.dataset) // self.width + + def __iter__(self): + rng = np.random.default_rng(self.seed + self.epoch) + batches = [] + tails = [] + for shard in rng.permutation(np.unique(self.shard)): + positions = np.flatnonzero(self.shard == shard) + rng.shuffle(positions) + for start in range(0, len(positions), self.width * 32): + pool = positions[start:start + self.width * 32] + pool = pool[np.argsort(self.duration[pool], kind='stable')] + full = len(pool) // self.width * self.width + for i in range(0, full, self.width): + batches.append(pool[i:i + self.width]) + tails.extend(pool[full:].tolist()) + rng.shuffle(tails) + for start in range(0, len(tails) - self.width + 1, self.width): + batches.append(np.asarray(tails[start:start + self.width])) + rng.shuffle(batches) + for group in batches: + lo = self.rank * self.batch_per_rank + yield group[lo:lo + self.batch_per_rank].tolist() + + +class P3WhisperCollator: + def __init__(self, model_path: Path): + self.model_path = str(model_path) + self.extractor = None + + def __getstate__(self): + return {'model_path': self.model_path, 'extractor': None} + + def __call__(self, batch: list[dict]) -> dict: + if self.extractor is None: + self.extractor = WhisperFeatureExtractor.from_pretrained( + self.model_path, local_files_only=True) + feature = self.extractor( + [item['audio'] for item in batch], sampling_rate=16000, + padding='longest', truncation=True, max_length=480000, + return_attention_mask=True, return_tensors='np') + mel = feature['input_features'].astype(np.float32) + mel_mask = feature['attention_mask'].astype(np.int64) + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + mel_mask = np.pad(mel_mask, ((0, 0), (0, 1))) + n_frames = mel.shape[-1] // 2 + frame = np.zeros((len(batch), n_frames), np.float32) + frame_mask = np.zeros_like(frame) + for i, item in enumerate(batch): + n = min(n_frames, len(item['frame'])) + frame[i, :n] = item['frame'][:n] + frame_mask[i, :n] = item['frame_mask'][:n] + result = { + 'key': [item['key'] for item in batch], + 'mel': torch.from_numpy(mel), + 'mel_mask': torch.from_numpy(mel_mask), + 'scores': torch.from_numpy(np.stack([item['scores'] for item in batch])), + 'score_mask': torch.from_numpy(np.stack([item['score_mask'] for item in batch])), + 'frame': torch.from_numpy(frame), + 'frame_mask': torch.from_numpy(frame_mask), + 'timbre': torch.from_numpy(np.stack([item['timbre'] for item in batch])), + 'identity': torch.from_numpy(np.stack([item['identity'] for item in batch])), + 'speaker_valid': torch.tensor([item['speaker_valid'] for item in batch]), + 'auxiliary_targets': [item['auxiliary_targets'] for item in batch], + 'negative_subtype': [item['negative_subtype'] for item in batch], + } + if 'voiceclap_embedding' in batch[0]: + for name in ('voiceclap_embedding', 'bud_e_embedding'): + result[name] = torch.from_numpy(np.stack([item[name] for item in batch])) + return result diff --git a/training/p3_frozen_mix_schedule.py b/training/p3_frozen_mix_schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..88fadf5db3cb4f3ec6f2f3de150de83a53f7fa12 --- /dev/null +++ b/training/p3_frozen_mix_schedule.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +"""Deterministic S1→S10 exposure plan for the frozen P3 train split.""" +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +from p3_mix_schedule import LADDER_STAGE_ROWS +from prepare_p3_frozen import ROOT + + +def coprime_multiplier(size: int) -> int: + candidate = 1_000_003 + while math.gcd(candidate, size) != 1: + candidate += 2 + return candidate + + +def plan(train_rows: int) -> dict: + if train_rows < 100_000: + raise ValueError('frozen P3 train split is too small') + # With L Ladder clips, L/9 P3 clips make P3 one tenth of the combined + # stage. Round to a whole clip; the resulting share is closest to 10%. + target_fraction = .10 + stages = [] + exposure_cursor = 0 + for stage, ladder in LADDER_STAGE_ROWS.items(): + p3 = (ladder + 4) // 9 + total = ladder + p3 + if abs(p3 / total - target_fraction) > 1 / total: + raise RuntimeError(f'{stage}: P3 share deviates from 10%') + stages.append({'stage': stage, 'ladder': ladder, 'p3': p3, + 'total': total, 'p3_share': p3 / total, + 'p3_exposure_start': exposure_cursor, + 'p3_exposure_end': exposure_cursor + p3}) + exposure_cursor += p3 + ladder_total = sum(stage['ladder'] for stage in stages) + global_fraction = exposure_cursor / (exposure_cursor + ladder_total) + if abs(global_fraction - target_fraction) > 1 / (exposure_cursor + ladder_total): + raise RuntimeError('global P3 share deviates from 10%') + return {'schema_version': 'p3-frozen-curriculum-v2', + 'p3_train_unique': train_rows, + 'target_p3_fraction_per_stage': target_fraction, + 'ladder_total_exposures': ladder_total, + 'p3_total_exposures': exposure_cursor, + 'mean_exposures_per_p3_key': exposure_cursor / train_rows, + 'min_exposures_per_p3_key': exposure_cursor // train_rows, + 'max_exposures_per_p3_key': math.ceil(exposure_cursor / train_rows), + 'global_p3_fraction': global_fraction, + 'permutation_a': coprime_multiplier(train_rows), + 'permutation_b': 640913 % train_rows, + 'stages': stages, + 'index_semantics': 'P3 index is a row of train_indices.npy, never a production global sample ID', + 'mix_rule': 'sample-level reference: floor((position+1)*p3/total)>floor(position*p3/total)', + 'training_batch_rule': 'whole-domain DDP global batches interleaved at approximately 10% P3; each stage consumes exactly its scheduled Ladder and P3 sample counts; only last batches are zero-loss padded'} + + +def p3_train_position(report: dict, exposure: int) -> int: + size = report['p3_train_unique'] + return (report['permutation_a'] * (exposure % size) + + report['permutation_b'] + exposure // size) % size + + +def mixture_at(report: dict, stage: dict, position: int) -> tuple[str, int]: + if not 0 <= position < stage['total']: + raise IndexError(position) + p3_before = position * stage['p3'] // stage['total'] + p3_after = (position + 1) * stage['p3'] // stage['total'] + if p3_after > p3_before: + exposure = stage['p3_exposure_start'] + p3_before + return 'p3', p3_train_position(report, exposure) + return 'ladder', position - p3_before + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument('--root', type=Path, default=ROOT) + args = ap.parse_args() + index = json.loads((args.root / 'training_index_report.json').read_text()) + result = plan(index['split_counts']['train']['samples']) + out = args.root / 'curriculum_mix_schedule.json' + tmp = out.with_suffix(out.suffix + '.incomplete') + tmp.write_text(json.dumps(result, indent=2) + '\n') + tmp.replace(out) + print({'p3_train_unique': result['p3_train_unique'], + 'p3_total_exposures': result['p3_total_exposures'], + 'mean_exposures': result['mean_exposures_per_p3_key'], + 'global_p3_fraction': result['global_p3_fraction'], + 'output': str(out)}, flush=True) + + +if __name__ == '__main__': + main() diff --git a/training/p3_mix_schedule.py b/training/p3_mix_schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..c5756975bf9cde9da189cb0724099962cb350736 --- /dev/null +++ b/training/p3_mix_schedule.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +"""Historical 10M P3 exposure plan; use p3_frozen_mix_schedule.py for 1.12M. + +Deterministic P3 exposure plan for one uninterrupted S1→S10 epoch per stage. + +This plans sample IDs and positions; it does not start the large training run. +Ladder and P3 loaders can consume the returned domain/index pair without +materializing an 83-million-row shuffle or mixing train with P3 holdout keys. +""" +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +LADDER_STAGE_ROWS = { + 'S1': 34_112_433, 'S2': 17_135_901, 'S3': 6_666_998, + 'S4': 3_425_099, 'S5': 1_764_990, 'S6': 912_789, + 'S7': 382_768, 'S8': 197_662, 'S9': 100_858, 'S10': 41_771, +} +P3_TRAIN_ROWS = 9_800_000 # 980 of the planned 1,000 ten-thousand-row shards. +P3_S1_EXPOSURES = 11_000_000 # One pass of all train IDs plus 1.2M repeats. +PERMUTATION_A = 1_000_003 +PERMUTATION_B = 640_913 +assert math.gcd(PERMUTATION_A, P3_TRAIN_ROWS) == 1 + + +def plan() -> list[dict]: + rows = [] + cursor = 0 + for stage, ladder in LADDER_STAGE_ROWS.items(): + p3 = P3_S1_EXPOSURES if stage == 'S1' else round(ladder / 4) + total = ladder + p3 + assert 3 * p3 <= ladder, f'{stage}: P3 exceeds 25% of stage' + rows.append({'stage': stage, 'ladder': ladder, 'p3': p3, + 'total': total, 'p3_share': p3 / total, + 'p3_exposure_start': cursor, + 'p3_exposure_end': cursor + p3}) + cursor += p3 + ladder_total = sum(row['ladder'] for row in rows) + p3_total = sum(row['p3'] for row in rows) + assert 3 * p3_total <= ladder_total, 'P3 exceeds 25% of whole run' + assert p3_total < 2 * P3_TRAIN_ROWS, 'a P3 train key would need a third pass' + return rows + + +def p3_train_id(exposure: int) -> int: + """A shuffled full first pass, then shuffled second pass over train keys.""" + return (PERMUTATION_A * (exposure % P3_TRAIN_ROWS) + PERMUTATION_B) % P3_TRAIN_ROWS + + +def mixture_at(row: dict, position: int) -> tuple[str, int]: + """Return (domain, index), with P3 IDs spread through the whole stage.""" + if not 0 <= position < row['total']: + raise IndexError(position) + p3_before = position * row['p3'] // row['total'] + p3_after = (position + 1) * row['p3'] // row['total'] + if p3_after > p3_before: + return 'p3', p3_train_id(row['p3_exposure_start'] + p3_before) + return 'ladder', position - p3_before + + +def rank_batch(row: dict, update: int, rank: int, world: int, + batch_per_rank: int) -> list[tuple[str, int] | None]: + """Equal-size DDP batches; None is a zero-loss padding slot on the last step.""" + if not (0 <= rank < world and batch_per_rank > 0 and update >= 0): + raise ValueError('invalid distributed batch coordinates') + width = world * batch_per_rank + if update >= math.ceil(row['total'] / width): + raise IndexError(update) + start = update * width + rank * batch_per_rank + return [mixture_at(row, start + j) if start + j < row['total'] else None + for j in range(batch_per_rank)] + + +def report() -> dict: + rows = plan() + ladder = sum(row['ladder'] for row in rows) + p3 = sum(row['p3'] for row in rows) + return {'schema': 'p3-curriculum-mix-v1', 'ladder_exposures': ladder, + 'p3_train_unique': P3_TRAIN_ROWS, 'p3_validation_unique': 100_000, + 'p3_test_unique': 100_000, 'p3_exposures': p3, + 'p3_global_fraction': p3 / (ladder + p3), + 'p3_mean_exposures_per_train_key': p3 / P3_TRAIN_ROWS, + 'p3_max_exposures_per_train_key': 2, + 'global_batch_reference': 256, + 'optimizer_updates_at_global_batch_256': + sum(math.ceil(row['total'] / 256) for row in rows), + 'permutation_a': PERMUTATION_A, 'permutation_b': PERMUTATION_B, + 'mix_rule': 'floor((position+1)*p3/total)>floor(position*p3/total)', + 'stages': rows} + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument('--output', type=Path) + args = ap.parse_args() + result = report() + content = json.dumps(result, indent=2) + '\n' + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(content) + print(content) + + +if __name__ == '__main__': + main() diff --git a/training/p3_target_adapter.py b/training/p3_target_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..25e19e8ba4955a5b6234629bc3185c1bad09abe0 --- /dev/null +++ b/training/p3_target_adapter.py @@ -0,0 +1,259 @@ +#!/usr/bin/env python3 +"""Join P3 teacher JSONs into the Ladder target schema and extra task masks. + +The five sidecars are immutable WebDataset companions to an audio TAR. This +adapter is also a contract check for future streaming loaders. It reads JSON +only; arrays remain in their sidecar TARs and are loaded on demand. +""" +from __future__ import annotations + +import argparse +import io +import json +import math +import os +import re +import tarfile +from collections import Counter +from pathlib import Path + +LADDER_COLUMNS = Path('/e/scratch/reformo/schuhmann1_moss/publish/scaling_ladder/COLUMNS.md') +EIV_ALIAS = {'Jealousy_and_Envy': 'Jealousy_&_Envy'} +DNSSCORES = ('SIG_raw', 'BAK_raw', 'OVRL_raw', 'SIG', 'BAK', 'OVRL', 'P808_MOS') +AUDIOBOX = ('CE', 'CU', 'PC', 'PQ') +SIDECARS = { + 'voiceclap': '.scores.json', 'audiobox': '.audiobox.json', + 'dnsmos': '.dnsmos.json', 'orange': '.orange.json', + 'empathic': '.eiv.json', +} + + +def target_names(columns_path: Path = LADDER_COLUMNS) -> list[str]: + columns = re.findall(r'^\| `([^`]+)`', columns_path.read_text(), re.M) + emo = [x for x in columns if x.startswith('emo_')] + voice = [x for x in columns if x.startswith('vn_') and x.endswith('_reg')] + assert len(emo) == 40 and len(voice) == 57, (len(emo), len(voice)) + return emo + voice + ['genuineness_0_6', 'blend_0_10', 'R_quality'] + + +def json_members(path: Path, suffix: str) -> dict[str, dict]: + rows = {} + with tarfile.open(path, 'r:') as tf: + for member in tf: + if member.isfile() and member.name.endswith(suffix): + key = member.name[:-len(suffix)] + if key in rows: + raise RuntimeError(f'duplicate key {key} in {path}') + rows[key] = json.load(tf.extractfile(member)) + return rows + + +def rasterize_burst_frames(frame_target: dict) -> tuple[list[int], list[int]]: + """Rasterize event/sample intervals at 20 ms with explicit valid frames.""" + hop = int(frame_target['hop_samples']) + end = int(frame_target['end_sample']) + count = (end + hop - 1) // hop + labels = [0] * count + valid = [int(bool(frame_target['valid']))] * count + for start, stop in frame_target['positive_intervals_samples']: + for frame in range(start // hop, min(count, (stop + hop - 1) // hop)): + labels[frame] = 1 + return labels, valid + + +def union_samples(intervals: list[list[int]]) -> int: + merged_end = total = 0 + for start, stop in sorted(intervals): + total += max(0, stop - max(start, merged_end)) + merged_end = max(merged_end, stop) + return total + + +def adapt(meta: dict, sidecars: dict[str, dict], names: list[str], + extra_eiv_names: list[str], locator: dict | None) -> dict: + key = meta['sample_id'] + for kind, doc in sidecars.items(): + if doc['sample_id'] != key: + raise RuntimeError(f'{key}: {kind} sidecar key mismatch') + voice = sidecars['voiceclap'] + attributes = voice.get('voiceclap_attributes') or {} + eiv = sidecars['empathic'] + dns = sidecars['dnsmos'] + box = sidecars['audiobox'] + orange = sidecars['orange'] + speech = bool(voice['validity']['speech_scores'] and eiv['speech_domain_valid']) + values = {} + for name in names: + if name.startswith('emo_'): + score = eiv['scores'].get(EIV_ALIAS.get(name[4:], name[4:])) if speech else None + elif name.startswith('vn_'): + dimension = name[3:-4] + score = voice['voicenet'].get(dimension, {}).get('regression') if speech else None + elif name == 'genuineness_0_6': + score = voice.get(name) if speech else None + elif name == 'blend_0_10': + score = voice.get('vocalburst_blend_0_10') if voice['validity']['vocalburst_blend'] else None + else: + # R_quality is cell-normalized using the Ladder selection pool; + # that calibration does not exist for a P3 synthetic mixture. + score = None + values[name] = score if isinstance(score, (int, float)) and math.isfinite(score) else None + if speech and any(values[name] is None for name in names[:97]): + raise RuntimeError(f'{key}: incomplete speech emotion/VoiceNet targets') + extra = {name: (eiv['scores'] or {}).get(name) for name in extra_eiv_names} + if len(extra) != 19: + raise RuntimeError(f'{key}: expected 19 non-Ladder EIV values, got {len(extra)}') + n = int(meta['n_vocal_bursts']) + if n != len(meta['events']): + raise RuntimeError(f'{key}: count and event list disagree') + positive = n > 0 + # Pilot locator flags are a quarantine signal, not a release certificate. + negative_screened = bool(locator and not locator['n_locator_spans']) + frame_valid = positive or negative_screened + end_sample = round(float(meta['duration_s']) * int(meta['sample_rate'])) + intervals = [] + for event in meta['events']: + a, b = int(event['start_sample']), int(event['end_sample']) + if not 0 <= a < b <= end_sample: + raise RuntimeError(f'{key}: invalid event interval {(a, b)}') + intervals.append([a, b]) + speech_intervals = [[int(c['start_sample']), int(c['end_sample'])] + for c in meta['components'] if c['role'] == 'speech'] + intersects = [[max(a, c), min(b, d)] for a, b in intervals + for c, d in speech_intervals if min(b, d) > max(a, c)] + burst_covered = union_samples(intervals) + empty = meta.get('known_empty_intervals_samples') + auxiliary = { + 'duration_s': float(meta['duration_s']), + 'burst_rate_per_minute': 60 * n / float(meta['duration_s']), + 'burst_occupancy_fraction': burst_covered / end_sample, + 'burst_speech_overlap_fraction': + union_samples(intersects) / burst_covered if burst_covered else 0.0, + 'speech_component_count': len(speech_intervals), + 'known_empty_fraction': union_samples(empty) / end_sample if empty is not None else None, + 'has_fx': int(any(c['role'] == 'fx' for c in meta['components'])), + 'has_music': int(any(c['role'] == 'music' for c in meta['components'])), + 'characters_per_second': None, + 'characters_per_second_teacher': + attributes.get('talking_speed') + if voice['validity'].get('attribute_speech_domain') else None, + } + return { + 'sample_id': key, 'ladder_scores': values, + 'extra_eiv_scores': extra if speech else {name: None for name in extra}, + 'audiobox_scores': {name: box['scores'].get(name) for name in AUDIOBOX}, + 'dnsmos_scores': {name: dns['scores'].get(name) if dns['valid_for_speech'] else None + for name in DNSSCORES}, + 'burst_count_log1p': math.log1p(n), + 'burst_frame_target': {'hop_samples': 320, 'end_sample': end_sample, + 'positive_intervals_samples': intervals, + 'valid': frame_valid, + 'negative_screening': 'locator_unflagged_provisional' + if negative_screened and not positive else + 'quarantined' if not positive else 'synthetic_insertions'}, + 'negative_subtype': meta.get('negative_subtype'), + 'known_empty_intervals_samples': meta.get('known_empty_intervals_samples', []), + 'auxiliary_targets': auxiliary, + 'orange_timbre': {'file': orange['timbre_file'], 'dim': 128, + 'valid_single_speaker': orange['valid_single_speaker_target']}, + 'orange_identity': {'file': orange['identity_file'], 'dim': 250, + 'valid_single_speaker': orange['valid_single_speaker_target']}, + 'voiceclap_embedding': voice['voiceclap_embedding'], + 'voiceclap_attribute_scores': attributes, + 'voiceclap_attributes_valid_for_speech': + bool(voice['validity'].get('attribute_speech_domain')), + 'bud_e_embedding': eiv['pooled_embedding_file'], + 'teacher_revisions': {'voiceclap': voice['teacher_revisions'], + 'audiobox': box['teacher_revision'], + 'orange': orange['teacher_revisions'], + 'eiv': eiv['teacher'], 'dnsmos': dns['teacher']}, + } + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument('--audio-tar', type=Path, required=True) + for kind in SIDECARS: + ap.add_argument('--' + kind, type=Path, required=True) + ap.add_argument('--locator-audit', type=Path) + ap.add_argument('--report', type=Path, required=True) + ap.add_argument('--targets-sidecar', type=Path, + help='optional key-aligned WebDataset TAR with all joined targets and 20-ms frames') + args = ap.parse_args() + names = target_names() + audio = json_members(args.audio_tar, '.json') + side = {kind: json_members(getattr(args, kind), suffix) + for kind, suffix in SIDECARS.items()} + for kind, rows in side.items(): + if set(rows) != set(audio): + raise RuntimeError(f'{kind}: sidecar keys differ from audio keys') + locator = {} + if args.locator_audit: + locator = {row['sample_id']: row for row in + json.loads(args.locator_audit.read_text())['rows']} + if set(locator) != set(audio): + raise RuntimeError('locator audit keys differ from audio keys') + example_scores = next((doc['scores'] for doc in side['empathic'].values() + if doc['scores'] is not None), None) + if example_scores is None: + raise RuntimeError('no EIV score schema in sidecars') + mapped_eiv = {EIV_ALIAS.get(name[4:], name[4:]) for name in names[:40]} + extra_eiv_names = sorted(set(example_scores) - mapped_eiv) + targets = [adapt(audio[key], {kind: rows[key] for kind, rows in side.items()}, + names, extra_eiv_names, locator.get(key)) for key in sorted(audio)] + if args.targets_sidecar: + args.targets_sidecar.parent.mkdir(parents=True, exist_ok=True) + tmp = args.targets_sidecar.with_suffix('.tar.incomplete') + with tarfile.open(tmp, 'w') as tf: + for row in targets: + labels, mask = rasterize_burst_frames(row['burst_frame_target']) + payload = {**row, 'burst_frame_labels': labels, + 'burst_frame_valid_mask': mask} + encoded = json.dumps(payload, separators=(',', ':')).encode() + member = tarfile.TarInfo(row['sample_id'] + '.targets.json') + member.size, member.mtime = len(encoded), 0 + tf.addfile(member, io.BytesIO(encoded)) + os.replace(tmp, args.targets_sidecar) + valid = Counter() + frame_positive = frame_valid = 0 + for row in targets: + for key, value in row['ladder_scores'].items(): + if value is not None: + valid[key] += 1 + frame_labels, frame_mask = rasterize_burst_frames(row['burst_frame_target']) + frame_positive += sum(a * b for a, b in zip(frame_labels, frame_mask)) + frame_valid += sum(frame_mask) + result = {'samples': len(targets), 'ladder_target_count': len(names), + 'ladder_valid_min': min(valid[name] for name in names), + 'ladder_valid_max': max(valid[name] for name in names), + 'emotion_valid': valid[names[0]], 'voicenet_valid': valid[names[40]], + 'genuineness_valid': valid['genuineness_0_6'], + 'blend_valid': valid['blend_0_10'], 'R_quality_valid': valid['R_quality'], + 'audiobox_valid': sum(all(v is not None for v in r['audiobox_scores'].values()) + for r in targets), + 'dnsmos_valid': sum(all(v is not None for v in r['dnsmos_scores'].values()) + for r in targets), + 'extra_eiv_dimensions': len(targets[0]['extra_eiv_scores']), + 'orange_single_speaker_valid': sum(r['orange_timbre']['valid_single_speaker'] + for r in targets), + 'frame_target_valid': sum(r['burst_frame_target']['valid'] for r in targets), + 'frame_target_quarantined': sum(not r['burst_frame_target']['valid'] for r in targets), + 'valid_frame_count': frame_valid, 'positive_frame_count': frame_positive, + 'positive_frame_fraction': frame_positive / frame_valid if frame_valid else None, + 'auxiliary_target_count': len(targets[0]['auxiliary_targets']), + 'voiceclap_attribute_dimensions': + len(targets[0]['voiceclap_attribute_scores']), + 'auxiliary_valid': {name: sum(r['auxiliary_targets'][name] is not None + for r in targets) + for name in targets[0]['auxiliary_targets']}, + 'target_schema': 'p3-ladder-multitask-v1', + 'targets_sidecar': str(args.targets_sidecar) if args.targets_sidecar else None, + 'targets_sidecar_samples': len(targets) if args.targets_sidecar else 0, + 'status': 'schema_join_pilot; locator-only negative screening is provisional'} + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(result, indent=2) + '\n') + print(json.dumps(result, indent=2)) + + +if __name__ == '__main__': + main() diff --git a/training/prepare.py b/training/prepare.py new file mode 100644 index 0000000000000000000000000000000000000000..01e791390889fa64eee94d555078de12ebd83f46 --- /dev/null +++ b/training/prepare.py @@ -0,0 +1,140 @@ +"""Index the released Ladder TARs without copying audio; make a fixed validation set.""" +from __future__ import annotations + +import json +import re +import tarfile +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/publish/scaling_ladder') +OUT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression') +SEED = 20260928 + + +def target_names() -> list[str]: + text = (ROOT / 'COLUMNS.md').read_text() + columns = re.findall(r'^\| `([^`]+)`', text, re.M) + emo = [c for c in columns if c.startswith('emo_')] + voice = [c for c in columns if c.startswith('vn_') and c.endswith('_reg')] + assert len(emo) == 40 and len(voice) == 57, (len(emo), len(voice)) + return emo + voice + ['genuineness_0_6', 'blend_0_10', 'R_quality'] + + +def build() -> None: + OUT.mkdir(parents=True, exist_ok=True) + targets = target_names() + wanted = ['uid', 'dur_s', 'lang', 'origin', 'source'] + targets + paths: list[str] = [] + sources: list[str] = [] + accum: dict[str, dict[str, list[np.ndarray]]] = { + s: {k: [] for k in ['path', 'offset', 'size', 'dur', 'uid', 'cell', 'source', 'y']} + for s in ['t0', 't1', 'heldout'] + } + for stage, pattern in [('t0', '*_t0-*.parquet'), ('t1', '*_t1-*.parquet'), ('heldout', '*_h-*.parquet')]: + for index_path in sorted((ROOT / 'index').rglob(pattern)): + tar_path = ROOT / 'data' / index_path.relative_to(ROOT / 'index').with_suffix('.tar') + assert tar_path.is_file(), tar_path + table = pq.read_table(index_path, columns=wanted) + uids = table['uid'].to_pylist() + n = len(uids) + offsets = np.empty(n, dtype=np.int64) + sizes = np.empty(n, dtype=np.int32) + with tarfile.open(tar_path, 'r:') as archive: + j = 0 + for member in archive: + if not member.name.endswith('.mp3'): + continue + assert j < n and member.name.rsplit('/', 1)[-1][:-4] == uids[j], (tar_path, j) + offsets[j], sizes[j] = member.offset_data, member.size + j += 1 + assert j == n, (tar_path, j, n) + path_id = len(paths) + paths.append(str(tar_path)) + lang = table['lang'].to_pylist() + origin = table['origin'].to_pylist() + cells = np.array([{'de_real': 0, 'de_synth': 1, 'en_real': 2, 'en_synth': 3}[f'{a}_{b}'] + for a, b in zip(lang, origin)], dtype=np.uint8) + source_names = table['source'].to_pylist() + for name in source_names: + if name not in sources: + sources.append(name) + source_id = np.array([sources.index(name) for name in source_names], dtype=np.uint8) + label_cols = [np.asarray(table[name].to_numpy(zero_copy_only=False), dtype=np.float32) + for name in targets] + labels = np.stack(label_cols, axis=1) + chunk = { + 'path': np.full(n, path_id, dtype=np.uint16), + 'offset': offsets, 'size': sizes, + 'dur': np.asarray(table['dur_s'].to_numpy(), dtype=np.float32), + 'uid': np.asarray(uids, dtype='S128'), + 'cell': cells, 'source': source_id, 'y': labels, + } + for name, value in chunk.items(): + accum[stage][name].append(value) + print(stage, tar_path.name, n, flush=True) + + data = {stage: {name: np.concatenate(parts) for name, parts in columns.items()} + for stage, columns in accum.items()} + # Shared normalization must not reveal S9-only labels to the S10 run. + train = data['t0']['y'] + with np.errstate(invalid='ignore'): + mean = np.nanmean(train, axis=0).astype(np.float32) + std = np.nanstd(train, axis=0).astype(np.float32) + assert np.all(np.isfinite(mean)) and np.all(np.isfinite(std)) + std = np.maximum(std, 0.25) + # Separate held-out UIDs are a release invariant; fail if it changed. + train_uids = set(np.concatenate([data['t0']['uid'], data['t1']['uid']]).tolist()) + val_uids = set(data['heldout']['uid'].tolist()) + assert len(train_uids) == len(data['t0']['uid']) + len(data['t1']['uid']) + assert not (train_uids & val_uids) + + selected = balanced_val(data['heldout'], 2048) + for stage, arrays in data.items(): + if stage == 'heldout': + arrays = {name: value[selected] for name, value in arrays.items()} + stage = 'val' + np.savez(OUT / f'{stage}.npz', **arrays) + meta = { + 'root': str(ROOT), 'seed': SEED, 'targets': targets, 'paths': paths, + 'sources': sources, 'mean': mean.tolist(), 'std': std.tolist(), + 'rows': {'S10': len(data['t0']['uid']), + 'S9': len(data['t0']['uid']) + len(data['t1']['uid']), + 'heldout_full': len(data['heldout']['uid']), 'val': len(selected)}, + 'val_policy': '512 per DE/EN x real/synth cell; half uniform and half score-tail weighted, fixed seed', + 'target_groups': {'emotion': [0, 40], 'voicenet': [40, 97], 'other': [97, 100]}, + } + (OUT / 'manifest.json').write_text(json.dumps(meta, indent=2) + '\n') + print('DONE', meta['rows'], flush=True) + + +def balanced_val(heldout: dict[str, np.ndarray], n: int) -> np.ndarray: + rng = np.random.default_rng(SEED) + y = heldout['y'] + low = np.nanquantile(y, 0.1, axis=0) + high = np.nanquantile(y, 0.9, axis=0) + extreme = np.isfinite(y) & ((y <= low) | (y >= high)) + # Rarer simultaneous tails get more weight. The same indices serve all four runs. + score = np.sqrt(1.0 + extreme[:, :99].sum(axis=1)) + source_counts = np.bincount(heldout['source'].astype(int)) + score *= 1 / np.sqrt(source_counts[heldout['source'].astype(int)] / source_counts.max()) + out: list[np.ndarray] = [] + assert n % 4 == 0 + for cell in range(4): + candidates = np.flatnonzero(heldout['cell'] == cell) + assert len(candidates) >= n // 4 + ordinary = rng.choice(candidates, n // 8, replace=False) + remaining = np.setdiff1d(candidates, ordinary, assume_unique=True) + p = score[remaining] / score[remaining].sum() + tails = rng.choice(remaining, n // 8, replace=False, p=p) + out.extend([ordinary, tails]) + chosen = np.sort(np.concatenate(out)) + assert len(chosen) == n and len(np.unique(chosen)) == n + return chosen + + +if __name__ == '__main__': + build() diff --git a/training/prepare_full_ladder.py b/training/prepare_full_ladder.py new file mode 100644 index 0000000000000000000000000000000000000000..2739627ca55cdd2324e5e151ef32804abab96836 --- /dev/null +++ b/training/prepare_full_ladder.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +"""Build resumable random-access metadata for every internal Ladder audio shard. + +The complete S1 stock is in out/scaling_stages/pack/out, whereas the published +copy contains only the small tiers. Existing TAR member offsets are reused; +audio TARs are never copied or rescanned. +""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from collections import Counter +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path + +import numpy as np +import pyarrow.parquet as pq + +from prepare import target_names +from p3_mix_schedule import LADDER_STAGE_ROWS + + +INDEX = Path('/e/scratch/reformo/schuhmann1_moss/out/scaling_stages/pack/out/index') +AUDIO = Path('/e/scratch/reformo/schuhmann1_moss/out/scaling_stages/pack/out/data') +OUT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/full_ladder') +MAP_ROOT = Path('/e/scratch/reformo/schuhmann1_moss/out/m2_600m_ladder/tar_member_map') +MAP_CODE = Path('/e/scratch/reformo/schuhmann1_moss/out/m2_600m_ladder/code_v4_highlr_20260920') +LEVELS = ('t0', 't1', 't2', 't3', 't4', 't5', 't6', 't7', 'x50', 'x100') +MAX_EVENTS = 32 +DTYPE = np.dtype([ + ('audio_offset', ' str: + value = path.stem.rsplit('_', 1)[-1].split('-', 1)[0] + if value not in LEVELS: + raise RuntimeError(f'unexpected Ladder level: {path}') + return value + + +def member_map(): + global _MEMBER_MAP + if _MEMBER_MAP is None: + sys.path.insert(0, str(MAP_CODE)) + from tar_member_map import TarMemberMap + _MEMBER_MAP = TarMemberMap(MAP_ROOT / 'manifest.json', capacity=2) + return _MEMBER_MAP + + +def process(path_text: str) -> dict: + path = Path(path_text) + relative = path.relative_to(INDEX) + audio = AUDIO / relative.with_suffix('.tar') + out = OUT / 'shards' / relative.with_suffix('.npy') + if out.exists(): + rows = np.load(out, mmap_mode='r') + if rows.dtype != DTYPE or len(rows) != pq.ParquetFile(path).metadata.num_rows: + raise RuntimeError(f'stale full Ladder metadata: {out}') + return {'level': level(path), 'parquet': str(path), 'audio': str(audio), + 'meta': str(out), 'rows': len(rows)} + columns = ['uid', 'dur_s', 'audio_bytes', 'n_bursts', + 'burst_starts', 'burst_ends'] + TARGETS + table = pq.read_table(path, columns=columns) + n = len(table) + rows = np.empty(n, dtype=DTYPE) + rows['starts'].fill(-1) + rows['ends'].fill(-1) + rows['event_count'].fill(0) + rows['burst_count_valid'].fill(0) + rows['burst_frame_valid'].fill(0) + rows['burst_count_log1p'].fill(0) + rows['dur_s'] = np.asarray(table['dur_s'].to_numpy(zero_copy_only=False), np.float32) + scores = np.stack([np.asarray(table[name].to_numpy(zero_copy_only=False), np.float32) + for name in TARGETS], axis=1) + rows['scores'] = scores.astype(np.float16) + uids = table['uid'].to_pylist() + audio_sizes = table['audio_bytes'].to_pylist() + burst_counts = table['n_bursts'].to_pylist() + starts = table['burst_starts'].to_pylist() + ends = table['burst_ends'].to_pylist() + mapper = member_map() + for i, uid in enumerate(uids): + if not uid or len(uid.encode()) > 128: + raise RuntimeError(f'invalid UID at {path}:{i}') + rows['uid'][i] = uid.encode() + size = audio_sizes[i] + if size is None or not 0 < int(size) < 2**32: + raise RuntimeError(f'{path}:{i}: missing MP3 byte count') + moss_offset, _ = mapper.locate(str(audio), uid + '.moss.npy') + # Packer order is MP3, MOSS, JSON. The MOSS data offset follows the + # padded MP3 payload and its own 512-byte TAR header. + offset = moss_offset - 512 - ((int(size) + 511) // 512) * 512 + if offset < 512: + raise RuntimeError(f'{audio}:{uid}: impossible MP3 offset') + rows['audio_offset'][i] = offset + rows['audio_size'][i] = int(size) + if burst_counts[i] is not None: + rows['burst_count_valid'][i] = 1 + if starts[i] is not None and ends[i] is not None: + if len(starts[i]) != len(ends[i]) or len(starts[i]) > MAX_EVENTS: + raise RuntimeError(f'invalid burst intervals at {path}:{i}') + events = [(max(0., float(a)), min(30., float(b))) + for a, b in zip(starts[i], ends[i]) + if a is not None and b is not None and float(a) < 30 + and float(b) > 0 and float(a) < float(b)] + rows['burst_count_log1p'][i] = np.log1p(len(events)) + rows['burst_frame_valid'][i] = 1 + rows['event_count'][i] = len(events) + for j, (start, end) in enumerate(events): + rows['starts'][i, j] = start + rows['ends'][i, j] = end + else: + rows['burst_count_log1p'][i] = np.log1p(int(burst_counts[i])) + # Verify the inferred offset against real TAR headers at both ends of + # every shard. This catches a changed member order or byte-count schema. + fd = os.open(audio, os.O_RDONLY) + try: + for i in sorted({0, n - 1}): + header = os.pread(fd, 512, int(rows['audio_offset'][i]) - 512) + name = header[:100].split(b'\0', 1)[0] + size = int(header[124:136].rstrip(b'\0 ').strip() or b'0', 8) + if name != (uids[i] + '.mp3').encode() or size != int(rows['audio_size'][i]): + raise RuntimeError(f'{audio}:{uids[i]}: inferred MP3 offset failed TAR header check') + finally: + os.close(fd) + # Keep consecutive training batches near the same duration. The audio + # offsets and UIDs travel with their score and burst targets. + rows = rows[np.argsort(rows['dur_s'], kind='stable')] + out.parent.mkdir(parents=True, exist_ok=True) + tmp = out.with_suffix('.npy.incomplete') + with tmp.open('wb') as stream: + np.save(stream, rows, allow_pickle=False) + stream.flush() + os.fsync(stream.fileno()) + tmp.replace(out) + return {'level': level(path), 'parquet': str(path), 'audio': str(audio), + 'meta': str(out), 'rows': n} + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument('--workers', type=int, default=32) + parser.add_argument('--limit', type=int, default=0, + help='Only for a small smoke test; no complete manifest') + args = parser.parse_args() + paths = sorted(str(p) for p in INDEX.rglob('*.parquet')) + if args.limit: + paths = paths[:args.limit] + print('shards', len(paths), 'workers', args.workers, flush=True) + shards = [] + with ProcessPoolExecutor(max_workers=args.workers) as pool: + futures = {pool.submit(process, path): path for path in paths} + for completed, future in enumerate(as_completed(futures), 1): + result = future.result() + shards.append(result) + if completed % 100 == 0 or completed == len(paths): + print('indexed', completed, '/', len(paths), flush=True) + shards.sort(key=lambda x: (LEVELS.index(x['level']), x['parquet'])) + counts = Counter() + for shard in shards: + counts[shard['level']] += shard['rows'] + if not args.limit: + for i, stage in enumerate(('S10', 'S9', 'S8', 'S7', 'S6', 'S5', + 'S4', 'S3', 'S2', 'S1'), 1): + expected = LADDER_STAGE_ROWS[stage] + actual = sum(counts[name] for name in LEVELS[:i]) + if actual != expected: + raise RuntimeError(f'{stage}: indexed {actual}, expected {expected}') + report = {'schema_version': 'full-ladder-random-access-v1', + 'status': 'smoke' if args.limit else 'complete', + 'target_names': TARGETS, 'dtype_descr': DTYPE.descr, + 'source_index': str(INDEX), 'source_audio': str(AUDIO), + 'offset_method': 'derive MP3 offset from packed MOSS member map and Parquet audio_bytes; verify first and last TAR headers per shard', + 'levels': list(LEVELS), 'level_counts': dict(counts), + 'shards': shards} + filename = 'smoke_manifest.json' if args.limit else 'manifest.json' + out = OUT / filename + out.parent.mkdir(parents=True, exist_ok=True) + tmp = out.with_suffix(out.suffix + '.incomplete') + tmp.write_text(json.dumps(report, indent=2) + '\n') + tmp.replace(out) + print('COMPLETE', filename, dict(counts), flush=True) + + +if __name__ == '__main__': + main() diff --git a/training/prepare_p3_frozen.py b/training/prepare_p3_frozen.py new file mode 100644 index 0000000000000000000000000000000000000000..4023a3829ba35f00f33445c0541768e68d7f03e8 --- /dev/null +++ b/training/prepare_p3_frozen.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python3 +"""Index frozen P3 WebDataset TARs and remove exact-source holdout overlap.""" +from __future__ import annotations + +import argparse +import json +import math +import subprocess +import tarfile +from collections import Counter +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np + +from p3_target_adapter import target_names + + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3/frozen_1120k') +DTYPE = np.dtype([ + ('sample_id', ' int: + if not key.startswith('p3_') or not key[3:].isdigit(): + raise RuntimeError(f'bad P3 key: {key}') + number = int(key[3:]) + if number // 10000 != shard: + raise RuntimeError(f'{key} does not belong to shard {shard}') + return shard_position * 10000 + number % 10000 + + +def groups(meta: dict) -> frozenset[str]: + sources = [item['source'] for item in meta['components'] + meta['events']] + result = [] + for source in sources: + if source.get('sha256'): + source_id = 'sha256:' + source['sha256'] + elif source.get('hf_dataset') and source.get('key'): + source_id = source['hf_dataset'] + ':' + source['key'] + else: + raise RuntimeError(f"{meta['sample_id']}: source lacks stable identity") + result.append(source_id) + return frozenset(result) + + +def score_schema(example: dict) -> list[str]: + names = target_names() + extra = sorted(example['extra_eiv_scores']) + attrs = sorted(example['voiceclap_attribute_scores']) + if len(names) != 100 or len(extra) != 19 or len(attrs) != 61: + raise RuntimeError('wrong P3 score schema') + return (names + [f'eiv_extra:{x}' for x in extra] + + [f'audiobox:{x}' for x in ('CE', 'CU', 'PC', 'PQ')] + + [f'dnsmos:{x}' for x in DNSSCORES] + + ['burst_count_log1p'] + [f'voiceclap_attribute:{x}' for x in attrs]) + + +def score_values(row: dict, names: list[str]) -> np.ndarray: + values = [] + for name in names: + if name.startswith('eiv_extra:'): + value = row['extra_eiv_scores'][name.split(':', 1)[1]] + elif name.startswith('audiobox:'): + value = row['audiobox_scores'][name.split(':', 1)[1]] + elif name.startswith('dnsmos:'): + value = row['dnsmos_scores'][name.split(':', 1)[1]] + elif name.startswith('voiceclap_attribute:'): + value = (row['voiceclap_attribute_scores'][name.split(':', 1)[1]] + if row['voiceclap_attributes_valid_for_speech'] else None) + elif name == 'burst_count_log1p': + value = row[name] + else: + value = row['ladder_scores'][name] + values.append(math.nan if value is None else float(value)) + return np.asarray(values, dtype=np.float64) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument('--root', type=Path, default=ROOT) + ap.add_argument('--validation-shard', type=int, default=188) + ap.add_argument('--test-shard', type=int, default=200) + args = ap.parse_args() + root = args.root + frozen = json.loads((root / 'frozen_manifest.json').read_text()) + shards = frozen['shards'] + if len(shards) != 112 or args.validation_shard == args.test_shard: + raise RuntimeError('wrong frozen snapshot or identical holdout shards') + if {args.validation_shard, args.test_shard} - {x['number'] for x in shards}: + raise RuntimeError('holdout shard is not frozen') + n = len(shards) * 10000 + index_path = root / 'sample_index.npy' + rows = np.lib.format.open_memmap(index_path.with_suffix('.npy.incomplete'), + mode='w+', dtype=DTYPE, shape=(n,)) + source_groups: list[frozenset[str] | None] = [None] * n + shard_ids = np.empty(n, dtype=np.uint16) + for shard_position, shard in enumerate(shards): + number = shard['number'] + shard_ids[shard_position * 10000:(shard_position + 1) * 10000] = number + audio_path = Path(shard['audio']['path']) + counts = Counter() + for archive, member in members(audio_path): + if member.name.endswith('.opus'): + pos = sample_number(member.name[:-5], number, shard_position) + rows[pos]['audio_offset'] = member.offset_data + rows[pos]['audio_size'] = member.size + counts['audio'] += 1 + elif member.name.endswith('.json'): + pos = sample_number(member.name[:-5], number, shard_position) + meta = json.load(archive.extractfile(member)) + rows[pos]['sample_id'] = int(meta['sample_id'][3:]) + rows[pos]['shard'] = number + rows[pos]['positive'] = int(bool(meta['events'])) + rows[pos]['duration_s'] = float(meta['duration_s']) + source_groups[pos] = groups(meta) + counts['meta'] += 1 + if counts != {'audio': 10000, 'meta': 10000}: + raise RuntimeError(f'{number}: incomplete audio TAR {counts}') + print('audio indexed', number, flush=True) + if any(x is None for x in source_groups): + raise RuntimeError('some source groups are missing') + val_groups, test_groups = set(), set() + for i, sample_groups in enumerate(source_groups): + if shard_ids[i] == args.validation_shard: + val_groups.update(sample_groups) + elif shard_ids[i] == args.test_shard: + test_groups.update(sample_groups) + holdout_groups = val_groups | test_groups + exclusions = Counter() + for i, sample_groups in enumerate(source_groups): + if shard_ids[i] == args.validation_shard: + split = 'validation' + elif shard_ids[i] == args.test_shard: + split = 'test' if sample_groups.isdisjoint(val_groups) else 'excluded' + if split == 'excluded': + exclusions['test_overlap_validation'] += 1 + else: + split = 'train' if sample_groups.isdisjoint(holdout_groups) else 'excluded' + if split == 'excluded': + exclusions['train_overlap_holdouts'] += 1 + rows[i]['split'] = SPLITS[split] + del source_groups + print('source-disjoint split complete', dict(exclusions), flush=True) + score_names = None + stats = {name: {'count': None, 'sum': None, 'sum2': None} + for name in ('train', 'validation', 'test')} + frame_counts = Counter() + for shard_position, shard in enumerate(shards): + number = shard['number'] + counts = Counter() + for archive, member in members(Path(shard['sidecars']['targets']['path'])): + if not member.name.endswith('.targets.json'): + raise RuntimeError(f'{number}: unexpected target member {member.name}') + key = member.name[:-len('.targets.json')] + pos = sample_number(key, number, shard_position) + rows[pos]['targets_offset'] = member.offset_data + rows[pos]['targets_size'] = member.size + target = json.load(archive.extractfile(member)) + if target['sample_id'] != key: + raise RuntimeError(f'{key}: target mismatch') + if score_names is None: + score_names = score_schema(target) + for data in stats.values(): + data['count'] = np.zeros(len(score_names), np.int64) + data['sum'] = np.zeros(len(score_names), np.float64) + data['sum2'] = np.zeros(len(score_names), np.float64) + split = next(name for name, code in SPLITS.items() + if code == rows[pos]['split']) + if split in stats: + values = score_values(target, score_names) + valid = np.isfinite(values) + clean = np.where(valid, values, 0) + data = stats[split] + data['count'] += valid + data['sum'] += clean + data['sum2'] += clean * clean + frame_counts[split] += len(target['burst_frame_labels']) + counts['targets'] += 1 + if counts['targets'] != 10000: + raise RuntimeError(f'{number}: incomplete targets {counts}') + for kind, (source_kind, suffix) in EMBEDDINGS.items(): + counts[kind] = 0 + for _archive, member in members(Path(shard['sidecars'][source_kind]['path'])): + if not member.name.endswith(suffix): + continue + pos = sample_number(member.name[:-len(suffix)], number, shard_position) + rows[pos][f'{kind}_offset'] = member.offset_data + rows[pos][f'{kind}_size'] = member.size + counts[kind] += 1 + if counts[kind] != 10000: + raise RuntimeError(f'{number}: {kind} embeddings incomplete') + print('targets and embeddings indexed', number, flush=True) + for kind in ('audio', 'targets', 'timbre', 'identity', 'voiceclap', 'bud_e'): + if np.any(rows[f'{kind}_size'] == 0): + raise RuntimeError(f'missing {kind} offsets') + split_counts = {} + for name, code in SPLITS.items(): + indexes = np.flatnonzero(rows['split'] == code).astype(np.uint32) + np.save(root / f'{name}_indices.npy', indexes) + split_counts[name] = {'samples': int(len(indexes)), + 'positive': int(rows['positive'][indexes].sum()), + 'negative': int(len(indexes) - rows['positive'][indexes].sum())} + if any(split_counts[x]['samples'] == 0 for x in ('train', 'validation', 'test')): + raise RuntimeError(f'empty split: {split_counts}') + train = stats['train'] + count = np.maximum(train['count'], 1) + mean = train['sum'] / count + variance = (train['sum2'] / count - mean * mean).clip(min=0) + std = np.where(train['count'] > 0, np.maximum(np.sqrt(variance), .25), 1) + mean = np.where(train['count'] > 0, mean, 0) + norm = {'score_names': score_names, 'mean': mean.tolist(), 'std': std.tolist(), + 'train_valid_counts': train['count'].tolist(), + 'validation_valid_counts': stats['validation']['count'].tolist(), + 'test_valid_counts': stats['test']['count'].tolist(), + 'method': 'train-only mean, population std with minimum 0.25; zero-count targets masked'} + (root / 'score_normalization.json').write_text(json.dumps(norm, indent=2) + '\n') + rows.flush() + del rows + index_path.with_suffix('.npy.incomplete').replace(index_path) + report = {'schema_version': 'p3-frozen-training-index-v1', + 'created_utc': datetime.now(timezone.utc).isoformat(), + 'frozen_manifest': str(root / 'frozen_manifest.json'), + 'index': str(index_path), 'dtype_descr': DTYPE.descr, + 'total_samples': n, 'split_counts': split_counts, + 'holdout_shards': {'validation': args.validation_shard, + 'test': args.test_shard}, + 'holdout_exact_source_id_count': {'validation': len(val_groups), + 'test': len(test_groups)}, + 'exclusions': dict(exclusions), 'frame_counts': dict(frame_counts), + 'normalization': str(root / 'score_normalization.json'), + 'split_method': 'two balanced completed shards held out; train clips reusing any exact holdout source audio excluded; test clips reusing any validation source audio excluded', + 'source_identity_scope': 'dataset-qualified source key or burst SHA-256 across speech, bursts, AudioSet effects and music', + 'speaker_disjoint': False, + 'annotation_note': 'teacher values are model predictions; masks retain domain validity'} + tmp = root / 'training_index_report.json.incomplete' + tmp.write_text(json.dumps(report, indent=2) + '\n') + tmp.replace(root / 'training_index_report.json') + print(report['split_counts'], flush=True) + subprocess.run(['python', str(Path(__file__).with_name('p3_frozen_mix_schedule.py')), + '--root', str(root)], check=True) + if root == ROOT: + subprocess.run(['python', str(Path(__file__).with_name('verify_p3_frozen.py'))], + check=True) + subprocess.run(['python', str(Path(__file__).with_name('write_p3_frozen_report.py'))], + check=True) + + +if __name__ == '__main__': + main() diff --git a/training/provenance/TRAINING_READY.json b/training/provenance/TRAINING_READY.json new file mode 100644 index 0000000000000000000000000000000000000000..49201beb67a3922c40be2f79d2a3a2e39be6f4cd --- /dev/null +++ b/training/provenance/TRAINING_READY.json @@ -0,0 +1,229 @@ +{ + "schema": "gemini-finetune-preparation-v1", + "updated_utc": "2026-10-06T21:50:56.124115+00:00", + "selected_manifest": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_multisource_100h_20261004/combined_unique_manifest.jsonl", + "snapshot_only": false, + "gemini_annotations_finished": true, + "counts": { + "selected": 66199, + "gemini_valid": 65163, + "gemini_pending_or_invalid": 1036, + "ready_for_whisper_training": 65163 + }, + "splits": { + "train": 59292, + "test": 3608, + "validation": 3299 + }, + "teacher_backfill_missing": {}, + "valid_score_counts": { + "emo_Affection": 66199, + "emo_Amusement": 66199, + "emo_Anger": 66199, + "emo_Astonishment_Surprise": 66199, + "emo_Awe": 66199, + "emo_Bitterness": 66199, + "emo_Concentration": 66199, + "emo_Confusion": 66199, + "emo_Contemplation": 66199, + "emo_Contempt": 66199, + "emo_Contentment": 66199, + "emo_Disappointment": 66199, + "emo_Disgust": 66199, + "emo_Distress": 66199, + "emo_Doubt": 66199, + "emo_Elation": 66199, + "emo_Embarrassment": 66199, + "emo_Emotional_Numbness": 66199, + "emo_Fatigue_Exhaustion": 66199, + "emo_Fear": 66199, + "emo_Helplessness": 66199, + "emo_Hope_Enthusiasm_Optimism": 66199, + "emo_Impatience_and_Irritability": 66199, + "emo_Infatuation": 66199, + "emo_Interest": 66199, + "emo_Intoxication_Altered_States_of_Consciousness": 66199, + "emo_Jealousy_and_Envy": 66031, + "emo_Longing": 66199, + "emo_Malevolence_Malice": 66199, + "emo_Pain": 66199, + "emo_Pleasure_Ecstasy": 66199, + "emo_Pride": 66199, + "emo_Relief": 66199, + "emo_Sadness": 66199, + "emo_Sexual_Lust": 66199, + "emo_Shame": 66199, + "emo_Sourness": 66199, + "emo_Teasing": 66199, + "emo_Thankfulness_Gratitude": 66199, + "emo_Triumph": 66199, + "vn_AGEV_reg": 66199, + "vn_AROU_reg": 66199, + "vn_ARSH_reg": 66199, + "vn_ATCK_reg": 66199, + "vn_BKGN_reg": 66199, + "vn_BRGT_reg": 66199, + "vn_CHNK_reg": 66199, + "vn_CLRT_reg": 66199, + "vn_COGL_reg": 66199, + "vn_DARC_reg": 66199, + "vn_DFLU_reg": 66199, + "vn_EMPH_reg": 66199, + "vn_ESTH_reg": 66199, + "vn_EXPL_reg": 66199, + "vn_FOCS_reg": 66199, + "vn_FULL_reg": 66199, + "vn_GEND_reg": 66199, + "vn_HARM_reg": 66199, + "vn_METL_reg": 66199, + "vn_RANG_reg": 66199, + "vn_RCQL_reg": 66199, + "vn_REGS_reg": 66199, + "vn_RESP_reg": 66199, + "vn_ROUG_reg": 66199, + "vn_R_CHST_reg": 66199, + "vn_R_HEAD_reg": 66199, + "vn_R_MASK_reg": 66199, + "vn_R_MIXD_reg": 66199, + "vn_R_NASL_reg": 66199, + "vn_R_ORAL_reg": 66199, + "vn_R_THRT_reg": 66199, + "vn_SMTH_reg": 66199, + "vn_STNC_reg": 66199, + "vn_STRU_reg": 66199, + "vn_S_ASMR_reg": 66199, + "vn_S_AUTH_reg": 66199, + "vn_S_CART_reg": 66199, + "vn_S_CASU_reg": 66199, + "vn_S_CONV_reg": 66199, + "vn_S_DRAM_reg": 66199, + "vn_S_FORM_reg": 66199, + "vn_S_MONO_reg": 66199, + "vn_S_NARR_reg": 66199, + "vn_S_NEWS_reg": 66199, + "vn_S_PLAY_reg": 66199, + "vn_S_RANT_reg": 66199, + "vn_S_STRY_reg": 66199, + "vn_S_TECH_reg": 66199, + "vn_S_WHIS_reg": 66199, + "vn_TEMP_reg": 66199, + "vn_TENS_reg": 66199, + "vn_VALN_reg": 66199, + "vn_VALS_reg": 66199, + "vn_VFLX_reg": 66199, + "vn_VOLT_reg": 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Invalid/blocked Flash responses are quarantined and excluded from tuning, with their IDs and reasons retained.", + "gemini_priority": "Strictly schema-valid Flash annotation supersedes matching prior scores and event spans; original labels retained for audit.", + "output": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_finetune_preparation_20261005/prepared" +} diff --git a/training/provenance/selected_pool.json b/training/provenance/selected_pool.json new file mode 100644 index 0000000000000000000000000000000000000000..bdfc2cc347fa526f79fef2050958c7454ac02fa4 --- /dev/null +++ b/training/provenance/selected_pool.json @@ -0,0 +1,724 @@ +{ + "schema_version": "gemini-multisource-100h-combined-v1", + "status": "READY", + "selected_clips": 66199, + "selected_hours": 197.35925378888888, + "s1": { + "unique_clips": 43569, + "hours": 99.98232277777778, + "input_clips": 43583, + "input_hours": 100.02266083333366, + "buckets": 400 + }, + "additional_subsets": { + "talent": 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"rcql": 100, + "regs": 100, + "relf": 100, + "resp": 100, + "robt": 100, + "roug": 100, + "s_asmr": 100, + "s_auth": 100, + "s_cart": 100, + "s_casu": 100, + "s_conv": 100, + "s_dram": 100, + "s_form": 100, + "s_mono": 100, + "s_narr": 100, + "s_news": 100, + "s_play": 100, + "s_rant": 100, + "s_stry": 100, + "s_tech": 100, + "s_whis": 100, + "sadn": 100, + "sale": 100, + "scon": 21, + "scre": 100, + "sexp": 15, + "sham": 100, + "shon": 100, + "shri": 100, + "sinh": 100, + "slsh": 32, + "smth": 100, + "snck": 57, + "snee": 3, + "snob": 100, + "snor": 26, + "snrk": 100, + "snrt": 52, + "sobc": 100, + "sobq": 100, + "sour": 26, + "sque": 100, + "srel": 54, + "stnc": 100, + "ston": 21, + "stru": 100, + "surp": 100, + "swis": 12, + "tbro": 100, + "tclr": 33, + "teas": 100, + "temp": 100, + "tens": 100, + "thrp": 100, + "trck": 31, + "triu": 100, + "trlr": 100, + "tskk": 15, + "valn": 100, + "vals": 100, + "vamp": 100, + "vflx": 100, + "viln": 100, + "volt": 100, + "vuln": 100, + "wail": 72, + "warm": 100, + "whim": 67, + "wizd": 100, + "wtch": 47, + "yawn": 9, + "zomb": 100 + }, + "unique_clips_within_source": 7643, + "unique_hours_within_source": 17.244698347222222 + } + }, + "duplicate_memberships_across_all_sources_and_tasks": 8165, + "input_slots": 74364, + "input_hours": 217.55793942222255, + "duplicate_hours_removed": 20.198685633333668, + "source_revisions": { + "laion/laions_got_talent_raw": "0a79709a6194d94c63b2b1275d2acc9abd3bf3e2", + "mitermix/balanced_audio_snippets_40x3k": "37011063d917e68658d51151127cb32c24ffea1b", + "mitermix/voice-annotation-poc": "31041279ad7cb078da15fbeb58e6dfa96283f320" + }, + "combined_manifest": "/e/scratch/reformo/schuhmann1_moss/whisper_score_regression/gemini_multisource_100h_20261004/combined_unique_manifest.jsonl", + "annotation_policy": "One Gemini request per unique SHA256 audio clip; keep all bucket/task memberships in the sidecar." +} diff --git a/training/provenance/valid_split_summary.json b/training/provenance/valid_split_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..78f2ec28f885144ef7c8d984c5d8792e1ce5edd3 --- /dev/null +++ b/training/provenance/valid_split_summary.json @@ -0,0 +1,210 @@ +{ + "valid_clips": 65163, + "split_clips": { + "train": 58380, + "test": 3544, + "validation": 3239 + }, + "split_hours": { + "train": 174.0395300111143, + "test": 10.389036091666668, + "validation": 9.740547116666654 + }, + "valid_total_hours": 194.1691132194476, + "language_strings": { + "Russian": 104, + "English": 32223, + "German": 22228, + "Spanish": 2849, + "Hindi": 83, + "German/English": 185, + "French": 2901, + "unclear": 2744, + "Vietnamese": 65, + "Pashto": 1, + "Japanese": 302, + "English / Mandarin Chinese": 1, + "Portuguese": 65, + "Arabic": 92, + "Mandarin Chinese/English": 3, + "German/French/English": 1, + "Malayalam": 24, + "Hungarian": 14, + "Tagalog": 19, + "Swahili": 11, + "English/French": 7, + "en": 99, + "Burmese": 8, + "English/German": 45, + "Romanian": 31, + "Ukrainian": 25, + "Urdu": 44, + "French/German/English": 1, + "Indonesian": 30, + "Bengali": 21, + "English / German": 12, + "Sanskrit": 8, + "German/Mandarin Chinese": 6, + "none": 7, + "Arabic/Thai": 1, + "French/English": 78, + "Gujarati": 8, + "Kinyarwanda": 3, + "Tagalog/English": 2, + "English / Vietnamese": 1, + "Greek": 10, + "Korean": 65, + "Sinhala": 9, + "Nepali": 11, + "English / Tagalog": 1, + "Igbo": 1, + "Haitian Creole": 8, + "Khmer": 24, + "English / Russian": 4, + "Polish/English": 1, + "English/Spanish": 41, + "Hebrew": 14, + "Scots": 7, + "Latvian": 1, + "Russian/English": 13, + "German/Mandarin": 3, + "Swedish": 19, + "Tamil": 29, + "Icelandic": 11, + "Persian": 10, + "Armenian": 4, + "Thai": 38, + "Polish": 25, + "Latin": 3, + "Mandarin Chinese": 68, + "Kannada": 9, + "English/Spanish/Japanese": 2, + "Turkish": 13, + "French/Arabic": 1, + "Dutch": 14, + "Uzbek": 2, + "Luxembourgish": 3, + "Korean/English": 1, + "Cantonese": 10, + "Mandarin": 3, + "Indonesian/English": 1, + "English / Spanish": 18, + "Telugu": 34, + "English/Sanskrit": 2, + "zxx": 11, + "Javanese": 10, + "Danish": 2, + "Macedonian": 1, + "Italian": 33, + "Mandarin Chinese / Dutch": 1, + "German/Japanese": 5, + "Kurdish": 2, + "Italian/English": 3, + "Hawaiian/English": 1, + "English / Malayalam": 1, + "Spanish/English": 14, + "Punjabi": 15, + "Norwegian": 3, + "Mandarin/German": 1, + "Catalan": 1, + "Japanese/English": 6, + "Yiddish": 1, + "English / Japanese": 2, + "Mandarin/English": 1, + "Finnish": 4, + "Azerbaijani": 1, + "Czech": 7, + "German / English": 5, + "Arabic/English": 4, + "Assamese": 3, + "Jamaican Patois": 4, + "Mandarin Chinese/German": 4, + "German/Turkish": 3, + "Slovak": 2, + "Luganda": 2, + "Bulgarian": 6, + "English/Japanese": 2, + "Urdu/English": 1, + "Twi": 5, + "German/Russian": 1, + "Spanish / English": 7, + "Korean/Mongolian": 1, + "French / English": 2, + "Swiss German": 7, + "English/Turkish": 1, + "Marathi": 6, + "English / Italian": 2, + "English/Hindi": 1, + "English/Italian": 5, + "German/Swedish": 1, + "Hindi/English": 3, + "German/Polish": 1, + "Urdu/Arabic": 1, + "English / Korean": 2, + "English / French": 8, + "French/German": 1, + "Vietnamese/English": 2, + "Somali": 4, + "Malay": 1, + "Georgian": 2, + "Arabic / English": 1, + "Mandarin / German": 1, + "Japanese/German": 2, + "English/Pali": 1, + "English/Russian": 2, + "English/Mandarin": 2, + "Serbian": 1, + "Korean / English": 1, + "German/Persian": 1, + "Bhojpuri": 1, + "Portuguese / English": 1, + "Fula / Arabic": 1, + "Sanskrit/Tamil": 1, + "English / Arabic": 2, + "Hindi / English": 1, + "English / Hindi": 1, + "Croatian": 1, + "Sundanese": 1, + "Ukrainian/Russian": 1, + "Moroccan Arabic": 1, + "English / Urdu": 1, + "German/Hungarian": 1, + "Māori/English": 1, + "German/unclear": 1, + "Albanian": 1, + "English / [unclear]": 1, + "Klingon": 1, + "Arabic/Urdu": 1, + "French/Portuguese/English": 1, + "Arabic/Malayalam": 1, + "Scots/Scottish English": 1, + "Isan / Lao": 1, + "Lao": 1, + "German/Arabic": 1, + "Turkish/English": 1, + "Low German": 1, + "Russian/Polish": 1, + "Modern Greek": 1, + "Kazakh": 1, + "multilingual": 1, + "English / Nigerian Pidgin": 1, + "Taiwanese Hokkien": 1, + "Saraiki": 1, + "German/Italian": 1, + "Hokkien": 1, + "English/Portuguese": 1, + "English / Bengali": 1, + "German/Finnish": 1 + }, + "source_membership_counts_nonexclusive": { + "S1_100h": 42701, + "poc": 7579, + "talent": 10917, + "balanced_40x3k": 3979 + }, + "prepared_targets_sha256": "4ebac2622f35d6ce91f7956c0f86c16276d6f45a19e1d1ff19f6eaaace6dfe44", + "deduplication": "Exact selected compressed audio SHA256 across all selected sources/tasks", + "split": "90/5/5 deterministic group hash; explicit speaker or synthetic family before exact audio SHA", + "excluded_clips": 1036, + "captions_used_as_model_inputs": false +} diff --git a/training/report.py b/training/report.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3116149c79737ac07b2f4d30a842ae06e200ec --- /dev/null +++ b/training/report.py @@ -0,0 +1,97 @@ +"""Summarize the four controlled runs against a constant-label baseline.""" +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression') +RUNS = Path('/e/project1/reformo/schuhmann1/whisper_score_regression/runs') +CONDITIONS = [('tiny', 'S10'), ('base', 'S10'), ('tiny', 'S9'), ('base', 'S9')] + + +def baseline(meta): + with np.load(ROOT / 'val.npz') as val: + y = val['y'].astype(np.float64) + mean = np.asarray(meta['mean']) + std = np.asarray(meta['std']) + z = (y - mean) / std + abs_z = np.abs(z) + huber = np.where(abs_z < 1, 0.5 * z * z, abs_z - 0.5) + result = {'huber_z_macro': float(np.nanmean(huber))} + for name, (a, b) in meta['target_groups'].items(): + result[name] = {'huber_z': float(np.nanmean(huber[:, a:b])), + 'mae': float(np.nanmean(np.abs(y[:, a:b] - mean[a:b])))} + return result + + +def main(): + meta = json.loads((ROOT / 'manifest.json').read_text()) + runs = {} + for model, stage in CONDITIONS: + path = RUNS / f'{model}_{stage.lower()}' / 'metrics.jsonl' + if path.is_file(): + epochs = [json.loads(line) for line in path.read_text().splitlines() if line] + runs[f'{model}_{stage}'] = min(epochs, key=lambda x: x['huber_z_macro']) + base = baseline(meta) + out = {'baseline': base, 'best_runs': runs, 'complete': len(runs) == 4} + RUNS.parent.mkdir(parents=True, exist_ok=True) + (RUNS.parent / 'comparison.json').write_text(json.dumps(out, indent=2) + '\n') + lines = [ + '# Whisper score regression: Tiny/Base × S10/S9', '', + f"Training clips: S10 {meta['rows']['S10']:,}; S9 {meta['rows']['S9']:,}. " + f"Common validation: {meta['rows']['val']:,} held-out clips.", '', + 'Encoder parameters: Tiny 8,208,384; Base 20,590,592 (2.51×). ' + 'S9 has 2.41× as many training clips as S10.', '', + 'All metrics use the same 100 regression targets. Huber is averaged after ' + 'standardizing each target with S10 training statistics; lower is better.', '', + '| Encoder | Stage | Best epoch | Train Huber | Val Huber | Emotion Huber | VoiceNet Huber | Other Huber |', + '|---|---|---:|---:|---:|---:|---:|---:|', + f"| Constant training mean | — | — | — | {base['huber_z_macro']:.4f} | " + f"{base['emotion']['huber_z']:.4f} | {base['voicenet']['huber_z']:.4f} | {base['other']['huber_z']:.4f} |", + ] + for model, stage in CONDITIONS: + r = runs.get(f'{model}_{stage}') + if r is None: + lines.append(f'| {model} | {stage} | pending | — | — | — | — | — |') + else: + lines.append(f"| {model} | {stage} | {r['epoch']} | {r['train_huber_z']:.4f} | " + f"{r['huber_z_macro']:.4f} | {r['emotion']['huber_z']:.4f} | " + f"{r['voicenet']['huber_z']:.4f} | {r['other']['huber_z']:.4f} |") + if len(runs) == 4: + lines += ['', '## Controlled differences', ''] + for stage in ['S10', 'S9']: + t, b = runs[f'tiny_{stage}']['huber_z_macro'], runs[f'base_{stage}']['huber_z_macro'] + lines.append(f'- {stage}, Base versus Tiny: {b - t:+.4f} normalized Huber (negative favors Base).') + for model in ['tiny', 'base']: + s10, s9 = runs[f'{model}_S10']['huber_z_macro'], runs[f'{model}_S9']['huber_z_macro'] + lines.append(f'- {model}, S9 versus S10: {s9 - s10:+.4f} normalized Huber (negative favors S9).') + lines += ['', '## Correlation and target patterns', ''] + for model, stage in CONDITIONS: + r = runs[f'{model}_{stage}'] + lines.append(f"- {model}/{stage}: mean Pearson emotion {r['emotion']['pearson']:.3f}, " + f"VoiceNet {r['voicenet']['pearson']:.3f}, other {r['other']['pearson']:.3f}.") + for title, left, right in [ + ('Base minus Tiny on S10', 'tiny_S10', 'base_S10'), + ('Base minus Tiny on S9', 'tiny_S9', 'base_S9'), + ('S9 minus S10 for Tiny', 'tiny_S10', 'tiny_S9'), + ('S9 minus S10 for Base', 'base_S10', 'base_S9'), + ]: + difference = np.asarray(runs[right]['huber_z']) - np.asarray(runs[left]['huber_z']) + improved = np.argsort(difference)[:5] + worsened = np.argsort(difference)[-5:][::-1] + fmt = lambda ids: ', '.join(f"`{meta['targets'][i]}` {difference[i]:+.3f}" for i in ids) + lines += ['', f'**{title}.** Five largest gains: {fmt(improved)}. ' + f'Five largest regressions: {fmt(worsened)}.'] + lines += ['', 'The reference labels are teacher scores from the Ladder, not independent human ratings.', + 'Audio longer than 30 seconds is truncated to the first 30 seconds by the Whisper input limit.', + 'The validation is UID-disjoint; it is not guaranteed speaker-disjoint.', + 'At equal epoch count, S9 also receives more optimizer updates; data size and compute exposure ' + 'are therefore coupled in the S9-versus-S10 contrast.', ''] + (RUNS.parent / 'comparison.md').write_text('\n'.join(lines)) + print('\n'.join(lines)) + + +if __name__ == '__main__': + main() diff --git a/training/tar_member_map.py b/training/tar_member_map.py new file mode 100644 index 0000000000000000000000000000000000000000..b9746c94bf41b904bcfeab6ff66347b97d240c37 --- /dev/null +++ b/training/tar_member_map.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 +"""Packed, read-only map from TAR path/member name to byte offset and size. + +The corpus stays in the existing large TAR containers. A single compressed +record per TAR replaces repeated full TAR-directory scans on every DDP rank. +""" +from collections import OrderedDict +import json +import os +from pathlib import Path +import threading +import zlib + + +class TarMemberMap: + def __init__(self, manifest, capacity=256): + self.manifest_path = Path(manifest) + spec = json.loads(self.manifest_path.read_text()) + assert spec['status'] == 'complete' and spec['format'] == 'zlib-json-tar-member-map-v1' + root = self.manifest_path.parent + index_path = root / spec['index'] + data_path = root / spec['data'] + assert index_path.stat().st_size == spec['index_bytes'] + assert data_path.stat().st_size == spec['data_bytes'] + index = json.loads(index_path.read_text()) + assert len(index['paths']) == spec['tars'] + assert all(len(index[k]) == spec['tars'] for k in ('offsets', 'sizes', 'members', 'tar_sizes')) + self.rows = {path: (int(index['offsets'][i]), int(index['sizes'][i]), + int(index['members'][i]), int(index['tar_sizes'][i])) + for i, path in enumerate(index['paths'])} + self.fd = os.open(data_path, os.O_RDONLY) + self.capacity = int(capacity) + self.local = threading.local() + + def _cache(self): + if not hasattr(self.local, 'members'): + self.local.members = OrderedDict() + return self.local.members + + def locate(self, path, member): + cache = self._cache() + if path not in cache: + assert path in self.rows, f'TAR absent from packed member map: {path}' + offset, size, expected_members, expected_tar_size = self.rows[path] + assert os.path.getsize(path) == expected_tar_size, f'TAR size changed after index build: {path}' + compressed = os.pread(self.fd, size, offset) + assert len(compressed) == size + decoded = json.loads(zlib.decompress(compressed)) + assert len(decoded) == expected_members + cache[path] = decoded + while len(cache) > self.capacity: + cache.popitem(last=False) + cache.move_to_end(path) + value = cache[path].get(member) + assert value is not None, f'Member absent from packed map: {path}::{member}' + return int(value[0]), int(value[1]) + diff --git a/training/timbre_targets.py b/training/timbre_targets.py new file mode 100644 index 0000000000000000000000000000000000000000..2c3af9e222e51c2270d6876581afcf8998790433 --- /dev/null +++ b/training/timbre_targets.py @@ -0,0 +1,419 @@ +"""Reusable Orange timbre and identity targets for Whisper training. + +Build separate sorted UID indexes from the existing Parquet sidecars once. +During training, ``TimbreTargetCache.get_or_compute`` uses existing vectors +and computes only missing 128-D timbre or 250-D identity vectors from the +same decoded waveform. New vectors are persisted as rank-owned Parquet chunks; +at the next stage boundary, index those chunks so S2..S10 and the second +Whisper run read them without teacher inference. + +Both teachers require compute-visible model snapshots. Their licenses are +staged alongside the weights. No CUDA work is done in DataLoader workers. +""" +from __future__ import annotations + +import argparse +from collections import OrderedDict +import json +import os +from pathlib import Path +from typing import Sequence +import zlib + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq +import torch +from safetensors import safe_open +from torch import nn +from torch.nn import functional as F +from transformers import WavLMConfig +from transformers.models.wavlm.modeling_wavlm import WavLMModel + + +DIM = 128 +IDENTITY_DIM = 250 +UID_DTYPE = 'S128' +DEFAULT_MODEL = Path('/e/project1/reformo/schuhmann1/whisper_score_regression/models/orange_speaker_wavlm_tbr_b8d2608') +IDENTITY_MODEL = Path('/e/project1/reformo/schuhmann1/whisper_score_regression/models/orange_speaker_wavlm_id_abbb3c7') +EMOLIA_SOURCE = Path('/e/scratch/reformo/schuhmann1_moss/out/refassign/emolia_emb.parquet') +IDENTITY_SOURCE = Path('/e/project1/reformo/schuhmann1/whisper_score_regression/sources/orange_id_kartoffelphon') + + +class TopLayers(nn.Module): + """Orange's two-layer projection; names match the published checkpoint.""" + + def __init__(self, embd_size: int, intermediate: int): + super().__init__() + self.affine1 = nn.Conv1d(2048, intermediate, 1) + self.batchnorm1 = nn.BatchNorm1d(intermediate, affine=False, eps=1e-3) + self.affine2 = nn.Conv1d(intermediate, embd_size, 1) + self.batchnorm2 = nn.BatchNorm1d(embd_size, affine=False, eps=1e-3) + self.activation = nn.ReLU(inplace=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + out = self.batchnorm1(self.activation(self.affine1(x))) + out = self.batchnorm2(self.activation(self.affine2(out))) + return F.normalize(out[:, :, 0], dim=-1) + + +class OrangeEmbeddingTeacher(nn.Module): + """Orange WavLM speaker embedding model with masked padded batches.""" + + def __init__(self, snapshot: Path, dim: int): + super().__init__() + config = WavLMConfig.from_pretrained(snapshot, local_files_only=True) + assert config.embd_size == dim, (config.embd_size, dim) + self.dim = dim + self.wavlm = WavLMModel(config) + self.top_layers = TopLayers(config.embd_size, config.top_interm_size) + with safe_open(snapshot / 'model.safetensors', framework='pt', device='cpu') as file: + state = {key: file.get_tensor(key) for key in file.keys()} + self.load_state_dict(state, strict=True) + self.eval() + + @torch.inference_mode() + def forward(self, samples: torch.Tensor, lengths: torch.Tensor) -> torch.Tensor: + """Input float32 16-kHz mono [B,T], output L2-normalized [B,dim].""" + assert samples.ndim == 2 and lengths.ndim == 1 and samples.shape[0] == len(lengths) + assert torch.all((lengths >= 1600) & (lengths <= samples.shape[1])) + t = samples.shape[1] + valid = (torch.arange(t, device=samples.device)[None, :] < lengths[:, None]) + n = lengths[:, None].float() + mu = (samples * valid).sum(1, keepdim=True) / n + var = (((samples - mu) * valid).square()).sum(1, keepdim=True) / (n - 1).clamp_min(1) + x = ((samples - mu) / var.clamp_min(1e-10).sqrt()) * valid + h = self.wavlm(input_values=x, attention_mask=valid.long(), + output_hidden_states=False).last_hidden_state.float() + frame_lengths = self.wavlm._get_feat_extract_output_lengths(lengths) + fvalid = (torch.arange(h.shape[1], device=h.device)[None, :] < frame_lengths[:, None])[:, :, None] + nf = fvalid.sum(1).clamp_min(1) + hm = (h * fvalid).sum(1) / nf + hv = ((h - hm[:, None, :]).square() * fvalid).sum(1) / (nf - 1).clamp_min(1) + pooled = torch.cat((hm, hv.clamp_min(1e-10).sqrt()), dim=1).unsqueeze(-1) + return self.top_layers(pooled) + + @torch.inference_mode() + def encode_waveforms(self, waveforms: Sequence[np.ndarray | torch.Tensor], + device: torch.device, batch_size: int = 4) -> np.ndarray: + """Dynamic length buckets; waveforms must already be mono at 16 kHz.""" + out = np.empty((len(waveforms), self.dim), dtype=np.float16) + order = sorted(range(len(waveforms)), key=lambda i: len(waveforms[i])) + for start in range(0, len(order), batch_size): + indices = order[start:start + batch_size] + lengths = torch.tensor([max(1600, min(len(waveforms[i]), 480000)) for i in indices], + device=device, dtype=torch.long) + max_len = max(1600, int(lengths.max())) + audio = torch.zeros((len(indices), max_len), device=device, dtype=torch.float32) + for row, i in enumerate(indices): + w = torch.as_tensor(waveforms[i], dtype=torch.float32, device=device) + count = min(len(w), 480000) + audio[row, :count] = w[:count] + pred = self(audio, lengths).float().cpu().numpy() + assert pred.shape == (len(indices), self.dim) and np.isfinite(pred).all() + out[indices] = pred.astype(np.float16) + return out + + +class OrangeTimbreTeacher(OrangeEmbeddingTeacher): + def __init__(self, snapshot: Path = DEFAULT_MODEL): + super().__init__(snapshot, DIM) + + +class OrangeIdentityTeacher(OrangeEmbeddingTeacher): + def __init__(self, snapshot: Path = IDENTITY_MODEL): + super().__init__(snapshot, IDENTITY_DIM) + + +def _uid_array(values: Sequence[str]) -> np.ndarray: + encoded = [u.encode('utf-8') for u in values] + assert all(0 < len(u) <= 128 for u in encoded) + return np.asarray(encoded, dtype=UID_DTYPE) + + +def _emb_array(column: pa.Array, dim: int = DIM) -> np.ndarray: + # Both list in Emolia and fixed_size_list in the Identity + # source/generated chunks are accepted. Reject missing/wrong-width vectors. + if isinstance(column, pa.ChunkedArray): + column = column.combine_chunks() + assert column.null_count == 0 + lens = np.diff(np.asarray(column.offsets)) if isinstance(column, pa.ListArray) else None + if lens is not None: + assert np.all(lens == dim), (lens.min(), lens.max()) + values = np.asarray(column.values.to_numpy(zero_copy_only=False), dtype=np.float32) + result = values.reshape(len(column), dim) + assert np.isfinite(result).all() + norms = np.linalg.norm(result, axis=1) + assert np.all((norms > .95) & (norms < 1.05)), (norms.min(), norms.max()) + return result.astype(np.float16) + + +def build_index(parquets: Sequence[Path], output: Path, allow_same_duplicates: bool = False, + dim: int = DIM) -> None: + """Create memory-mappable sorted UID→float16[dim] arrays atomically.""" + output.mkdir(parents=True, exist_ok=True) + paths = [Path(p) for p in parquets] + count = sum(pq.read_metadata(p).num_rows for p in paths) + assert count > 0 + raw_uid = np.lib.format.open_memmap(output / 'uids.raw.npy', mode='w+', dtype=UID_DTYPE, shape=(count,)) + raw_emb = np.lib.format.open_memmap(output / 'emb.raw.npy', mode='w+', dtype=np.float16, shape=(count, dim)) + offset = 0 + for path in paths: + for batch in pq.ParquetFile(path).iter_batches(batch_size=32768, columns=['uid', 'emb']): + n = batch.num_rows + raw_uid[offset:offset+n] = _uid_array(batch.column(0).to_pylist()) + raw_emb[offset:offset+n] = _emb_array(batch.column(1), dim) + offset += n + assert offset == count + raw_uid.flush(); raw_emb.flush() + order = np.argsort(raw_uid, kind='stable') + sorted_uid = np.lib.format.open_memmap(output / 'uids.next.npy', mode='w+', dtype=UID_DTYPE, shape=(count,)) + sorted_emb = np.lib.format.open_memmap(output / 'emb.next.npy', mode='w+', dtype=np.float16, shape=(count, dim)) + for start in range(0, count, 65536): + ix = order[start:start+65536] + sorted_uid[start:start+len(ix)] = raw_uid[ix] + sorted_emb[start:start+len(ix)] = raw_emb[ix] + sorted_uid.flush(); sorted_emb.flush() + dup = np.flatnonzero(sorted_uid[1:] == sorted_uid[:-1]) + if len(dup): + if not allow_same_duplicates: + raise ValueError(f'{len(dup)} duplicate UIDs') + for i in dup: + if not np.allclose(sorted_emb[i], sorted_emb[i+1], atol=2e-3): + raise ValueError(f'conflicting duplicate UID {sorted_uid[i]!r}') + # A compacted index is required for binary search to return one row. + keep = np.ones(count, dtype=bool); keep[dup+1] = False + unique_n = int(keep.sum()) + u2 = np.lib.format.open_memmap(output / 'uids.unique.npy', mode='w+', dtype=UID_DTYPE, shape=(unique_n,)) + e2 = np.lib.format.open_memmap(output / 'emb.unique.npy', mode='w+', dtype=np.float16, shape=(unique_n, dim)) + positions = np.flatnonzero(keep) + for start in range(0, unique_n, 65536): + ix = positions[start:start+65536] + u2[start:start+len(ix)] = sorted_uid[ix] + e2[start:start+len(ix)] = sorted_emb[ix] + u2.flush(); e2.flush() + del u2,e2 + os.replace(output / 'uids.unique.npy', output / 'uids.npy') + os.replace(output / 'emb.unique.npy', output / 'emb.npy') + count = unique_n + else: + os.replace(output / 'uids.next.npy', output / 'uids.npy') + os.replace(output / 'emb.next.npy', output / 'emb.npy') + del raw_uid, raw_emb, sorted_uid, sorted_emb + for name in ('uids.raw.npy','emb.raw.npy','uids.next.npy','emb.next.npy'): + (output / name).unlink(missing_ok=True) + (output / 'manifest.json').write_text(json.dumps({'rows': count, 'dim': dim, + 'sources': [str(p) for p in paths]}, indent=2) + '\n') + + +class TimbreIndex: + def __init__(self, root: Path, expected_dim: int | None = None): + manifest = json.loads((root / 'manifest.json').read_text()) + self.dim = manifest['dim'] + if expected_dim is not None: + assert self.dim == expected_dim, (self.dim, expected_dim) + self.uids = np.load(root / 'uids.npy', mmap_mode='r') + self.emb = np.load(root / 'emb.npy', mmap_mode='r') + assert self.emb.shape == (len(self.uids), self.dim) + + def lookup(self, uids: Sequence[str]) -> tuple[np.ndarray, np.ndarray]: + query = _uid_array(uids) + pos = np.searchsorted(self.uids, query) + valid = pos < len(self.uids) + valid[valid] &= self.uids[pos[valid]] == query[valid] + result = np.zeros((len(query), self.dim), dtype=np.float16) + result[valid] = self.emb[pos[valid]] + return result, valid + + +class TimbreChunkWriter: + def __init__(self, output: Path, rank: int, flush_rows: int = 8192, + dim: int = DIM, model: str = 'Orange/Speaker-wavLM-tbr', + revision: str = 'b8d2608d56f18e2b6e27ba566cf69132e2c2ad6c'): + self.output, self.rank, self.flush_rows = output, rank, flush_rows + self.dim, self.model, self.revision = dim, model, revision + output.mkdir(parents=True, exist_ok=True) + self.config_path = output / 'cache_config.json' + config = {'dim': dim, 'model': model, 'revision': revision, + 'sample_rate': 16000, 'max_audio_samples': 480000, + 'journal_format': 'uid-S128/emb-f16/crc32-v1'} + if self.config_path.exists(): + assert json.loads(self.config_path.read_text()) == config, self.config_path + else: + temp = output / f'cache_config.rank-{rank:02d}.partial' + temp.write_text(json.dumps(config, indent=2) + '\n') + with temp.open('rb') as file: + os.fsync(file.fileno()) + os.replace(temp, self.config_path) + _sync_directory(output) + self.seq = 1 + max((int(p.stem.rsplit('-',1)[-1]) for p in output.glob(f'rank-{rank:02d}-*.parquet')), default=-1) + self.journal = output / f'rank-{rank:02d}.pending' + self.record_dtype = np.dtype([('uid', UID_DTYPE), ('emb', ' None: + """Replay durable records left by a killed training process.""" + if not self.journal.exists(): + return + raw = self.journal.read_bytes() + width = self.record_dtype.itemsize + complete = len(raw) // width + records = np.frombuffer(raw[:complete * width], dtype=self.record_dtype) + good = 0 + for record in records: + payload = record.tobytes()[:-4] + if zlib.crc32(payload) != int(record['crc']): + break + good += 1 + if good != complete: + raise ValueError(f'Corrupt sidecar journal {self.journal} at record {good}; refusing to discard generated targets') + if complete * width != len(raw): + with self.journal.open('r+b') as file: + file.truncate(complete * width) + file.flush() + os.fsync(file.fileno()) + self.uids = [u.decode('utf-8') for u in records['uid'][:good].tolist()] + self.vectors = [row.copy() for row in records['emb'][:good]] + + def append(self, uids: Sequence[str], emb: np.ndarray) -> None: + assert emb.shape == (len(uids), self.dim) + if not uids: + return + vectors = emb.astype(np.float16) + records = np.empty(len(uids), dtype=self.record_dtype) + records['uid'] = _uid_array(uids) + records['emb'] = vectors + for i in range(len(records)): + records['crc'][i] = zlib.crc32(records[i].tobytes()[:-4]) + payload = records.tobytes() + fd = os.open(self.journal, os.O_WRONLY | os.O_CREAT | os.O_APPEND, 0o640) + try: + remaining = memoryview(payload) + while remaining: + written = os.write(fd, remaining) + assert written > 0 + remaining = remaining[written:] + os.fsync(fd) + finally: + os.close(fd) + self.uids.extend(uids); self.vectors.extend(vectors) + if len(self.uids) >= self.flush_rows: + self.flush() + + def flush(self) -> None: + if not self.uids: + return + emb = np.stack(self.vectors) + column = pa.FixedSizeListArray.from_arrays(pa.array(emb.reshape(-1), type=pa.float16()), self.dim) + table = pa.table({'uid': pa.array(self.uids), 'emb': column}) + table = table.replace_schema_metadata({ + b'model': self.model.encode(), + b'revision': self.revision.encode(), + b'sample_rate': b'16000', + b'max_audio_samples': b'480000', + b'embedding_dim': str(self.dim).encode(), + }) + dst = self.output / f'rank-{self.rank:02d}-{self.seq:06d}.parquet' + tmp = dst.with_suffix('.partial') + pq.write_table(table, tmp, compression='zstd') + with tmp.open('rb') as file: + os.fsync(file.fileno()) + os.replace(tmp, dst) + _sync_directory(self.output) + # The Parquet rename happens first. If the process dies before this + # truncation, journal replay may duplicate rows but never lose them. + with self.journal.open('wb') as file: + file.flush() + os.fsync(file.fileno()) + self.seq += 1 + self.uids.clear(); self.vectors.clear() + + +def _sync_directory(path: Path) -> None: + fd = os.open(path, os.O_RDONLY | os.O_DIRECTORY) + try: + os.fsync(fd) + finally: + os.close(fd) + + +def recover_sidecars(parts: Path, dim: int) -> None: + """Materialize any unflushed rank journals before building an index.""" + config_path = parts / 'cache_config.json' + if not config_path.exists(): + return + config = json.loads(config_path.read_text()) + assert config['dim'] == dim, (config['dim'], dim) + for journal in sorted(parts.glob('rank-*.pending')): + rank = int(journal.stem.split('-')[1]) + writer = TimbreChunkWriter(parts, rank, dim=dim, model=config['model'], + revision=config['revision']) + writer.flush() + + +class TimbreTargetCache: + def __init__(self, precomputed: TimbreIndex | None, generated: TimbreIndex | None, + teacher: OrangeEmbeddingTeacher, writer: TimbreChunkWriter, device: torch.device): + self.precomputed, self.generated = precomputed, generated + self.teacher, self.writer, self.device = teacher, writer, device + self.dim = teacher.dim + assert writer.dim == self.dim + assert precomputed is None or precomputed.dim == self.dim + assert generated is None or generated.dim == self.dim + self.recent: OrderedDict[str, np.ndarray] = OrderedDict() + self.recent_limit = 8192 + self.computed = 0 + for uid, vector in zip(writer.uids, writer.vectors): + self.recent[uid] = vector.copy() + + def get_or_compute(self, uids: Sequence[str], waveforms: Sequence[np.ndarray | torch.Tensor]) -> np.ndarray: + assert len(uids) == len(waveforms) + result = np.zeros((len(uids), self.dim), dtype=np.float16) + have = np.zeros(len(uids), dtype=bool) + for index in (self.precomputed, self.generated): + if index is None: continue + values, mask = index.lookup(uids) + take = mask & ~have + result[take] = values[take]; have |= take + for i, uid in enumerate(uids): + if not have[i] and uid in self.recent: + result[i] = self.recent[uid] + self.recent.move_to_end(uid) + have[i] = True + missing = np.flatnonzero(~have) + if len(missing): + values = self.teacher.encode_waveforms([waveforms[i] for i in missing], self.device) + result[missing] = values + self.writer.append([uids[i] for i in missing], values) + self.computed += len(missing) + for i, value in zip(missing, values): + self.recent[uids[i]] = value.copy() + if len(self.recent) > self.recent_limit: + self.recent.popitem(last=False) + return result + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument('command', choices=['index-emolia', 'index-identity', 'index-generated']) + parser.add_argument('--output', type=Path, required=True) + parser.add_argument('--parts', type=Path) + parser.add_argument('--dim', type=int, choices=[DIM, IDENTITY_DIM], default=DIM) + args = parser.parse_args() + if args.command == 'index-emolia': + build_index([EMOLIA_SOURCE], args.output) + elif args.command == 'index-identity': + build_index(sorted(IDENTITY_SOURCE.glob('*.parquet')), args.output, + allow_same_duplicates=True, dim=IDENTITY_DIM) + else: + assert args.parts is not None + recover_sidecars(args.parts, args.dim) + build_index(sorted(args.parts.glob('rank-*.parquet')), args.output, + allow_same_duplicates=True, dim=args.dim) + + +if __name__ == '__main__': + main() diff --git a/training/train.py b/training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..45f904a569fd366b60067a8f1737423d06cf484c --- /dev/null +++ b/training/train.py @@ -0,0 +1,465 @@ +"""End-to-end Whisper encoder regression for Ladder S10/S9. + +Use under the local fastgen environment. No Whisper decoder is instantiated. +""" +from __future__ import annotations + +import argparse +from contextlib import nullcontext +from functools import partial +import io +import json +import math +import os +from pathlib import Path +from typing import Iterator + +import numpy as np +import soundfile as sf +from scipy.signal import resample_poly +from safetensors import safe_open +import torch +import transformers +from torch import nn +from torch.nn import functional as F +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, Dataset, Sampler +from transformers import WhisperConfig, WhisperFeatureExtractor +from transformers.models.whisper.modeling_whisper import WhisperEncoder +from timbre_targets import (OrangeTimbreTeacher, OrangeIdentityTeacher, TimbreChunkWriter, + TimbreIndex, TimbreTargetCache) + + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/whisper_score_regression') +RUNS = Path('/e/project1/reformo/schuhmann1/whisper_score_regression/runs') +MODEL_CACHE = Path('/e/scratch/reformo/schuhmann1_moss/hf_runtime_whisper_regression/hub') +_EXTRACTOR = None + + +def snapshot(name: str) -> Path: + matches = list((MODEL_CACHE / f'models--openai--whisper-{name}' / 'snapshots').glob('*/config.json')) + assert len(matches) == 1, matches + return matches[0].parent + + +class AudioRows(Dataset): + def __init__(self, arrays: list[dict[str, np.ndarray]], paths: list[str], mean: np.ndarray, std: np.ndarray): + self.columns = {key: np.concatenate([part[key] for part in arrays]) for key in arrays[0]} + self.paths = paths + self.mean, self.std = mean, std + self.open_files: dict[int, io.BufferedReader] = {} + self.durations = self.columns['dur'] + + def __len__(self) -> int: + return len(self.durations) + + def __getitem__(self, i: int): + c = self.columns + path_id = int(c['path'][i]) + if path_id not in self.open_files: + self.open_files[path_id] = open(self.paths[path_id], 'rb', buffering=0) + file = self.open_files[path_id] + file.seek(int(c['offset'][i])) + raw = file.read(int(c['size'][i])) + assert len(raw) == int(c['size'][i]), (i, path_id) + audio, sr = sf.read(io.BytesIO(raw), dtype='float32', always_2d=False) + if audio.ndim == 2: + audio = audio.mean(axis=1) + if sr != 16000: + common = math.gcd(sr, 16000) + audio = resample_poly(audio, 16000 // common, sr // common).astype(np.float32) + audio = np.nan_to_num(audio[:480000], copy=False) + if len(audio) < 1600: + audio = np.pad(audio, (0, 1600 - len(audio))) + y = c['y'][i] + mask = np.isfinite(y) + target = np.where(mask, (y - self.mean) / self.std, 0).astype(np.float32) + return audio, target, mask, i + + +def collate(batch, model_path: str, include_audio: bool = False): + global _EXTRACTOR + if _EXTRACTOR is None: + _EXTRACTOR = WhisperFeatureExtractor.from_pretrained(model_path, local_files_only=True) + audio, targets, masks, indices = zip(*batch) + feat = _EXTRACTOR(list(audio), sampling_rate=16000, padding='longest', + truncation=True, max_length=480000, return_attention_mask=True, + return_tensors='np') + mel = feat['input_features'].astype(np.float32) + attention = feat['attention_mask'].astype(np.int64) + # The second Whisper convolution has stride two. + if mel.shape[-1] % 2: + mel = np.pad(mel, ((0, 0), (0, 0), (0, 1))) + attention = np.pad(attention, ((0, 0), (0, 1))) + result = (torch.from_numpy(mel), torch.from_numpy(attention), + torch.from_numpy(np.stack(targets)), torch.from_numpy(np.stack(masks)), indices) + return result + (audio,) if include_audio else result + + +def init_worker(_worker_id: int): + # OpenMP/ACL was initialized before forking in the parent after CUDA setup. + # Spawned workers and one CPU thread avoid inherited OpenMP barriers. + torch.set_num_threads(1) + + +class BucketBatches(Sampler[list[int]]): + def __init__(self, duration: np.ndarray, batch: int, world: int, rank: int, seed: int): + self.duration, self.batch, self.world, self.rank, self.seed = duration, batch, world, rank, seed + self.epoch = 0 + + def __len__(self) -> int: + return len(self.duration) // (self.batch * self.world) + + def __iter__(self) -> Iterator[list[int]]: + rng = np.random.default_rng(self.seed + self.epoch) + order = rng.permutation(len(self.duration)) + width = self.batch * self.world + batches = [] + for start in range(0, len(order), width * 32): + pool = order[start:start + width * 32] + pool = pool[np.argsort(self.duration[pool], kind='stable')] + for j in range(0, len(pool) - width + 1, width): + batches.append(pool[j:j + width]) + rng.shuffle(batches) + for group in batches: + yield group[self.rank * self.batch:(self.rank + 1) * self.batch].tolist() + + +class EncoderRegressor(nn.Module): + def __init__(self, model_path: Path, outputs: int, timbre: bool = False, + identity: bool = False): + super().__init__() + config = WhisperConfig.from_pretrained(model_path, local_files_only=True) + self.encoder = WhisperEncoder(config) + with safe_open(model_path / 'model.safetensors', framework='pt', device='cpu') as file: + prefix = 'model.encoder.' + state = {key[len(prefix):]: file.get_tensor(key) for key in file.keys() if key.startswith(prefix)} + missing, unexpected = self.encoder.load_state_dict(state, strict=True) + assert not missing and not unexpected, (missing, unexpected) + self.encoder.embed_positions.weight.requires_grad_(True) + dim = config.d_model + self.head = nn.Sequential(nn.LayerNorm(dim * 4), nn.Linear(dim * 4, 512), nn.GELU(), + nn.Dropout(0.1), nn.Linear(512, 256), nn.GELU(), + nn.Dropout(0.1), nn.Linear(256, outputs)) + self.timbre_head = (nn.Sequential(nn.LayerNorm(dim * 4), nn.Linear(dim * 4, 512), + nn.GELU(), nn.Dropout(0.1), nn.Linear(512, 128)) + if timbre else None) + self.identity_head = (nn.Sequential(nn.LayerNorm(dim * 4), nn.Linear(dim * 4, 512), + nn.GELU(), nn.Dropout(0.1), nn.Linear(512, 250)) + if identity else None) + + def forward(self, mel: torch.Tensor, frame_mask: torch.Tensor) -> torch.Tensor: + # Identical pretrained encoder blocks, with positional embeddings sliced to the + # longest item in this duration bucket. This avoids 30 s padding for every clip. + enc = self.encoder + h = F.gelu(enc.conv1(mel)) + h = F.gelu(enc.conv2(h)).transpose(1, 2) + length = h.shape[1] + assert length <= enc.max_source_positions + h = h + enc.embed_positions(torch.arange(length, device=h.device)) + valid = torch.arange(length, device=h.device)[None, :] < ((frame_mask.sum(1) + 1) // 2)[:, None] + # Whisper's stock forward ignores masks; here padded frames cannot act as keys. + attention = torch.zeros((len(h), 1, 1, length), device=h.device, dtype=h.dtype) + attention.masked_fill_(~valid[:, None, None, :], torch.finfo(h.dtype).min) + for layer in enc.layers: + h = layer(h, attention) + h = enc.layer_norm(h).float() + mask = valid[:, :, None] + count = mask.sum(1).clamp_min(1) + mean = (h * mask).sum(1) / count + variance = ((h - mean[:, None, :]).square() * mask).sum(1) / count + minimum = h.masked_fill(~mask, torch.inf).amin(1) + maximum = h.masked_fill(~mask, -torch.inf).amax(1) + pooled = torch.cat([mean, minimum, maximum, variance.clamp_min(1e-8).sqrt()], dim=-1) + scores = self.head(pooled) + if self.timbre_head is None and self.identity_head is None: + return scores + vectors = {} + if self.timbre_head is not None: + vectors['timbre'] = F.normalize(self.timbre_head(pooled).float(), dim=-1) + if self.identity_head is not None: + vectors['identity'] = F.normalize(self.identity_head(pooled).float(), dim=-1) + return scores, vectors + + +def regression_loss(pred: torch.Tensor, target: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + element = F.huber_loss(pred.float(), target.float(), reduction='none', delta=1.0) + return (element * mask).sum() / mask.sum().clamp_min(1) + + +def embedding_loss(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + # Unit vectors: cosine is the primary metric. A small Huber term anchors + # individual coordinates without letting dimensions swamp the score loss. + target = F.normalize(target.float(), dim=-1) + return (1 - (pred.float() * target).sum(-1)).mean() + 0.1 * F.huber_loss( + pred.float(), target, delta=0.1) + + +def embedding_targets(caches: dict[str, TimbreTargetCache], columns: dict, + indices, audio, device): + uids = [columns['uid'][i].decode('utf-8') for i in indices] + return {name: torch.from_numpy(cache.get_or_compute(uids, audio).copy()).to( + device, dtype=torch.float32) for name, cache in caches.items()} + + +@torch.no_grad() +def evaluate(model, loader, mean: torch.Tensor, std: torch.Tensor, device, world: int, + max_batches: int = 0, embedding_caches: dict[str, TimbreTargetCache] | None = None, + columns: dict | None = None): + model.eval() + n = len(mean) + sums = torch.zeros((9, n), dtype=torch.float64, device=device) + embedding_caches = embedding_caches or {} + embedding_sums = {name: torch.zeros(3, dtype=torch.float64, device=device) + for name in embedding_caches} + for step, batch in enumerate(loader): + if max_batches and step >= max_batches: + break + mel, frame_mask, target, mask, indices = batch[:5] + mel, frame_mask = mel.to(device), frame_mask.to(device) + target, mask = target.to(device), mask.to(device) + if embedding_caches: + assert columns is not None + target_vectors = embedding_targets(embedding_caches, columns, indices, + batch[5], device) + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(mel, frame_mask) + if embedding_caches: + pred, pred_vectors = output + for name, target_vector in target_vectors.items(): + pred_vector = pred_vectors[name] + stats = embedding_sums[name] + stats[0] += len(target_vector) + stats[1] += (pred_vector * F.normalize(target_vector, dim=-1)).sum().double() + stats[2] += F.huber_loss(pred_vector, target_vector, delta=0.1, + reduction='sum').double() / target_vector.shape[1] + else: + pred = output + pred, target, mask = pred.double(), target.double(), mask.double() + y = target * std + mean + p = pred * std + mean + sums[0] += mask.sum(0) + sums[1] += (F.huber_loss(pred, target, reduction='none', delta=1.0) * mask).sum(0) + sums[2] += ((p - y).abs() * mask).sum(0) + sums[3] += ((p - y).square() * mask).sum(0) + sums[4] += (y * mask).sum(0) + sums[5] += (y.square() * mask).sum(0) + sums[6] += (p * mask).sum(0) + sums[7] += (p.square() * mask).sum(0) + sums[8] += (p * y * mask).sum(0) + if world > 1: + torch.distributed.all_reduce(sums) + for stats in embedding_sums.values(): + torch.distributed.all_reduce(stats) + count = sums[0].clamp_min(1) + mae = sums[2] / count + huber = sums[1] / count + y_var = (sums[5] - sums[4].square() / count).clamp_min(1e-10) + p_var = (sums[7] - sums[6].square() / count).clamp_min(1e-10) + corr = (sums[8] - sums[4] * sums[6] / count) / (y_var * p_var).sqrt() + r2 = 1 - sums[3] / y_var + result = {'count': sums[0].int().tolist(), 'mae': mae.tolist(), + 'huber_z': huber.tolist(), 'pearson': corr.tolist(), 'r2': r2.tolist()} + for name, a, b in [('emotion', 0, 40), ('voicenet', 40, 97), ('other', 97, 100)]: + result[name] = {'mae': float(mae[a:b].mean()), 'huber_z': float(huber[a:b].mean()), + 'pearson': float(corr[a:b].mean()), 'r2': float(r2[a:b].mean())} + result['huber_z_macro'] = float(huber.mean()) + for name, stats in embedding_sums.items(): + result[f'{name}_count'] = int(stats[0]) + result[f'{name}_cosine'] = float(stats[1] / stats[0].clamp_min(1)) + result[f'{name}_huber'] = float(stats[2] / stats[0].clamp_min(1)) + return result + + +def main(): + p = argparse.ArgumentParser() + p.add_argument('--model', choices=['tiny', 'base'], required=True) + p.add_argument('--stage', choices=['S10', 'S9'], required=True) + p.add_argument('--epochs', type=int, default=3) + p.add_argument('--batch-per-gpu', type=int, default=16) + p.add_argument('--accum', type=int, default=2) + p.add_argument('--workers', type=int, default=2) + p.add_argument('--encoder-lr', type=float, default=2e-5) + p.add_argument('--head-lr', type=float, default=3e-4) + p.add_argument('--timbre', action='store_true', help='Train the 128-D Orange TBR head') + p.add_argument('--timbre-weight', type=float, default=0.2) + p.add_argument('--timbre-index', type=Path, help='Sorted index of precomputed Emolia embeddings') + p.add_argument('--timbre-generated-index', type=Path, help='Index of earlier generated chunks') + p.add_argument('--timbre-cache-dir', type=Path, default=ROOT / 'timbre' / 'chunks') + p.add_argument('--identity', action='store_true', help='Train the 250-D Orange ID head') + p.add_argument('--identity-weight', type=float, default=0.2) + p.add_argument('--identity-index', type=Path, help='Sorted index of precomputed KartoffelPhon embeddings') + p.add_argument('--identity-generated-index', type=Path, help='Index of earlier generated ID chunks') + p.add_argument('--identity-cache-dir', type=Path, default=ROOT / 'identity' / 'chunks') + p.add_argument('--max-train-batches', type=int, default=0) + p.add_argument('--max-val-batches', type=int, default=0) + p.add_argument('--out', type=Path) + args = p.parse_args() + assert torch.cuda.is_available(), 'Run on a Booster GPU node' + world = int(os.environ.get('WORLD_SIZE', os.environ.get('SLURM_NTASKS', '1'))) + rank = int(os.environ.get('RANK', os.environ.get('SLURM_PROCID', '0'))) + if world > 1: + os.environ.setdefault('RANK', str(rank)) + os.environ.setdefault('WORLD_SIZE', str(world)) + torch.distributed.init_process_group('nccl') + torch.cuda.set_device(0) + torch.set_num_threads(2) + torch.manual_seed(20260928) + device = torch.device('cuda:0') + meta = json.loads((ROOT / 'manifest.json').read_text()) + mean = torch.tensor(meta['mean'], dtype=torch.float64, device=device) + std = torch.tensor(meta['std'], dtype=torch.float64, device=device) + def load(name): + with np.load(ROOT / f'{name}.npz') as file: + return {key: file[key] for key in file.files} + train_parts = [load('t0')] + ([load('t1')] if args.stage == 'S9' else []) + train_data = AudioRows(train_parts, meta['paths'], np.asarray(meta['mean']), np.asarray(meta['std'])) + val_data = AudioRows([load('val')], meta['paths'], np.asarray(meta['mean']), np.asarray(meta['std'])) + path = snapshot(args.model) + sampler = BucketBatches(train_data.durations, args.batch_per_gpu, world, rank, meta['seed']) + collate_fn = partial(collate, model_path=str(path), include_audio=args.timbre or args.identity) + loader_options = {'num_workers': args.workers, 'pin_memory': True, + 'persistent_workers': args.workers > 0, 'collate_fn': collate_fn} + if args.workers: + loader_options.update({'multiprocessing_context': 'spawn', 'worker_init_fn': init_worker}) + train_loader = DataLoader(train_data, batch_sampler=sampler, **loader_options) + val_indices = np.arange(rank, len(val_data), world) + val_indices = val_indices[np.argsort(val_data.durations[val_indices])] + val_batches = [val_indices[i:i + args.batch_per_gpu].tolist() + for i in range(0, len(val_indices), args.batch_per_gpu)] + val_loader = DataLoader(val_data, batch_sampler=val_batches, **loader_options) + core = EncoderRegressor(path, len(meta['targets']), timbre=args.timbre, + identity=args.identity).to(device) + model = DDP(core, device_ids=[0], broadcast_buffers=False) if world > 1 else core + parameter_groups = [ + {'params': core.encoder.parameters(), 'lr': args.encoder_lr}, + {'params': core.head.parameters(), 'lr': args.head_lr}] + if args.timbre: + parameter_groups.append({'params': core.timbre_head.parameters(), 'lr': args.head_lr}) + if args.identity: + parameter_groups.append({'params': core.identity_head.parameters(), 'lr': args.head_lr}) + optimizer = torch.optim.AdamW(parameter_groups, weight_decay=0.01) + embedding_caches = {} + if args.timbre: + assert args.timbre_index is not None and (args.timbre_index / 'manifest.json').exists(), \ + 'Build the Emolia timbre index first: python timbre_targets.py index-emolia --output PATH' + teacher = OrangeTimbreTeacher().to(device).eval() + for parameter in teacher.parameters(): + parameter.requires_grad_(False) + precomputed = TimbreIndex(args.timbre_index, expected_dim=128) + generated = TimbreIndex(args.timbre_generated_index, expected_dim=128) if args.timbre_generated_index else None + writer = TimbreChunkWriter(args.timbre_cache_dir, rank, flush_rows=4096) + embedding_caches['timbre'] = TimbreTargetCache(precomputed, generated, teacher, writer, device) + if args.identity: + assert args.identity_index is not None and (args.identity_index / 'manifest.json').exists(), \ + 'Build the Orange ID index first: python timbre_targets.py index-identity --output PATH' + teacher = OrangeIdentityTeacher().to(device).eval() + for parameter in teacher.parameters(): + parameter.requires_grad_(False) + precomputed = TimbreIndex(args.identity_index, expected_dim=250) + generated = TimbreIndex(args.identity_generated_index, expected_dim=250) if args.identity_generated_index else None + writer = TimbreChunkWriter(args.identity_cache_dir, rank, flush_rows=4096, + dim=250, model='Orange/Speaker-wavLM-id', + revision='abbb3c7b8d220ceebc33d9bd3bb5aedb580342c') + embedding_caches['identity'] = TimbreTargetCache(precomputed, generated, teacher, writer, device) + batches = min(len(sampler), args.max_train_batches or len(sampler)) + total_steps = args.epochs * math.ceil(batches / args.accum) + warmup = max(1, int(total_steps * 0.05)) + def schedule(step): + if step < warmup: + return (step + 1) / warmup + progress = (step - warmup) / max(1, total_steps - warmup) + return 0.05 + 0.95 * 0.5 * (1 + math.cos(math.pi * progress)) + scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, schedule) + suffix = '_timbre_identity' if args.timbre and args.identity else \ + '_timbre' if args.timbre else '_identity' if args.identity else '' + out = args.out or RUNS / f'{args.model}_{args.stage.lower()}{suffix}' + out.mkdir(parents=True, exist_ok=True) + if rank == 0: + (out / 'config.json').write_text(json.dumps(vars(args) | { + 'out': str(out), 'world': world, 'train_rows': len(train_data), + 'val_rows': len(val_data), 'model_snapshot': str(path), + 'torch_version': torch.__version__, 'transformers_version': transformers.__version__, + }, indent=2, default=str) + '\n') + print('CONFIG', args.model, args.stage, 'train', len(train_data), 'val', len(val_data), + 'world', world, 'batches', batches, flush=True) + best = math.inf + updates = 0 + for epoch in range(args.epochs): + sampler.epoch = epoch + model.train() + optimizer.zero_grad(set_to_none=True) + running = 0.0 + for step, batch in enumerate(train_loader): + if step >= batches: + break + mel, frame_mask, target, mask, indices = batch[:5] + mel, frame_mask = mel.to(device, non_blocking=True), frame_mask.to(device, non_blocking=True) + target, mask = target.to(device, non_blocking=True), mask.to(device, non_blocking=True) + if embedding_caches: + target_vectors = embedding_targets(embedding_caches, train_data.columns, + indices, batch[5], device) + sync = ((step + 1) % args.accum == 0) or (step + 1 == batches) + context = nullcontext() if sync or world == 1 else model.no_sync() + with context: + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(mel, frame_mask) + if embedding_caches: + pred, pred_vectors = output + loss = regression_loss(pred, target, mask) + for name, target_vector in target_vectors.items(): + weight = args.timbre_weight if name == 'timbre' else args.identity_weight + loss = loss + weight * embedding_loss(pred_vectors[name], target_vector) + else: + loss = regression_loss(output, target, mask) + group_start = (step // args.accum) * args.accum + group_size = min(args.accum, batches - group_start) + (loss / group_size).backward() + running += float(loss.detach()) + if sync: + nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optimizer.step() + scheduler.step() + optimizer.zero_grad(set_to_none=True) + updates += 1 + if rank == 0 and (updates % 50 == 0 or step + 1 == batches): + print('STEP', epoch + 1, updates, step + 1, 'loss', running / (step + 1), flush=True) + metrics = evaluate(core, val_loader, mean, std, device, world, args.max_val_batches, + embedding_caches, val_data.columns) + for name, cache in embedding_caches.items(): + cache.writer.flush() + count = torch.tensor(cache.computed, dtype=torch.long, device=device) + if world > 1: + torch.distributed.all_reduce(count) + metrics[f'{name}_new_saved'] = int(count) + if rank == 0: + selection = metrics['huber_z_macro'] + for name in embedding_caches: + weight = args.timbre_weight if name == 'timbre' else args.identity_weight + selection += weight * (1 - metrics[f'{name}_cosine']) + metrics.update({'epoch': epoch + 1, 'train_loss': running / batches, + 'selection_loss': selection, + 'model': args.model, 'stage': args.stage, 'updates': updates}) + if not embedding_caches: + metrics['train_huber_z'] = running / batches + with (out / 'metrics.jsonl').open('a') as file: + file.write(json.dumps(metrics) + '\n') + print('EPOCH', epoch + 1, 'train', metrics['train_loss'], + 'val', metrics['huber_z_macro'], 'emo', metrics['emotion'], + 'vn', metrics['voicenet'], flush=True) + if selection < best: + best = selection + torch.save({'model': core.state_dict(), 'epoch': epoch + 1, + 'targets': meta['targets'], 'mean': meta['mean'], 'std': meta['std']}, + out / 'best.pt') + if world > 1: + torch.distributed.barrier() + if world > 1: + torch.distributed.destroy_process_group() + if rank == 0 and not args.max_train_batches and not embedding_caches: + from report import main as write_comparison + write_comparison() + + +if __name__ == '__main__': + main() diff --git a/training/train_full_curriculum.py b/training/train_full_curriculum.py new file mode 100644 index 0000000000000000000000000000000000000000..54417404978dd1e47aed720fe95f30a026c14b2a --- /dev/null +++ b/training/train_full_curriculum.py @@ -0,0 +1,406 @@ +#!/usr/bin/env python3 +"""Continuous Whisper Tiny/Base S1→S10 multitask training with 10% frozen P3. + +One optimizer and one cosine schedule span all ten stages. Ladder has the 100 +released scores and any valid burst intervals; the frozen P3 sidecars supply +the additional teacher targets. Missing targets remain masked, not imputed. +""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +import signal +from contextlib import nullcontext +from pathlib import Path + +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, Dataset, Subset + +from full_ladder_dataset import (LadderAudioDataset, MixedCollator, MixedDataset, + StageBatches) +from multitask_p3_smoke import GROUPS, MultiTaskWhisper +from p3_frozen_loader import P3FrozenDataset +from prepare_full_ladder import OUT +from timbre_targets import TimbreIndex +from train import AudioRows, ROOT as OLD_ROOT, RUNS, snapshot + + +P3_ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3/frozen_1120k') +TIMBRE_INDEX = OLD_ROOT / 'timbre' / 'emolia_index' +IDENTITY_INDEX = OLD_ROOT / 'identity' / 'kartoffelphon_index' +_STOP = False + + +def request_stop(_signal, _frame): + global _STOP + _STOP = True + + +def digest(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def normalization() -> tuple[np.ndarray, np.ndarray, list[str]]: + ladder = json.loads((OLD_ROOT / 'manifest.json').read_text()) + p3 = json.loads((P3_ROOT / 'score_normalization.json').read_text()) + if p3['score_names'][:100] != ladder['targets']: + raise RuntimeError('Ladder and P3 target order differ') + mean = np.asarray(p3['mean'], np.float32) + std = np.asarray(p3['std'], np.float32) + mean[:100] = ladder['mean'] + std[:100] = ladder['std'] + return mean, std, p3['score_names'] + + +class LadderVal(Dataset): + def __init__(self, mean: np.ndarray, std: np.ndarray): + manifest = json.loads((OLD_ROOT / 'manifest.json').read_text()) + with np.load(OLD_ROOT / 'val.npz') as source: + rows = {key: source[key] for key in source.files} + self.rows = AudioRows([rows], manifest['paths'], mean[:100], std[:100]) + + def __len__(self): + return len(self.rows) + + def __getitem__(self, i: int) -> dict: + audio, score100, mask100, _ = self.rows[i] + score = np.zeros(192, np.float32) + mask = np.zeros(192, np.bool_) + score[:100], mask[:100] = score100, mask100 + uid = self.rows.columns['uid'][i].decode('utf-8') + n_frames = min(1500, (len(audio) + 319) // 320) + return {'key': uid, 'uid': uid, 'domain': 'ladder', 'audio': audio, + 'scores': score, 'score_mask': mask, + 'frame': np.zeros(n_frames, np.float32), + 'frame_mask': np.zeros(n_frames, np.float32), + 'timbre': np.zeros(128, np.float32), + 'identity': np.zeros(250, np.float32), + 'timbre_valid': False, 'identity_valid': False} + + +class P3Val(Dataset): + def __init__(self, p3: P3FrozenDataset): + self.p3 = p3 + + def __len__(self): + return len(self.p3) + + def __getitem__(self, i: int) -> dict: + result = self.p3[i] + result['uid'] = result['key'] + result['domain'] = 'p3' + result['timbre_valid'] = result['speaker_valid'] + result['identity_valid'] = result['speaker_valid'] + return result + + +def collate_options(model_path: Path, workers: int) -> dict: + result = {'num_workers': workers, 'pin_memory': True, + 'persistent_workers': workers > 0, + 'collate_fn': MixedCollator(model_path)} + if workers: + result['multiprocessing_context'] = 'spawn' + return result + + +def fill_precomputed(batch: dict, indices: dict[str, TimbreIndex]) -> None: + positions = [i for i, (domain, enabled) in enumerate(zip(batch['domain'], + batch['enabled'])) + if domain == 'ladder' and bool(enabled)] + if not positions: + return + uids = [batch['uid'][i] for i in positions] + for name in ('timbre', 'identity'): + values, valid = indices[name].lookup(uids) + for source, use in enumerate(valid): + if use: + dest = positions[source] + batch[name][dest] = torch.from_numpy(values[source].astype(np.float32)) + batch[f'{name}_valid'][dest] = True + + +def gpu_batch(batch: dict, device: torch.device) -> tuple[torch.Tensor, ...]: + fields = ('mel', 'mel_mask', 'scores', 'score_mask', 'frame', 'frame_mask', + 'timbre', 'identity', 'timbre_valid', 'identity_valid') + return tuple(batch[name].to(device, non_blocking=True) for name in fields) + + +def loss_terms(output: dict, batch: tuple[torch.Tensor, ...]) -> dict[str, torch.Tensor]: + _, _, targets, score_mask, frame, frame_mask, timbre, identity, timbre_valid, identity_valid = batch + pred = output['scores'].float() + result = {} + for name, (start, stop, weight) in GROUPS.items(): + valid = score_mask[:, start:stop].float() + huber = F.huber_loss(pred[:, start:stop], targets[:, start:stop].float(), + delta=1., reduction='none') + result[name] = weight * (huber * valid).sum() / valid.sum().clamp_min(1) + valid = frame_mask.float() + logits = output['frame'].float() + bce = F.binary_cross_entropy_with_logits(logits, frame.float(), + pos_weight=torch.tensor(5., device=frame.device), + reduction='none') + result['frame_bce'] = .5 * (bce * valid).sum() / valid.sum().clamp_min(1) + prob = logits.sigmoid() * valid + intersection = (prob * frame).sum() + result['frame_dice'] = .25 * (1 - (2 * intersection + 1) / + (prob.sum() + (frame * valid).sum() + 1)) + for name, target, mask in (('timbre', timbre, timbre_valid), + ('identity', identity, identity_valid)): + if mask.any(): + predicted = output[name][mask].float() + reference = F.normalize(target[mask].float(), dim=-1) + result[name] = .2 * ((1 - (predicted * reference).sum(-1)).mean() + + .1 * F.huber_loss(predicted, reference, delta=.1)) + else: + result[name] = output[name].sum() * 0 + return result + + +def lr_factor(update: int, total: int, warmup: int, floor: float) -> float: + if update < warmup: + return (update + 1) / max(1, warmup) + progress = min(1., (update - warmup) / max(1, total - warmup)) + return floor + (1 - floor) * .5 * (1 + math.cos(math.pi * progress)) + + +@torch.no_grad() +def validate(model, sources: dict[str, DataLoader], device: torch.device, + world: int, indices: dict[str, TimbreIndex]) -> dict: + model.eval() + report = {} + for source, loader in sources.items(): + names = list(GROUPS) + ['frame_bce', 'frame_dice', 'timbre', 'identity'] + sums = torch.zeros((len(names), 2), dtype=torch.float64, device=device) + counts = torch.zeros((4,), dtype=torch.float64, device=device) + for raw in loader: + fill_precomputed(raw, indices) + batch = gpu_batch(raw, device) + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(batch[0], batch[1]) + terms = loss_terms(output, batch) + for i, name in enumerate(names): + has_targets = (batch[3][:, GROUPS[name][0]:GROUPS[name][1]].any() + if name in GROUPS else + batch[5].any() if name.startswith('frame_') else + batch[8].any() if name == 'timbre' else batch[9].any()) + if has_targets: + sums[i, 0] += terms[name].double() * len(raw['uid']) + sums[i, 1] += len(raw['uid']) + if source == 'p3': + use = batch[5].bool() + predicted = (output['frame'].float().sigmoid() > .5) & use + truth = batch[4].bool() & use + counts[0] += (predicted & truth).sum() + counts[1] += (predicted & ~truth).sum() + counts[2] += (~predicted & truth).sum() + counts[3] += use.sum() + if world > 1: + torch.distributed.all_reduce(sums) + torch.distributed.all_reduce(counts) + result = {name: float(sums[i, 0] / sums[i, 1]) if sums[i, 1] else None + for i, name in enumerate(names)} + result['weighted_loss_sum'] = sum(x for x in result.values() if x is not None) + if source == 'p3': + tp, fp, fn = counts[:3].tolist() + result['frame_precision_at_0.5'] = tp / max(1., tp + fp) + result['frame_recall_at_0.5'] = tp / max(1., tp + fn) + result['valid_frame_count'] = int(counts[3]) + report[source] = result + return report + + +def save(out: Path, model: MultiTaskWhisper, optimizer, *, stage_index: int, + batch_index: int, update: int, hashes: dict, best: float, + args: argparse.Namespace): + state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), + 'stage_index': stage_index, 'batch_index': batch_index, + 'update': update, 'hashes': hashes, 'best': best, + 'config': vars(args)} + tmp = out / 'latest.pt.incomplete' + torch.save(state, tmp) + os.replace(tmp, out / 'latest.pt') + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument('--model', choices=('tiny', 'base'), required=True) + parser.add_argument('--out', type=Path, required=True) + parser.add_argument('--resume', type=Path) + parser.add_argument('--batch-per-gpu', type=int, default=8) + parser.add_argument('--accum', type=int, default=2) + parser.add_argument('--workers', type=int, default=2) + parser.add_argument('--encoder-lr', type=float, default=2e-5) + parser.add_argument('--head-lr', type=float, default=3e-4) + parser.add_argument('--min-lr-ratio', type=float, default=.10) + parser.add_argument('--warmup-ratio', type=float, default=.02) + parser.add_argument('--checkpoint-every', type=int, default=5000) + parser.add_argument('--max-updates', type=int, default=0, + help='Smoke mode: stop after this many optimizer updates') + args = parser.parse_args() + if not torch.cuda.is_available(): + raise RuntimeError('training requires Booster GPUs') + world = int(os.environ.get('WORLD_SIZE', os.environ.get('SLURM_NTASKS', '1'))) + rank = int(os.environ.get('RANK', os.environ.get('SLURM_PROCID', '0'))) + if world > 1: + os.environ.setdefault('RANK', str(rank)) + os.environ.setdefault('WORLD_SIZE', str(world)) + torch.distributed.init_process_group('nccl') + torch.cuda.set_device(0) + torch.set_num_threads(2) + torch.manual_seed(640913) + signal.signal(signal.SIGUSR1, request_stop) + device = torch.device('cuda:0') + schedule_path = P3_ROOT / 'curriculum_mix_schedule.json' + index_path = OUT / 'manifest.json' + p3_index_path = P3_ROOT / 'training_index_report.json' + hashes = {'schedule': digest(schedule_path), 'ladder_index': digest(index_path), + 'p3_index': digest(p3_index_path)} + schedule = json.loads(schedule_path.read_text()) + if (schedule['schema_version'] != 'p3-frozen-curriculum-v2' or + schedule['target_p3_fraction_per_stage'] != .10): + raise RuntimeError('wrong P3 curriculum schedule') + mean, std, names = normalization() + ladder = LadderAudioDataset(mean, std) + p3_train = P3FrozenDataset('train', score_mean=mean, score_std=std) + if len(p3_train) != schedule['p3_train_unique']: + raise RuntimeError('P3 train split changed after schedule creation') + mixed = MixedDataset(ladder, p3_train) + path = snapshot(args.model) + options = collate_options(path, args.workers) + val_ladder = LadderVal(mean, std) + val_p3 = P3Val(P3FrozenDataset('validation', score_mean=mean, score_std=std)) + validation = { + 'ladder': DataLoader(Subset(val_ladder, range(rank, len(val_ladder), world)), + batch_size=args.batch_per_gpu, **options), + 'p3': DataLoader(Subset(val_p3, range(rank, len(val_p3), world)), + batch_size=args.batch_per_gpu, **options)} + indices = {'timbre': TimbreIndex(TIMBRE_INDEX, expected_dim=128), + 'identity': TimbreIndex(IDENTITY_INDEX, expected_dim=250)} + core = MultiTaskWhisper(path).to(device) + model = DDP(core, device_ids=[0], broadcast_buffers=False) if world > 1 else core + heads = [parameter for name, parameter in core.named_parameters() + if not name.startswith('encoder.')] + optimizer = torch.optim.AdamW([ + {'params': core.encoder.parameters(), 'lr': args.encoder_lr, + 'base_lr': args.encoder_lr}, + {'params': heads, 'lr': args.head_lr, 'base_lr': args.head_lr}], + weight_decay=.01) + width = world * args.batch_per_gpu + total_updates = sum(math.ceil((math.ceil(stage['ladder'] / width) + + math.ceil(stage['p3'] / width)) / args.accum) + for stage in schedule['stages']) + warmup = round(total_updates * args.warmup_ratio) + args.out.mkdir(parents=True, exist_ok=True) + start_stage = start_batch = update = 0 + best = math.inf + if args.resume: + checkpoint = torch.load(args.resume, map_location='cpu', weights_only=False) + if checkpoint['hashes'] != hashes or checkpoint['config']['model'] != args.model: + raise RuntimeError('resume checkpoint/data/model mismatch') + core.load_state_dict(checkpoint['model']) + optimizer.load_state_dict(checkpoint['optimizer']) + start_stage = int(checkpoint['stage_index']) + start_batch = int(checkpoint['batch_index']) + update = int(checkpoint['update']) + best = float(checkpoint['best']) + if rank == 0: + config = dict(vars(args), world=world, total_updates=total_updates, + warmup_updates=warmup, score_names=names, hashes=hashes, + ladder_speaker_targets='precomputed Emolia TBR and KartoffelPhon ID only; missing masked', + ladder_quality_targets='missing and masked; P3 quality sidecars active') + (args.out / 'config.json').write_text(json.dumps(config, indent=2, default=str) + '\n') + print('CONFIG', args.model, 'world', world, 'updates', total_updates, + 'resume', start_stage, start_batch, update, flush=True) + for stage_index in range(start_stage, len(schedule['stages'])): + stage = schedule['stages'][stage_index] + sampler = StageBatches(ladder, schedule, stage_index, + batch_per_rank=args.batch_per_gpu, + world=world, rank=rank, + start_batch=start_batch if stage_index == start_stage else 0) + loader = DataLoader(mixed, batch_sampler=sampler, **options) + model.train() + optimizer.zero_grad(set_to_none=True) + for local_step, raw in enumerate(loader): + batch_index = sampler.start_batch + local_step + fill_precomputed(raw, indices) + batch = gpu_batch(raw, device) + last = batch_index + 1 == sampler.total_batches + sync = (batch_index + 1) % args.accum == 0 or last + group_start = (batch_index // args.accum) * args.accum + group_size = min(args.accum, sampler.total_batches - group_start) + context = nullcontext() if sync or world == 1 else model.no_sync() + with context: + with torch.autocast('cuda', dtype=torch.bfloat16): + output = model(batch[0], batch[1]) + terms = loss_terms(output, batch) + loss = sum(terms.values()) / group_size + loss.backward() + if sync: + factor = lr_factor(update, total_updates, warmup, args.min_lr_ratio) + for group in optimizer.param_groups: + group['lr'] = group['base_lr'] * factor + nn.utils.clip_grad_norm_(core.parameters(), 1.) + optimizer.step() + optimizer.zero_grad(set_to_none=True) + update += 1 + if rank == 0 and (update % 100 == 0 or update == 1): + print('STEP', stage['stage'], update, 'batch', batch_index + 1, + 'loss', float(loss.detach() * group_size), + 'lr_encoder', optimizer.param_groups[0]['lr'], flush=True) + requested = torch.tensor(int(_STOP or bool(args.max_updates and + update >= args.max_updates)), + dtype=torch.int32, device=device) + if world > 1: + torch.distributed.all_reduce(requested, op=torch.distributed.ReduceOp.MAX) + stop_now = bool(requested.item()) + if (update % args.checkpoint_every == 0 or stop_now) and rank == 0: + save(args.out, core, optimizer, stage_index=stage_index, + batch_index=batch_index + 1, update=update, + hashes=hashes, best=best, args=args) + if stop_now: + if world > 1: + torch.distributed.barrier() + print('STOP', rank, stage['stage'], update, flush=True) + return + if rank == 0: + save(args.out, core, optimizer, stage_index=stage_index + 1, + batch_index=0, update=update, hashes=hashes, best=best, args=args) + if world > 1: + torch.distributed.barrier() + report = validate(core, validation, device, world, indices) + selection = (report['ladder']['weighted_loss_sum'] + + report['p3']['weighted_loss_sum']) + best = min(best, selection) + if rank == 0: + row = {'stage': stage['stage'], 'update': update, + 'p3_share': stage['p3_share'], 'validation': report, + 'selection_loss': selection, 'best': best} + with (args.out / 'metrics.jsonl').open('a') as stream: + stream.write(json.dumps(row) + '\n') + save(args.out, core, optimizer, stage_index=stage_index + 1, + batch_index=0, update=update, hashes=hashes, best=best, args=args) + print('STAGE', json.dumps(row), flush=True) + if world > 1: + torch.distributed.barrier() + start_batch = 0 + ladder.close() + p3_train.close() + val_p3.p3.close() + if rank == 0: + marker = args.out / 'COMPLETE.json.incomplete' + marker.write_text(json.dumps({'status': 'complete', 'model': args.model, + 'updates': update, 'hashes': hashes}, indent=2) + '\n') + marker.replace(args.out / 'COMPLETE.json') + if world > 1: + torch.distributed.destroy_process_group() + + +if __name__ == '__main__': + main() diff --git a/training/train_layered_curriculum.py b/training/train_layered_curriculum.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf3f62fb49406949529dbcfd67e2e3299bc05bb --- /dev/null +++ b/training/train_layered_curriculum.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python3 +"""Base/Small S1→S10 training with layer residuals, event classes and CPS.""" +from __future__ import annotations + +import argparse +import json +import math +import os +import signal +from contextlib import nullcontext +from pathlib import Path + +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, Subset + +from full_ladder_dataset import MixedDataset, StageBatches +from layered_curriculum_data import (LADDER_ROOT, P3_ROOT, LadderAuxDataset, + LadderAuxVal, LayeredCollator, taxonomy) +from layered_multitask_model import LayeredMultiTaskWhisper +from multitask_p3_smoke import GROUPS +from p3_frozen_loader import P3FrozenDataset +from timbre_targets import TimbreIndex +from train import ROOT as OLD_ROOT, snapshot +from train_full_curriculum import (IDENTITY_INDEX, TIMBRE_INDEX, P3Val, + digest, fill_precomputed, lr_factor, normalization) + + +STOP=False + + +def request_stop(_signal,_frame): + global STOP + STOP=True + + +def gpu_batch(batch: dict,device: torch.device) -> dict: + fields=('mel','mel_mask','scores','score_mask','frame','frame_mask', + 'timbre','identity','timbre_valid','identity_valid','cps','cps_raw', + 'cps_valid','event_starts','event_ends','event_classes','event_valid', + 'event_span_valid') + return {name:batch[name].to(device,non_blocking=True) for name in fields} + + +def loss_terms(out: dict,batch: dict,class_weights: torch.Tensor) -> dict[str,torch.Tensor]: + pred=out['scores'].float() + terms={} + for name,(start,stop,weight) in GROUPS.items(): + mask=batch['score_mask'][:,start:stop].float() + huber=F.huber_loss(pred[:,start:stop],batch['scores'][:,start:stop].float(), + delta=1.,reduction='none') + terms[name]=weight*(huber*mask).sum()/mask.sum().clamp_min(1) + valid=batch['frame_mask'].float() + frame=batch['frame'].float() + logits=out['frame'].float() + bce=F.binary_cross_entropy_with_logits(logits,frame, + pos_weight=torch.tensor(5.,device=frame.device), + reduction='none') + terms['frame_bce']=.5*(bce*valid).sum()/valid.sum().clamp_min(1) + prob=logits.sigmoid()*valid + overlap=(prob*frame).sum() + terms['frame_dice']=.25*(1-(2*overlap+1)/ + (prob.sum()+(frame*valid).sum()+1)) + onset_target=torch.zeros_like(out['onset'].float()) + span_mask=batch['event_span_valid'].bool() + if span_mask.any(): + row=torch.arange(len(span_mask),device=span_mask.device)[:,None].expand_as(span_mask) + start=batch['event_starts'].clamp(0,onset_target.shape[1]-1) + onset_target[row[span_mask],start[span_mask]]=1. + selected=out['log_duration'][row[span_mask],start[span_mask]].float() + duration=(batch['event_ends']-batch['event_starts']).clamp_min(1) + expected=duration[span_mask].float().log1p() + terms['duration_huber']=.1*F.huber_loss(selected,expected,delta=.5) + else: + terms['duration_huber']=out['log_duration'].sum()*0 + onset_bce=F.binary_cross_entropy_with_logits( + out['onset'].float(),onset_target, + pos_weight=torch.tensor(100.,device=frame.device),reduction='none') + terms['onset_bce']=.2*(onset_bce*valid).sum()/valid.sum().clamp_min(1) + for name in ('timbre','identity'): + mask=batch[f'{name}_valid'].bool() + if mask.any(): + predicted=out[name][mask].float() + reference=F.normalize(batch[name][mask].float(),dim=-1) + terms[name]=.2*((1-(predicted*reference).sum(-1)).mean()+ + .1*F.huber_loss(predicted,reference,delta=.1)) + else: + terms[name]=out[name].sum()*0 + cps_mask=batch['cps_valid'].bool() + terms['cps_huber']=(.2*F.huber_loss(out['cps'][cps_mask].float(), + batch['cps'][cps_mask].float(),delta=1.) + if cps_mask.any() else out['cps'].sum()*0) + event_mask=batch['event_valid'].bool() + terms['event_class_ce']=(.2*F.cross_entropy( + out['event_class'][event_mask].float(),batch['event_classes'][event_mask], + weight=class_weights,label_smoothing=.02) + if event_mask.any() else out['event_class'].sum()*0) + return terms + + +@torch.no_grad() +def validate(core: LayeredMultiTaskWhisper,sources: dict,device: torch.device, + world: int,indices: dict,class_weights: torch.Tensor,cps_mean: float, + cps_std: float,n_classes: int) -> dict: + core.eval() + result={} + names=list(GROUPS)+['frame_bce','frame_dice','timbre','identity', + 'cps_huber','event_class_ce','onset_bce','duration_huber'] + for domain,loader in sources.items(): + sums=torch.zeros((len(names),2),dtype=torch.float64,device=device) + extra=torch.zeros(7,dtype=torch.float64,device=device) + confusion=torch.zeros((n_classes,n_classes),dtype=torch.int64,device=device) + for raw in loader: + fill_precomputed(raw,indices) + batch=gpu_batch(raw,device) + with torch.autocast('cuda',dtype=torch.bfloat16): + out=core(batch['mel'],batch['mel_mask'], + batch['event_starts'],batch['event_ends']) + terms=loss_terms(out,batch,class_weights) + for i,name in enumerate(names): + if name in GROUPS: + a,b,_=GROUPS[name] + count=batch['score_mask'][:,a:b].any(dim=1).sum().double() + elif name.startswith('frame_') or name=='onset_bce': + count=batch['frame_mask'].any(dim=1).sum().double() + elif name in ('timbre','identity'): + count=batch[f'{name}_valid'].sum().double() + elif name=='cps_huber': + count=batch['cps_valid'].sum().double() + elif name=='duration_huber': + count=batch['event_span_valid'].sum().double() + else: + count=batch['event_valid'].sum().double() + sums[i,0]+=terms[name].double()*count + sums[i,1]+=count + frame_valid=batch['frame_mask'].bool() + frame_truth=batch['frame'].bool()&frame_valid + frame_pred=(out['frame'].float().sigmoid()>=.5)&frame_valid + extra[0]+=(frame_pred&frame_truth).sum() + extra[1]+=(frame_pred&~frame_truth).sum() + extra[2]+=(~frame_pred&frame_truth).sum() + cps_mask=batch['cps_valid'] + if cps_mask.any(): + raw_pred=out['cps'][cps_mask].float()*cps_std+cps_mean + extra[3]+=(raw_pred-batch['cps_raw'][cps_mask]).abs().sum() + extra[4]+=cps_mask.sum() + event_mask=batch['event_valid'] + if event_mask.any(): + truth=batch['event_classes'][event_mask] + prediction=out['event_class'][event_mask].float().argmax(-1) + hist=torch.bincount(truth*n_classes+prediction,minlength=n_classes*n_classes) + confusion+=hist.reshape(n_classes,n_classes) + extra[5]+=(truth==prediction).sum() + extra[6]+=truth.numel() + if world>1: + for item in (sums,extra,confusion): + torch.distributed.all_reduce(item) + metrics={name:(float(sums[i,0]/sums[i,1]) if sums[i,1] else None) + for i,name in enumerate(names)} + metrics['weighted_loss_sum']=sum(x for x in metrics.values() if x is not None) + tp,fp,fn=extra[:3].tolist() + metrics['frame_f1_at_0.5']=2*tp/max(1,2*tp+fp+fn) + metrics['cps_mae_raw']=float(extra[3]/extra[4]) if extra[4] else None + metrics['cps_valid_clips']=int(extra[4]) + metrics['event_class_accuracy']=float(extra[5]/extra[6]) if extra[6] else None + metrics['event_class_valid_events']=int(extra[6]) + if extra[6]: + tp_class=confusion.diag().double() + support=confusion.sum(1).double() + predicted=confusion.sum(0).double() + f1=2*tp_class/(support+predicted).clamp_min(1) + metrics['event_class_macro_f1']=float(f1[support>0].mean()) + else: + metrics['event_class_macro_f1']=None + result[domain]=metrics + core.train() + return result + + +def local_rng(): + return {'torch':torch.get_rng_state(),'cuda':torch.cuda.get_rng_state(), + 'numpy':np.random.get_state()} + + +def gather_rng(world: int): + mine=local_rng() + if world==1: + return [mine] + gathered=[None]*world + torch.distributed.all_gather_object(gathered,mine) + return gathered + + +def save_checkpoint(path: Path,core,optimizer,*,world: int, + stage_index: int,batch_index: int,update: int, + best_by_stage: dict,hashes: dict,args): + states=gather_rng(world) + if torch.distributed.is_initialized() and torch.distributed.get_rank()!=0: + return + state={'model':core.state_dict(),'optimizer':optimizer.state_dict(), + 'scheduler':{'kind':'single_cosine','completed_updates':update, + 'min_lr_ratio':args.min_lr_ratio,'warmup_ratio':args.warmup_ratio}, + 'stage_index':stage_index,'batch_index':batch_index,'update':update, + 'best_by_stage':best_by_stage,'hashes':hashes,'config':vars(args), + 'rng_by_rank':states} + path.parent.mkdir(parents=True,exist_ok=True) + temp=path.with_name(path.name+'.incomplete') + torch.save(state,temp) + os.replace(temp,path) + + +def main(): + ap=argparse.ArgumentParser() + ap.add_argument('--model',choices=('base','small'),required=True) + ap.add_argument('--out',type=Path,required=True) + ap.add_argument('--resume',type=Path) + ap.add_argument('--batch-per-gpu',type=int,default=16) + ap.add_argument('--accum',type=int,default=1) + ap.add_argument('--workers',type=int,default=4) + ap.add_argument('--encoder-lr',type=float,default=None) + ap.add_argument('--head-lr',type=float,default=3e-4) + ap.add_argument('--min-lr-ratio',type=float,default=.10) + ap.add_argument('--warmup-ratio',type=float,default=.02) + ap.add_argument('--checkpoint-every',type=int,default=5000) + ap.add_argument('--validation-every',type=int,default=5000) + ap.add_argument('--minimum-stage-validations',type=int,default=4) + ap.add_argument('--min-class-events',type=int,default=200) + ap.add_argument('--max-updates',type=int,default=0,help='Smoke limit; zero means full run') + args=ap.parse_args() + if not torch.cuda.is_available(): + raise RuntimeError('Booster GPU required') + if args.encoder_lr is None: + args.encoder_lr=2e-5 if args.model=='base' else 1.5e-5 + world=int(os.environ.get('WORLD_SIZE',os.environ.get('SLURM_NTASKS','1'))) + rank=int(os.environ.get('RANK',os.environ.get('SLURM_PROCID','0'))) + if world>1: + os.environ.setdefault('RANK',str(rank)) + os.environ.setdefault('WORLD_SIZE',str(world)) + torch.distributed.init_process_group('nccl') + torch.cuda.set_device(0) + torch.set_num_threads(2) + torch.manual_seed(640913+rank) + np.random.seed(640913+rank) + signal.signal(signal.SIGUSR1,request_stop) + device=torch.device('cuda:0') + + schedule_path=P3_ROOT/'curriculum_mix_schedule.json' + schedule=json.loads(schedule_path.read_text()) + if schedule['schema_version']!='p3-frozen-curriculum-v2': + raise RuntimeError('unexpected S1→S10 schedule') + hashes={name:digest(path) for name,path in { + 'schedule':schedule_path,'ladder_index':LADDER_ROOT/'manifest.json', + 'ladder_aux_schema':LADDER_ROOT/'ladder_aux_schema.json', + 'p3_index':P3_ROOT/'training_index_report.json', + 'p3_event_schema':P3_ROOT/'event_class_schema.json', + 'p3_score_normalization':P3_ROOT/'score_normalization.json', + 'ladder_score_normalization':OLD_ROOT/'manifest.json'}.items()} + classes=taxonomy(args.min_class_events) + mean,std,score_names=normalization() + cps_schema=json.loads((LADDER_ROOT/'ladder_aux_schema.json').read_text()) + cps_mean=float(cps_schema['cps_filtered_train_mean']) + cps_std=max(.1,float(cps_schema['cps_filtered_train_std'])) + cps_filter=cps_schema['cps_training_filter'] + cps_min=float(cps_filter['minimum']) + cps_max=float(cps_filter['maximum']) + ladder=LadderAuxDataset(mean,std) + event_sidecar=P3_ROOT/'event_class_ids.npy' + p3_train=P3FrozenDataset('train',score_mean=mean,score_std=std, + event_class_path=event_sidecar) + if len(p3_train)!=schedule['p3_train_unique']: + raise RuntimeError('P3 split changed') + mixed=MixedDataset(ladder,p3_train) + path=snapshot(args.model) + def collator(): + return LayeredCollator(path,cps_mean=cps_mean,cps_std=cps_std, + ladder_lookup=classes['ladder_raw_to_canonical'], + p3_lookup=classes['p3_raw_to_canonical'], + cps_min=cps_min,cps_max=cps_max) + def loader_options(): + options={'num_workers':args.workers,'pin_memory':True, + 'persistent_workers':args.workers>0,'collate_fn':collator()} + if args.workers: + options['multiprocessing_context']='spawn' + return options + val_ladder=LadderAuxVal(mean,std) + val_p3=P3Val(P3FrozenDataset('validation',score_mean=mean,score_std=std, + event_class_path=event_sidecar)) + validation={ + 'ladder':DataLoader(Subset(val_ladder,range(rank,len(val_ladder),world)), + batch_size=args.batch_per_gpu,**loader_options()), + 'p3':DataLoader(Subset(val_p3,range(rank,len(val_p3),world)), + batch_size=args.batch_per_gpu,**loader_options())} + indices={'timbre':TimbreIndex(TIMBRE_INDEX,expected_dim=128), + 'identity':TimbreIndex(IDENTITY_INDEX,expected_dim=250)} + core=LayeredMultiTaskWhisper(path,len(classes['names'])).to(device) + model=DDP(core,device_ids=[0],broadcast_buffers=False) if world>1 else core + heads=[parameter for name,parameter in core.named_parameters() + if not name.startswith('encoder.')] + optimizer=torch.optim.AdamW([ + {'params':core.encoder.parameters(),'lr':args.encoder_lr,'base_lr':args.encoder_lr}, + {'params':heads,'lr':args.head_lr,'base_lr':args.head_lr}],weight_decay=.01) + class_weights=torch.tensor(classes['class_weights'],device=device,dtype=torch.float32) + width=world*args.batch_per_gpu + total_updates=sum(math.ceil((math.ceil(stage['ladder']/width)+ + math.ceil(stage['p3']/width))/args.accum) + for stage in schedule['stages']) + warmup=round(total_updates*args.warmup_ratio) + args.out.mkdir(parents=True,exist_ok=True) + start_stage=start_batch=update=0 + best_by_stage={} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=False) + if state['hashes']!=hashes or state['config']['model']!=args.model: + raise RuntimeError('resume checkpoint model or source hashes differ') + settings=('batch_per_gpu','accum','encoder_lr','head_lr','min_lr_ratio', + 'warmup_ratio','min_class_events','validation_every', + 'minimum_stage_validations') + if any(state['config'][name]!=getattr(args,name) for name in settings): + raise RuntimeError('resume checkpoint hyperparameters differ') + if len(state['rng_by_rank'])!=world: + raise RuntimeError('resume checkpoint distributed world size differs') + core.load_state_dict(state['model']) + optimizer.load_state_dict(state['optimizer']) + start_stage=int(state['stage_index']) + start_batch=int(state['batch_index']) + update=int(state['update']) + best_by_stage=state['best_by_stage'] + rng=state.get('rng_by_rank',[None]*world)[rank] + if rng: + torch.set_rng_state(rng['torch']) + torch.cuda.set_rng_state(rng['cuda']) + np.random.set_state(rng['numpy']) + if rank==0: + (args.out/'classes.json').write_text(json.dumps(classes,indent=2)+'\n') + (args.out/'config.json').write_text(json.dumps({**vars(args),'world':world, + 'total_updates':total_updates,'warmup_updates':warmup, + 'score_names':score_names,'score_mean':mean.tolist(), + 'score_std':std.tolist(),'cps_mean':cps_mean,'cps_std':cps_std, + 'cps_min':cps_min,'cps_max':cps_max, + 'hashes':hashes,'selection':'sum of fixed Ladder and P3 weighted validation losses', + 'stage_best':'lowest selection at validation checks within each stage'}, + indent=2,default=str)+'\n') + print('CONFIG',args.model,'world',world,'updates',total_updates, + 'resume',start_stage,start_batch,update,'classes',len(classes['names']),flush=True) + + def evaluate(stage_index: int,next_stage: int,batch_index: int): + nonlocal best_by_stage + report=validate(core,validation,device,world,indices,class_weights, + cps_mean,cps_std,len(classes['names'])) + selection=report['ladder']['weighted_loss_sum']+report['p3']['weighted_loss_sum'] + if not math.isfinite(selection): + raise RuntimeError(f'nonfinite validation selection at {stage_index}:{update}') + stage_name=schedule['stages'][stage_index]['stage'] + old=float(best_by_stage.get(stage_name,math.inf)) + improved=selection1: + torch.distributed.barrier() + + for stage_index in range(start_stage,len(schedule['stages'])): + stage=schedule['stages'][stage_index] + sampler=StageBatches(ladder,schedule,stage_index, + batch_per_rank=args.batch_per_gpu,world=world,rank=rank, + start_batch=start_batch if stage_index==start_stage else 0) + stage_updates=math.ceil(sampler.total_batches/args.accum) + stage_val_interval=max(1,min(args.validation_every, + math.ceil(stage_updates/(args.minimum_stage_validations+1)))) + loader=DataLoader(mixed,batch_sampler=sampler,**loader_options()) + model.train() + optimizer.zero_grad(set_to_none=True) + for local_step,raw in enumerate(loader): + batch_index=sampler.start_batch+local_step + fill_precomputed(raw,indices) + batch=gpu_batch(raw,device) + final_batch=batch_index+1==sampler.total_batches + sync=(batch_index+1)%args.accum==0 or final_batch + group_start=(batch_index//args.accum)*args.accum + group_size=min(args.accum,sampler.total_batches-group_start) + context=nullcontext() if sync or world==1 else model.no_sync() + with context: + with torch.autocast('cuda',dtype=torch.bfloat16): + output=model(batch['mel'],batch['mel_mask'], + batch['event_starts'],batch['event_ends']) + terms=loss_terms(output,batch,class_weights) + loss=sum(terms.values())/group_size + loss.backward() + if not sync: + continue + factor=lr_factor(update,total_updates,warmup,args.min_lr_ratio) + for group in optimizer.param_groups: + group['lr']=group['base_lr']*factor + nn.utils.clip_grad_norm_(core.parameters(),1.) + optimizer.step() + optimizer.zero_grad(set_to_none=True) + update+=1 + if rank==0 and (update%100==0 or update==1): + print('STEP',stage['stage'],update,'batch',batch_index+1, + 'loss',float(loss.detach()*group_size), + 'lr_encoder',optimizer.param_groups[0]['lr'],flush=True) + requested=torch.tensor(int(STOP or bool(args.max_updates and + update>=args.max_updates)), + dtype=torch.int32,device=device) + if world>1: + torch.distributed.all_reduce(requested,op=torch.distributed.ReduceOp.MAX) + stop_now=bool(requested.item()) + if stop_now: + save_checkpoint(args.out/'latest.pt',core,optimizer,world=world, + stage_index=stage_index,batch_index=batch_index+1, + update=update,best_by_stage=best_by_stage,hashes=hashes,args=args) + if world>1: + torch.distributed.barrier() + print('STOP',rank,stage['stage'],update,flush=True) + if world>1: + torch.distributed.destroy_process_group() + return + stage_update=math.ceil((batch_index+1)/args.accum) + if not final_batch and stage_update%stage_val_interval==0: + evaluate(stage_index,stage_index,batch_index+1) + elif not final_batch and update%args.checkpoint_every==0: + save_checkpoint(args.out/'latest.pt',core,optimizer,world=world, + stage_index=stage_index,batch_index=batch_index+1, + update=update,best_by_stage=best_by_stage,hashes=hashes,args=args) + evaluate(stage_index,stage_index+1,sampler.total_batches) + start_batch=0 + ladder.close() + p3_train.close() + val_p3.p3.close() + if rank==0: + temp=args.out/'COMPLETE.json.incomplete' + temp.write_text(json.dumps({'status':'complete','model':args.model, + 'updates':update,'hashes':hashes, + 'best_by_stage':best_by_stage},indent=2)+'\n') + os.replace(temp,args.out/'COMPLETE.json') + if world>1: + torch.distributed.destroy_process_group() + + +if __name__=='__main__':main() diff --git a/training_normalization.json b/training_normalization.json new file mode 100644 index 0000000000000000000000000000000000000000..48c2374aedc17ad1ce670ea8fe31a56de8b6c886 --- /dev/null +++ b/training_normalization.json @@ -0,0 +1,589 @@ +{ + "schema": "layered-multitask-training-normalization-v1", + "score_names": [ + "emo_Affection", + "emo_Amusement", + "emo_Anger", + "emo_Astonishment_Surprise", + "emo_Awe", + "emo_Bitterness", + "emo_Concentration", + "emo_Confusion", + "emo_Contemplation", + "emo_Contempt", + "emo_Contentment", + "emo_Disappointment", + "emo_Disgust", + "emo_Distress", + "emo_Doubt", + "emo_Elation", + "emo_Embarrassment", + "emo_Emotional_Numbness", + "emo_Fatigue_Exhaustion", + "emo_Fear", + "emo_Helplessness", + "emo_Hope_Enthusiasm_Optimism", + "emo_Impatience_and_Irritability", + "emo_Infatuation", + "emo_Interest", + "emo_Intoxication_Altered_States_of_Consciousness", + "emo_Jealousy_and_Envy", + "emo_Longing", + "emo_Malevolence_Malice", + "emo_Pain", + "emo_Pleasure_Ecstasy", + "emo_Pride", + "emo_Relief", + "emo_Sadness", + "emo_Sexual_Lust", + "emo_Shame", + "emo_Sourness", + "emo_Teasing", + "emo_Thankfulness_Gratitude", + "emo_Triumph", + "vn_AGEV_reg", + "vn_AROU_reg", + "vn_ARSH_reg", + "vn_ATCK_reg", + "vn_BKGN_reg", + "vn_BRGT_reg", + "vn_CHNK_reg", + "vn_CLRT_reg", + "vn_COGL_reg", + "vn_DARC_reg", + "vn_DFLU_reg", + "vn_EMPH_reg", + "vn_ESTH_reg", + "vn_EXPL_reg", + "vn_FOCS_reg", + "vn_FULL_reg", + "vn_GEND_reg", + "vn_HARM_reg", + "vn_METL_reg", + "vn_RANG_reg", + "vn_RCQL_reg", + "vn_REGS_reg", + "vn_RESP_reg", + "vn_ROUG_reg", + "vn_R_CHST_reg", + "vn_R_HEAD_reg", + "vn_R_MASK_reg", + "vn_R_MIXD_reg", + "vn_R_NASL_reg", + "vn_R_ORAL_reg", + "vn_R_THRT_reg", + "vn_SMTH_reg", + "vn_STNC_reg", + "vn_STRU_reg", + "vn_S_ASMR_reg", + "vn_S_AUTH_reg", + "vn_S_CART_reg", + "vn_S_CASU_reg", + "vn_S_CONV_reg", + "vn_S_DRAM_reg", + "vn_S_FORM_reg", + "vn_S_MONO_reg", + "vn_S_NARR_reg", + "vn_S_NEWS_reg", + "vn_S_PLAY_reg", + "vn_S_RANT_reg", + "vn_S_STRY_reg", + "vn_S_TECH_reg", + "vn_S_WHIS_reg", + "vn_TEMP_reg", + "vn_TENS_reg", + "vn_VALN_reg", + "vn_VALS_reg", + "vn_VFLX_reg", + "vn_VOLT_reg", + "vn_VULN_reg", + "vn_WARM_reg", + "genuineness_0_6", + "blend_0_10", + "R_quality", + "eiv_extra:Age", + "eiv_extra:Arousal", + "eiv_extra:Authenticity", + "eiv_extra:Background_Noise", + "eiv_extra:Confident_vs._Hesitant", + "eiv_extra:Gender", + "eiv_extra:High-Pitched_vs._Low-Pitched", + "eiv_extra:Monotone_vs._Expressive", + "eiv_extra:Recording_Quality", + "eiv_extra:Serious_vs._Humorous", + "eiv_extra:Soft_vs._Harsh", + "eiv_extra:Submissive_vs._Dominant", + "eiv_extra:Valence", + "eiv_extra:Vulnerable_vs._Emotionally_Detached", + "eiv_extra:Warm_vs._Cold", + "eiv_extra:score_background_quality", + "eiv_extra:score_content_enjoyment", + "eiv_extra:score_overall_quality", + "eiv_extra:score_speech_quality", + "audiobox:CE", + "audiobox:CU", + "audiobox:PC", + "audiobox:PQ", + "dnsmos:SIG_raw", + "dnsmos:BAK_raw", + "dnsmos:OVRL_raw", + "dnsmos:SIG", + "dnsmos:BAK", + "dnsmos:OVRL", + "dnsmos:P808_MOS", + "burst_count_log1p", + "voiceclap_attribute:Affection", + "voiceclap_attribute:Age", + "voiceclap_attribute:Amusement", + "voiceclap_attribute:Anger", + "voiceclap_attribute:Arousal", + "voiceclap_attribute:Astonishment_Surprise", + "voiceclap_attribute:Authenticity", + "voiceclap_attribute:Awe", + "voiceclap_attribute:Background_Noise", + "voiceclap_attribute:Bitterness", + "voiceclap_attribute:Concentration", + "voiceclap_attribute:Confident_vs._Hesitant", + "voiceclap_attribute:Confusion", + "voiceclap_attribute:Contemplation", + "voiceclap_attribute:Contempt", + "voiceclap_attribute:Contentment", + "voiceclap_attribute:Disappointment", + "voiceclap_attribute:Disgust", + "voiceclap_attribute:Distress", + "voiceclap_attribute:Doubt", + "voiceclap_attribute:Elation", + "voiceclap_attribute:Embarrassment", + "voiceclap_attribute:Emotional_Numbness", + "voiceclap_attribute:Fatigue_Exhaustion", + "voiceclap_attribute:Fear", + "voiceclap_attribute:Gender", + "voiceclap_attribute:Helplessness", + "voiceclap_attribute:High-Pitched_vs._Low-Pitched", + "voiceclap_attribute:Hope_Enthusiasm_Optimism", + "voiceclap_attribute:Impatience_and_Irritability", + "voiceclap_attribute:Infatuation", + "voiceclap_attribute:Interest", + "voiceclap_attribute:Intoxication_Altered_States_of_Consciousness", + "voiceclap_attribute:Jealousy_&_Envy", + "voiceclap_attribute:Longing", + "voiceclap_attribute:Malevolence_Malice", + "voiceclap_attribute:Monotone_vs._Expressive", + "voiceclap_attribute:Pain", + "voiceclap_attribute:Pleasure_Ecstasy", + "voiceclap_attribute:Pride", + "voiceclap_attribute:Recording_Quality", + "voiceclap_attribute:Relief", + "voiceclap_attribute:Sadness", + "voiceclap_attribute:Serious_vs._Humorous", + "voiceclap_attribute:Sexual_Lust", + "voiceclap_attribute:Shame", + "voiceclap_attribute:Soft_vs._Harsh", + "voiceclap_attribute:Sourness", + "voiceclap_attribute:Submissive_vs._Dominant", + "voiceclap_attribute:Teasing", + "voiceclap_attribute:Thankfulness_Gratitude", + "voiceclap_attribute:Triumph", + "voiceclap_attribute:Valence", + "voiceclap_attribute:Vulnerable_vs._Emotionally_Detached", + "voiceclap_attribute:Warm_vs._Cold", + "voiceclap_attribute:duration", + "voiceclap_attribute:score_background_quality", + "voiceclap_attribute:score_content_enjoyment", + "voiceclap_attribute:score_overall_quality", + "voiceclap_attribute:score_speech_quality", + "voiceclap_attribute:talking_speed" + ], + "score_mean": [ + 0.27749165892601013, + 0.4777434766292572, + 0.36061015725135803, + 0.2777629792690277, + 0.11817667633295059, + 0.21946945786476135, + 0.6705899834632874, + 0.3177536427974701, + 0.5056841969490051, + 0.20877057313919067, + 0.35425904393196106, + 0.4897621273994446, + 0.22247810661792755, + 0.4589405953884125, + 0.3137844204902649, + 0.4519732594490051, + 0.2580322027206421, + 0.46182429790496826, + 0.28273433446884155, + 0.3217501640319824, + 0.4309713840484619, + 0.5629669427871704, + 0.5035319924354553, + 0.19843806326389313, + 1.4420181512832642, + 0.1724642664194107, + 0.21464531123638153, + 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b/verification/base.json new file mode 100644 index 0000000000000000000000000000000000000000..509338e4bb2df7c7184d1f481784882a4dc870ef --- /dev/null +++ b/verification/base.json @@ -0,0 +1,23 @@ +{ + "model": "base", + "checkpoint_epoch": 2, + "updates": 914, + "source_checkpoint_sha256": "02ea9f6203a1d12590c36d34ba2f4e1bfa9034cae7018fcf1489bf1a6934ba0b", + "safe_weights_sha256": "c4d9e05295020cd0fab4e44ee10aae3bdf934b0a7700fea05bb271ffa2e410b4", + "safe_weights_bytes": 105795828, + "all_tensors_bitwise_equal": true, + "all_model_outputs_bitwise_equal": true, + "heldout_audio_sha256": "0003abe6b5462dfec7bd1ece5ece2c02c289c6bdefd8318db13580bbd050cd48", + "heldout_duration_s": 4.896, + "targets": 192, + "speaker_dimensions": [ + 128, + 250 + ], + "finite_outputs": true, + "timestamps_have_start_and_end": true, + "device": "cuda:0", + "one_thread_cpu_inference_passed": true, + "torch": "2.9.1+cu128", + "slurm_job": "2204359" +} diff --git a/verification/small.json b/verification/small.json new file mode 100644 index 0000000000000000000000000000000000000000..a4e23c3fe1d477447fcfcfcffe44171ef8335a1a --- /dev/null +++ b/verification/small.json @@ -0,0 +1,23 @@ +{ + "model": "small", + "checkpoint_epoch": 2, + "updates": 914, + "source_checkpoint_sha256": "63f2dd8314cde82e998fa7ef802020ff984948d2cf4b7da32711e89b2bc637e6", + "safe_weights_sha256": "00f30de0f7d656d2a1e86bdc629673ff8b576532e1263b02bcebdfc2763e54f1", + "safe_weights_bytes": 388447068, + "all_tensors_bitwise_equal": true, + "all_model_outputs_bitwise_equal": true, + "heldout_audio_sha256": "0003abe6b5462dfec7bd1ece5ece2c02c289c6bdefd8318db13580bbd050cd48", + "heldout_duration_s": 4.896, + "targets": 192, + "speaker_dimensions": [ + 128, + 250 + ], + "finite_outputs": true, + "timestamps_have_start_and_end": true, + "device": "cuda:1", + "one_thread_cpu_inference_passed": true, + "torch": "2.9.1+cu128", + "slurm_job": "2204359" +} diff --git a/vocal_burst_pool/p3/annotate_audiobox.py b/vocal_burst_pool/p3/annotate_audiobox.py new file mode 100644 index 0000000000000000000000000000000000000000..b08e2133f12b1276f79e03d868f11925be7d9bf9 --- /dev/null +++ b/vocal_burst_pool/p3/annotate_audiobox.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +"""Write key-aligned Meta Audiobox Aesthetics CE/CU/PC/PQ pilot sidecars.""" +import io +import json +import os +import tarfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch + +from audiobox_aesthetics.infer import AXES_NAME, make_inference_batch +from audiobox_aesthetics.model.aes import AesMultiOutput, Normalize + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3') +MODEL = ROOT / 'teachers/audiobox-aesthetics-model' +IN = Path(os.environ.get('P3_INPUT', ROOT / 'pilot/p3-pilot-00000.tar')) +OUT = Path(os.environ.get('P3_OUTPUT', ROOT / 'pilot/p3-pilot-audiobox-sidecars-00000.tar')) +MODEL_REVISION = '9b1dd8e5df9af7216e836a98974fe3b82c56ded6' + + +def records(): + with tarfile.open(IN) as tf: + members = {m.name: m for m in tf if m.isfile()} + for key in sorted(n[:-5] for n in members if n.endswith('.json')): + wav, sr = sf.read(io.BytesIO(tf.extractfile(members[key + '.opus']).read()), + dtype='float32') + assert sr == 16000 and wav.ndim == 1 + yield key, wav + + +def add(tf, name, payload): + h = tarfile.TarInfo(name) + h.size, h.mtime = len(payload), 0 + tf.addfile(h, io.BytesIO(payload)) + + +@torch.inference_mode() +def infer(model, batch): + wavs = [torch.from_numpy(w).reshape(1, -1) for _, w in batch] + windows, masks, weights, owners = make_inference_batch( + wavs, hop_size=10, window_size=10, sample_rate=16000) + wavs = torch.stack(windows).to('cuda') + masks = torch.stack(masks).to('cuda') + weights = torch.tensor(weights, device='cuda') + owners = torch.tensor(owners, device='cuda') + with torch.autocast('cuda', torch.bfloat16): + preds = model({'wav': wavs, 'mask': masks}) + rows = [{} for _ in batch] + for axis in AXES_NAME: + transform = Normalize(mean=model.target_transform[axis]['mean'], + std=model.target_transform[axis]['std']) + value = transform.inverse(preds[axis].float()) + for i in range(len(batch)): + mask = owners == i + score = (value[mask] * weights[mask]).sum() / weights[mask].sum() + rows[i][axis] = round(float(score), 5) + return rows + + +def main(): + assert torch.cuda.is_available() + model = AesMultiOutput.from_pretrained(str(MODEL)).to('cuda').eval() + examples = list(records()) + OUT.parent.mkdir(parents=True, exist_ok=True) + tmp = OUT.with_suffix('.tar.incomplete') + with tarfile.open(tmp, 'w') as tf: + for i in range(0, len(examples), 4): + batch = examples[i:i + 4] + scores = infer(model, batch) + for (key, _), row in zip(batch, scores): + assert set(row) == set(AXES_NAME) + assert all(np.isfinite(v) for v in row.values()) + payload = {'schema_version': 'p3-audiobox-sidecar-v1', + 'sample_id': key, 'teacher': 'facebook/audiobox-aesthetics', + 'teacher_revision': MODEL_REVISION, + 'scores': row} + add(tf, key + '.audiobox.json', + json.dumps(payload, separators=(',', ':')).encode()) + print('annotated', min(i + 4, len(examples)), flush=True) + os.replace(tmp, OUT) + print({'samples': len(examples), 'archive': str(OUT), + 'bytes': OUT.stat().st_size}, flush=True) + + +if __name__ == '__main__': + main() diff --git a/vocal_burst_pool/p3/annotate_dnsmos.py b/vocal_burst_pool/p3/annotate_dnsmos.py new file mode 100644 index 0000000000000000000000000000000000000000..95cadb190cd2df412bca47bde420100e2c7a5e80 --- /dev/null +++ b/vocal_burst_pool/p3/annotate_dnsmos.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +"""Write all raw/calibrated DNSMOS + P808 scores as key-aligned pilot sidecars.""" +import io +import json +import math +import multiprocessing as mp +import os +import sys +import tarfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import librosa +import onnxruntime as ort + +CODE = Path('/e/scratch/reformo/schuhmann1_moss/code/dns-challenge/DNSMOS') +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3') +IN = Path(os.environ.get('P3_INPUT', ROOT / 'pilot/p3-pilot-00000.tar')) +OUT = Path(os.environ.get('P3_OUTPUT', ROOT / 'pilot/p3-pilot-dnsmos-sidecars-00000.tar')) + +class ComputeScore: + """In-memory subset of the bundled DNSMOS reference inference code.""" + def __init__(self, primary, p808, threads=None): + opts = None + if threads is not None: + opts = ort.SessionOptions() + opts.intra_op_num_threads = threads + opts.inter_op_num_threads = 1 + self.onnx_sess = ort.InferenceSession( + primary, sess_options=opts, providers=['CPUExecutionProvider']) + self.p808_onnx_sess = ort.InferenceSession( + p808, sess_options=opts, providers=['CPUExecutionProvider']) + + def audio_melspec(self, audio): + mel = librosa.feature.melspectrogram(y=audio, sr=16000, n_fft=321, + hop_length=160, n_mels=120) + return ((librosa.power_to_db(mel, ref=np.max) + 40) / 40).T + + def get_polyfit_val(self, sig, bak, ovr, personalized): + if personalized: + raise ValueError('personalized DNSMOS is not requested') + return (np.poly1d([-0.08397278, 1.22083953, 0.0052439])(sig), + np.poly1d([-0.13166888, 1.60915514, -0.39604546])(bak), + np.poly1d([-0.06766283, 1.11546468, 0.04602535])(ovr)) + + +def records(): + with tarfile.open(IN) as tf: + members = {m.name: m for m in tf if m.isfile()} + for key in sorted(n[:-5] for n in members if n.endswith('.json')): + meta = json.load(tf.extractfile(members[key + '.json'])) + wav, sr = sf.read(io.BytesIO(tf.extractfile(members[key + '.opus']).read()), + dtype='float32') + assert sr == 16000 and wav.ndim == 1 + yield key, meta, wav + + +def raw_records(): + with tarfile.open(IN) as tf: + members = {m.name: m for m in tf if m.isfile()} + for key in sorted(n[:-5] for n in members if n.endswith('.json')): + meta = json.load(tf.extractfile(members[key + '.json'])) + has_speech = any(x['role'] == 'speech' for x in meta['components']) + audio = tf.extractfile(members[key + '.opus']).read() + yield key, has_speech, audio + + +def score_wav(scorer, wav): + # Same 9.01-s repeated/1-s-hop procedure as the bundled DNSMOS script. + n = int(9.01 * 16000) + if len(wav) < n: + wav = np.tile(wav, math.ceil(n / len(wav))) + hops = max(1, int(np.floor(len(wav) / 16000) - 9.01) + 1) + values = [] + for idx in range(hops): + seg = wav[idx * 16000:idx * 16000 + n] + if len(seg) < n: + break + feat = seg.astype('float32')[None] + mel = scorer.audio_melspec(seg[:-160]).astype('float32')[None] + p808 = float(scorer.p808_onnx_sess.run(None, {'input_1': mel})[0][0][0]) + raw_sig, raw_bak, raw_ovr = map(float, + scorer.onnx_sess.run(None, {'input_1': feat})[0][0]) + sig, bak, ovr = map(float, + scorer.get_polyfit_val(raw_sig, raw_bak, raw_ovr, False)) + values.append([raw_sig, raw_bak, raw_ovr, sig, bak, ovr, p808]) + if not values: + raise RuntimeError('DNSMOS produced no windows') + mean = np.mean(values, axis=0) + names = ['SIG_raw', 'BAK_raw', 'OVRL_raw', 'SIG', 'BAK', 'OVRL', 'P808_MOS'] + return {k: round(float(v), 5) for k, v in zip(names, mean)}, len(values) + + +def add(tf, name, payload): + h = tarfile.TarInfo(name) + h.size, h.mtime = len(payload), 0 + tf.addfile(h, io.BytesIO(payload)) + + +_WORKER_SCORER = None + + +def _init_worker(): + global _WORKER_SCORER + _WORKER_SCORER = ComputeScore(str(CODE / 'DNSMOS/sig_bak_ovr.onnx'), + str(CODE / 'DNSMOS/model_v8.onnx'), threads=1) + + +def _score_record(task): + key, has_speech, audio = task + wav, sr = sf.read(io.BytesIO(audio), dtype='float32') + if sr != 16000 or wav.ndim != 1: + raise RuntimeError(f'{key}: invalid decoded audio') + scores, hops = score_wav(_WORKER_SCORER, wav) + payload = {'schema_version': 'p3-dnsmos-sidecar-v2', 'sample_id': key, + 'teacher': 'microsoft/DNSMOS local sig_bak_ovr + model_v8 P808', + 'valid_for_speech': has_speech, + 'window_count': hops, 'scores': scores} + return key, has_speech, payload + + +def main(): + workers = int(os.environ.get('P3_DNSMOS_WORKERS', '1')) + threads = int(os.environ.get('P3_DNSMOS_THREADS', '0')) + if workers < 1: + raise ValueError('P3_DNSMOS_WORKERS must be positive') + if threads < 0: + raise ValueError('P3_DNSMOS_THREADS must be nonnegative') + scorer = (ComputeScore(str(CODE / 'DNSMOS/sig_bak_ovr.onnx'), + str(CODE / 'DNSMOS/model_v8.onnx'), + threads=threads or None) if workers == 1 else None) + OUT.parent.mkdir(parents=True, exist_ok=True) + tmp = OUT.with_suffix('.tar.incomplete') + count = valid = 0 + with tarfile.open(tmp, 'w') as tf: + if workers == 1: + def scored(): + for key, meta, wav in records(): + has_speech = any(x['role'] == 'speech' for x in meta['components']) + scores, hops = score_wav(scorer, wav) + yield key, has_speech, { + 'schema_version': 'p3-dnsmos-sidecar-v2', 'sample_id': key, + 'teacher': 'microsoft/DNSMOS local sig_bak_ovr + model_v8 P808', + 'valid_for_speech': has_speech, + 'window_count': hops, 'scores': scores} + iterator = scored() + pool = None + else: + pool = mp.get_context('spawn').Pool(workers, initializer=_init_worker) + iterator = pool.imap(_score_record, raw_records(), chunksize=4) + try: + for key, has_speech, payload in iterator: + add(tf, key + '.dnsmos.json', + json.dumps(payload, separators=(',', ':')).encode()) + count += 1 + valid += has_speech + if count % 100 == 0: + print('annotated', count, flush=True) + finally: + if pool is not None: + pool.terminate() + pool.join() + os.replace(tmp, OUT) + print({'samples': count, 'speech_valid': valid, 'archive': str(OUT), + 'bytes': OUT.stat().st_size}, flush=True) + + +if __name__ == '__main__': + main() diff --git a/vocal_burst_pool/p3/annotate_empathic.py b/vocal_burst_pool/p3/annotate_empathic.py new file mode 100644 index 0000000000000000000000000000000000000000..4a1cfd993ea0d9ef9fe4317114da5f2db3033873 --- /dev/null +++ b/vocal_burst_pool/p3/annotate_empathic.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +"""All cached Empathic-Insight-Voice-Plus experts, one BUD-E encoder pass.""" +from __future__ import annotations + +import io +import json +import os +import tarfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch +from torch import nn +from transformers import WhisperFeatureExtractor, WhisperModel + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3') +CACHE = Path('/e/data1/datasets/playground/mmlaion/schuhmann1/dramabox/hfcache/.cache/dramabox') +BUD_SHA = '9400e6740ea103bf619a4ce66374576fab8a4e23' +EIV_SHA = '9ceb45912f4d555fc0210e92ec8459234b3992ec' +BUD = CACHE / f'models--laion--BUD-E-Whisper/snapshots/{BUD_SHA}' +EIV = CACHE / f'models--laion--Empathic-Insight-Voice-Plus/snapshots/{EIV_SHA}' +IN = Path(os.environ.get('P3_INPUT', ROOT / 'pilot/p3-pilot-00000.tar')) +OUT = Path(os.environ.get('P3_OUTPUT', ROOT / 'pilot/p3-pilot-empathic-sidecars-00000.tar')) + + +class FullEmbeddingMLP(nn.Module): + def __init__(self): + super().__init__() + self.flatten = nn.Flatten() + self.proj = nn.Linear(1500 * 768, 64) + self.mlp = nn.Sequential( + nn.ReLU(), nn.Dropout(0.0), + nn.Linear(64, 64), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(64, 32), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(32, 16), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(16, 1), + ) + + def forward(self, x): + return self.mlp(self.proj(self.flatten(x))) + + +class PooledEmbeddingMLP(nn.Module): + def __init__(self): + super().__init__() + self.proj = nn.Linear(3072, 64) + self.mlp = nn.Sequential( + nn.ReLU(), nn.Dropout(0.0), + nn.Linear(64, 64), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(64, 32), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(32, 16), nn.ReLU(), nn.Dropout(0.1), + nn.Linear(16, 1), + ) + + def forward(self, x): + return self.mlp(self.proj(x)) + + +def models(): + result = {} + for path in sorted(EIV.glob('model_*_best.pth')): + quality = path.name.startswith('model_score_') + name = path.stem.removeprefix('model_').removesuffix('_best') + net = PooledEmbeddingMLP() if quality else FullEmbeddingMLP() + state = torch.load(path, map_location='cpu', weights_only=True) + if any(k.startswith('_orig_mod.') for k in state): + state = {k.removeprefix('_orig_mod.'): v for k, v in state.items()} + net.load_state_dict(state, strict=True) + result[name] = (net.to('cuda').eval(), quality) + print('loaded expert', name, flush=True) + if len(result) < 58: + raise RuntimeError(f'expected 54+4 experts, got {len(result)}') + return result + + +def records(): + with tarfile.open(IN) as tf: + members = {m.name: m for m in tf if m.isfile()} + for key in sorted(n[:-5] for n in members if n.endswith('.json')): + meta = json.load(tf.extractfile(members[key + '.json'])) + wav, sr = sf.read(io.BytesIO(tf.extractfile(members[key + '.opus']).read()), + dtype='float32') + assert sr == 16000 and wav.ndim == 1 + yield key, meta, wav + + +def add(tf, name, payload): + h = tarfile.TarInfo(name) + h.size, h.mtime = len(payload), 0 + tf.addfile(h, io.BytesIO(payload)) + + +@torch.inference_mode() +def main(): + assert torch.cuda.is_available() + feature_extractor = WhisperFeatureExtractor.from_pretrained(BUD, local_files_only=True) + full = WhisperModel.from_pretrained(BUD, local_files_only=True, + low_cpu_mem_usage=True) + encoder = full.get_encoder().to('cuda').eval() + del full + experts = models() + rows = list(records()) + OUT.parent.mkdir(parents=True, exist_ok=True) + tmp = OUT.with_suffix('.tar.incomplete') + n_valid = 0 + with tarfile.open(tmp, 'w') as tf: + for start in range(0, len(rows), 4): + part = rows[start:start + 4] + features = feature_extractor([w for _, _, w in part], sampling_rate=16000, + return_tensors='pt').input_features.to('cuda') + with torch.autocast('cuda', torch.bfloat16): + embedding = encoder(features).last_hidden_state.float() + assert embedding.shape == (len(part), 1500, 768), embedding.shape + pooled = torch.cat([embedding.mean(1), embedding.min(1).values, + embedding.max(1).values, embedding.std(1)], dim=1) + expert_scores = {} + for name, (net, quality) in experts.items(): + source = pooled if quality else embedding + expert_scores[name] = net(source).reshape(-1).cpu().numpy() + pooled16 = pooled.cpu().numpy().astype(np.float16) + for i, (key, meta, _) in enumerate(part): + has_speech = any(c['role'] == 'speech' for c in meta['components']) + result = { + 'schema_version': 'p3-empathic-sidecar-v2', 'sample_id': key, + 'teacher': f'laion/Empathic-Insight-Voice-Plus@{EIV_SHA}', + 'encoder': f'laion/BUD-E-Whisper@{BUD_SHA}', + 'speech_domain_valid': has_speech, + 'synthetic_mixture': True, + 'pooled_embedding_file': key + '.bud_e_pooled.npy', + 'pooled_embedding_dim': 3072, + 'scores': {name: round(float(values[i]), 5) for name, values + in expert_scores.items()}, + } + buf = io.BytesIO() + np.save(buf, pooled16[i], allow_pickle=False) + add(tf, key + '.bud_e_pooled.npy', buf.getvalue()) + add(tf, key + '.eiv.json', + json.dumps(result, separators=(',', ':')).encode()) + n_valid += has_speech + print('annotated', min(start + len(part), len(rows)), flush=True) + os.replace(tmp, OUT) + print({'samples': len(rows), 'speech_domain_valid': n_valid, + 'n_experts': len(experts), 'archive': str(OUT), + 'bytes': OUT.stat().st_size}, flush=True) + + +if __name__ == '__main__': + main() diff --git a/vocal_burst_pool/p3/annotate_voiceclap.py b/vocal_burst_pool/p3/annotate_voiceclap.py new file mode 100644 index 0000000000000000000000000000000000000000..7d3a9d69345cdc31bb5fa4f983ca1e6cfdbc6567 --- /dev/null +++ b/vocal_burst_pool/p3/annotate_voiceclap.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python3 +"""Write key-aligned VoiceCLAP/VoiceNet/genuineness/blend pilot sidecars. + +One shared frozen 768-d encoder pass feeds 57 regression and classification +heads plus the two extra regression heads. Scores are masked outside each +teacher's stated speech domain; embeddings are always retained. +""" +from __future__ import annotations + +import io +import json +import os +import sys +import tarfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import torch + +ROOT = Path('/e/scratch/reformo/schuhmann1_moss/vocalburst_p3') +TEACHERS = ROOT / 'teachers' +VN = TEACHERS / 'voicenet-dimension-predictors-commercial' +GEN = TEACHERS / 'voiceclap-commercial-genuineness' +BLEND = TEACHERS / 'voiceclap-commercial-vocalburst-blend' +ATTR = TEACHERS / 'voiceclap-commercial-attribute-heads' +IN = Path(os.environ.get('P3_INPUT', ROOT / 'pilot/p3-pilot-00000.tar')) +OUT = Path(os.environ.get('P3_OUTPUT', ROOT / 'pilot/p3-pilot-voiceclap-sidecars-00000.tar')) +REVISIONS = { + 'voiceclap': '4e0170bf9a7e29704dd3a26ca92855b3cdd8706c', + 'voicenet': 'cdafe5170561eaadb09f012ed5ae48e4f82b7b91', + 'genuineness': '36526b2264d92c61b372248253de4270791f52ee', + 'blend': 'fe7086f447f1de2aeb0fbf78f02b99b9c5e5074a', + 'attribute_heads': '8441fbd4050fb670c34453d99b3fdae7e8513667', +} + +sys.path.insert(0, str(VN)) +sys.path.insert(0, str(GEN)) +sys.path.insert(0, str(BLEND)) +sys.path.insert(0, str(ATTR)) +from predict import VoiceNetPredictor +from genuineness_scorer import _MLP as GenuinenessMLP +from blend_model import _build_head as build_blend_head +from voiceclap_heads import build_head as build_attribute_head + + +def weights(path: Path, kind: str): + ck = torch.load(path, map_location='cpu', weights_only=False) + if kind == 'genuineness': + a = ck['arch'] + net = GenuinenessMLP(int(a['D']), int(a['H']), float(a['dropout'])) + net.load_state_dict(ck['state_dict']) + else: + net = build_blend_head(ck) + mu = torch.as_tensor(np.asarray(ck['mu'], dtype=np.float32), device='cuda').reshape(1, -1) + sd = torch.as_tensor(np.asarray(ck['sd'], dtype=np.float32), device='cuda').reshape(1, -1) + return net.to('cuda').eval(), mu, sd + + +def records(): + with tarfile.open(IN) as tf: + members = {m.name: m for m in tf if m.isfile()} + for key in sorted(n[:-5] for n in members if n.endswith('.json')): + meta = json.load(tf.extractfile(members[key + '.json'])) + wav, sr = sf.read(io.BytesIO(tf.extractfile(members[key + '.opus']).read()), + dtype='float32') + assert sr == 16000 and wav.ndim == 1 and 16000 <= len(wav) <= 480000 + yield key, meta, wav + + +def add(tf: tarfile.TarFile, name: str, payload: bytes): + h = tarfile.TarInfo(name) + h.size, h.mtime = len(payload), 0 + tf.addfile(h, io.BytesIO(payload)) + + +@torch.no_grad() +def main(): + assert torch.cuda.is_available() + predictor = VoiceNetPredictor(root=str(VN), device='cuda') + assert len(predictor.dims) == 57, len(predictor.dims) + gen_net, gen_mu, gen_sd = weights(GEN / 'genuineness_head.pt', 'genuineness') + blend_net, blend_mu, blend_sd = weights(BLEND / 'blend_head_commercial.pt', 'blend') + attr_bundle = torch.load(ATTR / 'heads.pt', map_location='cpu', weights_only=False) + attr_mu = attr_bundle['emb_mu'].to('cuda').float() + attr_sd = attr_bundle['emb_sd'].to('cuda').float() + attr_heads = {name: build_attribute_head(state).to('cuda').eval() + for name, state in attr_bundle['heads'].items()} + assert len(attr_heads) == 61 and 'talking_speed' in attr_heads + rows = list(records()) + OUT.parent.mkdir(parents=True, exist_ok=True) + tmp = OUT.with_suffix('.tar.incomplete') + counted = {'samples': 0, 'voice_valid': 0, 'blend_valid': 0} + with tarfile.open(tmp, 'w') as tf: + for start in range(0, len(rows), 8): + part = rows[start:start + 8] + wavs = torch.zeros(len(part), 480000, dtype=torch.float32, device='cuda') + for i, (_, _, w) in enumerate(part): + wavs[i, :len(w)] = torch.from_numpy(w).to('cuda') + emb = predictor.embedder.encode_waveform(wavs).float() + assert emb.shape == (len(part), 768), emb.shape + emb = torch.nn.functional.normalize(emb, p=2, dim=-1) + vn = {} + for dim in predictor.dims: + reg, cls = predictor.reg[dim], predictor.cls[dim] + score = reg['net']((emb - reg['mu']) / reg['sd']).reshape(-1) + probs = torch.softmax(cls['net']((emb - cls['mu']) / cls['sd']), -1) + confidence, bucket = probs.max(-1) + vn[dim] = (score.cpu().numpy(), bucket.cpu().numpy(), + confidence.cpu().numpy()) + genuineness = gen_net((emb - gen_mu) / gen_sd).reshape(-1).clamp(0, 6).cpu().numpy() + blend = blend_net((emb - blend_mu) / blend_sd).reshape(-1).clamp(0, 10).cpu().numpy() + attr_input = (emb - attr_mu) / attr_sd + attributes = {name: head(attr_input).reshape(-1).cpu().numpy() + for name, head in attr_heads.items()} + embeddings = emb.cpu().numpy().astype(np.float16) + for i, (key, meta, _) in enumerate(part): + has_speech = any(c['role'] == 'speech' for c in meta['components']) + has_burst = meta['n_vocal_bursts'] > 0 + clean_background = meta.get('background_kind') == 'none' + scores = { + 'schema_version': 'p3-voiceclap-sidecar-v2', 'sample_id': key, + 'teacher_revisions': REVISIONS, + 'validity': {'speech_scores': has_speech, + 'vocalburst_blend': has_speech and has_burst, + 'speech_scores_clean_background': has_speech and clean_background, + 'vocalburst_blend_clean_background': + has_speech and has_burst and clean_background, + 'attribute_speech_domain': has_speech, + 'attribute_speech_clean_background': + has_speech and clean_background}, + 'voiceclap_embedding': {'file': key + '.voiceclap.npy', + 'dim': 768, 'dtype': 'float16', + 'l2_normalized': True}, + 'voicenet': { + dim: {'regression': round(float(vn[dim][0][i]), 5), + 'bucket': int(vn[dim][1][i]), + 'confidence': round(float(vn[dim][2][i]), 5)} + for dim in predictor.dims}, + 'genuineness_0_6': round(float(genuineness[i]), 5), + 'vocalburst_blend_0_10': round(float(blend[i]), 5), + 'voiceclap_attributes': { + name: round(float(value[i]), 5) + for name, value in attributes.items()}, + } + buf = io.BytesIO() + np.save(buf, embeddings[i], allow_pickle=False) + add(tf, key + '.voiceclap.npy', buf.getvalue()) + add(tf, key + '.scores.json', json.dumps(scores, separators=(',', ':')).encode()) + counted['samples'] += 1 + counted['voice_valid'] += int(has_speech) + counted['blend_valid'] += int(has_speech and has_burst) + print('annotated', min(start + len(part), len(rows)), flush=True) + os.replace(tmp, OUT) + report = {**counted, 'archive': str(OUT), 'archive_bytes': OUT.stat().st_size, + 'teacher_revisions': REVISIONS} + OUT.with_suffix('.summary.json').write_text(json.dumps(report, indent=2) + '\n') + print(report, flush=True) + + +if __name__ == '__main__': + main()