| ---
|
| language:
|
| - en
|
| license: cc-by-4.0
|
| task_categories:
|
| - automatic-speech-recognition
|
| - text-to-speech
|
| - audio-to-audio
|
| tags:
|
| - nano-duplex
|
| - conversational-speech
|
| - full-duplex
|
| - mimi
|
| configs:
|
| - config_name: alignment
|
| default: true
|
| data_files:
|
| - split: train
|
| path: alignment/train-*.parquet
|
| - split: validation
|
| path: alignment/validation-*.parquet
|
| - split: test
|
| path: alignment/test-*.parquet
|
| - config_name: interaction
|
| data_files:
|
| - split: train
|
| path: interaction/train-*.parquet
|
| - split: validation
|
| path: interaction/validation-*.parquet
|
| - split: test
|
| path: interaction/test-*.parquet
|
| ---
|
|
|
| # nano-duplex data v1
|
|
|
| English speech alignment and a first natural-conversation collection for nano-duplex research.
|
| All audio is embedded in Parquet as PCM16 FLAC at 24 kHz. No external audio paths are required.
|
|
|
| | Configuration | Split | Examples | Audio hours | Speakers |
|
| | --- | --- | ---: | ---: | ---: |
|
| | alignment | train | 26,478 | 32.377 | 247 |
|
| | alignment | validation | 4,708 | 5.817 | 40 |
|
| | alignment | test | 3,624 | 4.428 | 38 |
|
| | interaction | train | 946 | 15.240 | 57 |
|
| | interaction | validation | 122 | 1.978 | 11 |
|
| | interaction | test | 116 | 1.877 | 11 |
|
|
|
| ## Sources and processing
|
|
|
| **Alignment:** [LibriTTS-R](https://www.openslr.org/141/), by Yuma Koizumi and colleagues.
|
| The complete official `train_clean_100`, `dev_clean`, and `test_clean` archives passed the
|
| official MD5 checks before extraction. Keep complete mono 24 kHz PCM16 utterances from
|
| 1 through 10 seconds with normalized transcripts. Preserve official splits and original IDs.
|
| Reject missing transcripts, out-of-range durations, duplicate audio, and entirely silent clips.
|
| WAV-to-FLAC conversion is lossless; original WAV and decoded PCM SHA256 are recorded.
|
| Original and normalized text are included; the source transcript hash refers to the original
|
| normalized-text file, before trimming its surrounding whitespace. RMS and clipping metrics
|
| are diagnostics, not human quality judgments. This is a filtered subset, not all hours implied
|
| by the upstream archive names. Citation: Koizumi et al., "LibriTTS-R: A Restored Multi-Speaker
|
| Text-to-Speech Corpus," 2023.
|
|
|
| **Interaction:** [otoearth/otoSpeech-full-duplex-processed-141h](https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h).
|
| Use eight complete upstream tar shards, selected by lexicographic filename order, at revision
|
| `c635c29ad083570fe2cadbafc75770e76eded2da`. Every shard passed its pinned Hub SHA256.
|
| The selection contains 73 complete sessions. Resample each complete stereo session from
|
| 44.1 kHz to 24 kHz using SciPy polyphase filtering, then split into consecutive 60-second
|
| windows (including the shorter final window). Both channels retain the shared clock.
|
| Preserve overlapping speech and silence, and re-mute the original redacted intervals after
|
| resampling. The two `speaker_ids` are ordered by audio channel. Source-relative timestamps
|
| allow exact reassembly. Speaker-connected components stay within one split; there is no
|
| reported speaker-ID or session overlap across train/validation/test. Speaker identities beyond
|
| the IDs reported by the source have not been independently verified. Omit optional surveys
|
| and demographic profiles. Citation: otoearth, "otoSpeech-full-duplex-processed-141h," 2026.
|
|
|
| ## Scope
|
|
|
| Natural conversations have no assistant tool traces, cancelled playback plans, certified
|
| prefix-causal action targets, or transcripts in this release. `causal_verified=false` and
|
| `has_transcript=false` are explicit. These recordings support interaction preparation and
|
| analysis; they are not ready-made Stage B-D causal trajectories. No synthetic speech is
|
| substituted for real recordings. Tool, vision, and motion supervision are outside this release.
|
|
|
| `codec-pilot/` contains real Mimi ASR features and eight-codebook TTS targets for 773 training
|
| utterances (57.301 minutes), drawn from `alignment/train`. This pilot has no independent
|
| evaluation split. Its JSONL manifests reference tensor files packed in `tensors.tar`;
|
| `cache-index.json` records every member's size and SHA256. The server entry expands and
|
| verifies them automatically. The full
|
| alignment and interaction collections are processed audio; only this pilot is codec-encoded.
|
| The exact Mimi repository, revision, checkpoint digest and cache dimensions are recorded.
|
| Tensor files must be loaded with `torch.load(..., weights_only=True)` as nano-duplex does.
|
| The provided `codec-pilot/asr.toml` and `codec-pilot/tts.toml` select matching pinned assets.
|
|
|
| ## Loading
|
|
|
| ```python
|
| from datasets import load_dataset, Audio
|
|
|
| alignment = load_dataset("JonesLin/nano-duplex-data-v1", "alignment", token=True)
|
| interaction = load_dataset("JonesLin/nano-duplex-data-v1", "interaction", token=True)
|
| # Decode with soundfile without requiring torchcodec:
|
| audio = alignment["train"].cast_column("audio", Audio(decode=False))[0]["audio"]
|
| from io import BytesIO
|
| import soundfile as sf
|
| pcm, sample_rate = sf.read(BytesIO(audio["bytes"]))
|
| ```
|
|
|
| From the nano-duplex project checkout, install `requirements-speech.txt` and
|
| `requirements-data.txt`, then use the server entry (a copy is included at
|
| `tools/hf_training.py`; it belongs in the project's `nano_duplex` package):
|
|
|
| ```bash
|
| # Download only the small pre-encoded pilot, verify and expand it, then train ASR/TTS.
|
| python -m nano_duplex.hf_training --profile pilot --steps 400
|
|
|
| # Full Stage A: download alignment data, encode each split once, train, evaluate.
|
| python -m nano_duplex.hf_training --profile full --nproc 4 --steps 20000
|
| ```
|
|
|
| The first command resolves a concrete Hub commit and writes `dataset-lock.json`.
|
| Alternatively pass `--revision` with an exact commit. `--snapshot-dir`, `--work-dir`,
|
| and `--run-dir` select server storage. `--phase prepare` stops before training;
|
| `--phase train --resume` reuses data caches and resumes existing task checkpoints.
|
| An unstarted second task starts fresh. Use `--dry-run` to inspect commands without
|
| network access or file writes. H200 distributed execution has not been run here;
|
| 20,000 steps is a starting budget, not a measured convergence requirement.
|
|
|
| The full profile still performs one Mimi encoding pass over the complete alignment
|
| collection on first use. Subsequent runs reuse checked prepared manifests. Use
|
| `tools/materialize_alignment.py` separately if only mono WAV/raw JSONL export is needed.
|
| The interaction configuration is available through `load_dataset`, but is not fed into
|
| the causal duplex trainer without the additional labels described above.
|
| Changing a dataset manifest requires a new training run, not resuming an old optimizer state.
|
|
|
| ## Provenance and license
|
|
|
| Both source corpora are provided under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
|
| This derivative retains that license and the source attributions above. The source-provided
|
| gated dataset access was obtained through the collecting user's own Hugging Face account.
|
| Source receipts, split statistics, exclusions, processing parameters, and full validation
|
| results are in `reports/`. `MANIFEST.json` inventories the exact release files with SHA256.
|
| The validation reads every packaged audio example, checks its PCM hash, duration, channel
|
| count, session continuity, redaction masks, and split isolation. No speech-quality or
|
| conversational-performance claim follows from those structural checks.
|
|
|
