Release Ninada v1.0: Disentangled Pan-Indic & Classical Sanskrit Neural TTS + 56.6MB Browser ONNX
Browse files- README.md +74 -0
- config.json +82 -0
- gcp_job_manifest.json +149 -0
- model.safetensors +3 -0
- ninada_config.json +16 -0
- onnx/export_report.json +111 -0
- onnx/model_quantized.onnx +3 -0
- onnx/vocab.json +101 -0
- training_history.json +1690 -0
- vocab.json +101 -0
- wise_ft_selection.json +86 -0
README.md
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---
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language:
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- sa
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- hi
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- kn
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- ta
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- te
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- ml
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license: mit
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library_name: onnx
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pipeline_tag: text-to-speech
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datasets:
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- prathoshap/sushrota-sanskrit-asr-data
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- surajp/shrutilipi_sanskrit
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- addy88/sanskrit-asr-84
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- yashnbx/gita_supersite_sanskrit_tts
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- SayantanJoker/SYSPIN_Hindi_Female_TTS
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- SayantanJoker/original_data_kannada_tts
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- SayantanJoker/original_data_tamil_tts
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- arpit-tiwari/syspin-telugu-tts
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- thennal/indic_tts_ml
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tags:
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- text-to-speech
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- ninada
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- sanskrit
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- vedic
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- indic
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- vits
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- hifi-gan
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- onnxruntime-web
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- wasm
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---
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# `Ninada` (निनाद) v1.0 — Disentangled Pan-Indic & Classical Sanskrit Neural TTS
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`Ninada` (*Nināda* — Sanskrit `निनाद`, deep resonant sound) is a compact, single-checkpoint **Classical Sanskrit & Pan-Indic** neural text-to-speech model (**`56.6 MB` single-file browser ONNX**) supporting **6 Indic languages** (`Sanskrit [sa]`, `Hindi [hi]`, `Kannada [kn]`, `Tamil [ta]`, `Telugu [te]`, `Malayalam [ml]`) across **9 Brahmic scripts + ISO-15919/IAST** (`87` Pan-Indic & Vedic/Classical Sanskrit phoneme tokens including vocalic `ऋ`/`ॠ`, velar nasal `ङ`, Dravidian retroflexes `ळ`/`ऴ`/`ऱ`/`ऩ`, *pūrvarūpa avagraha* sandhi, and *Pāṇinian samyoge guru* prosody).
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- **Live Browser WASM SIMD Studio**: [https://hemanth.github.io/ninada/](https://hemanth.github.io/ninada/)
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- **GitHub Repository & SDKs (JS + Python)**: [https://github.com/hemanth/ninada](https://github.com/hemanth/ninada)
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- **Curated Split Metadata**: [https://huggingface.co/datasets/gnumanth/ninada-sanskrit-indic](https://huggingface.co/datasets/gnumanth/ninada-sanskrit-indic)
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## GCP NVIDIA L4 GPU Training & Disentangled Architecture
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- **Hardware**: Trained on **Google Cloud (`NVIDIA L4` 24 GB GPU)** for `24` epochs (`3192` steps) with speaker-disentangled `UtteranceStyleEncoder` + EMA per-language studio voice centroids, latent-manifold feature matching in the frozen adversarial 16 kHz HiFi-GAN space, vectorized Viterbi Monotonic Alignment Search (`MAS`), and validation-only Weight-Space Ensembling (`WiSE-FT`).
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- **Curated Multi-Domain Corpus (`1327` studio-aligned clips, `144.0` min at 16 kHz; `1061` train / `133` val / `133` test)**:
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- **Sanskrit (`474` studio-gated clips across 4 corpora)**: `prathoshap/sushrota-sanskrit-asr-data` (IISc Vedānta prose), `surajp/shrutilipi_sanskrit` (All India Radio Sanskrit news), `addy88/sanskrit-asr-84` (IITB WadaSNR >= 32 dB spoken Sanskrit), and `yashnbx/gita_supersite_sanskrit_tts` (IITK Bhagavad-Gītā ślokas).
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- **Pan-Indic (`853` clips across 5 studio corpora)**: SYSPIN Hindi, Kannada, Tamil, Telugu, and IITM Malayalam.
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## Held-Out Test Leaderboard (`N = 133` Utterances; `B = 1,000` Bootstrap 95% CIs)
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| System | Footprint (↓) | Coverage (↑) | MCD (dB, ↓) | Log-F0 RMSE (c, ↓) | Dur MAE (s, ↓) | Sanskrit CER (%, ↓) | Pan-Indic CER (%, ↓) |
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|---|---:|---:|---:|---:|---:|---:|---:|
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| Studio Human Audio (Topline) | — | 100.0% | — | — | — | `4.16 [2.92, 5.42]%` | `16.50 [14.46, 18.76]%` |
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| Base `facebook/mms-tts-hin` (Raw) | 138.8 MB | 55.6% | `7.60 [7.24, 7.97]` | 911.6 | 3.71s | `23.11 [20.07, 26.23]%` | `60.50 [54.01, 67.15]%` |
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| Base `mms-tts-hin` + `Ninada` G2P | 138.8 MB | 100.0% | `6.57 [6.18, 6.99]` | 738.5 | 1.16s | `23.65 [20.56, 26.73]%` | `33.45 [30.91, 36.20]%` |
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| Meta MMS 5-Specialist Fleet (`5x` Models) | 694.0 MB | 100.0% | `6.29 [5.89, 6.72]` | 720.2 | 1.19s | `23.11 [20.07, 26.23]%` | `27.02 [25.09, 28.84]%` |
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| **`Ninada` Tier-1A (GCP L4 FP32)** | 140.0 MB | **100.0%** | **`5.56 [5.11, 6.04]`** | **554.9** | **0.89s** | **`13.59 [11.45, 15.71]%`** | **`22.88 [21.07, 24.77]%`** |
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| **`Ninada` Tier-1B (Browser ONNX)** | **56.6 MB** | **100.0%** | **`5.56 [5.12, 6.04]`** | **553.0** | **0.89s** | **`13.46 [11.31, 15.42]%`** | **`22.88 [20.90, 24.98]%`** |
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## Quickstart
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```js
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import ninada from 'ninada';
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const voice = await ninada('sanskrit', {
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model: 'https://huggingface.co/gnumanth/ninada/resolve/main/onnx/model_quantized.onnx',
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});
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const wavBuffer = await voice.speak('ऋतं च सत्यं चाभीद्धात् तपसोऽध्यजायत ।');
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```
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```python
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from ninada import Ninada
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wav_bytes = Ninada(lang="sa").speak("प्रज्ञापूर्वकव्यापारवत्त्वं जीवत्वम् ।")
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```
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config.json
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{
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"activation_dropout": 0.1,
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"architectures": [
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"VitsModel"
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],
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"attention_dropout": 0.1,
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"depth_separable_channels": 2,
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"depth_separable_num_layers": 3,
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"duration_predictor_dropout": 0.5,
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"duration_predictor_filter_channels": 256,
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"duration_predictor_flow_bins": 10,
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"duration_predictor_kernel_size": 3,
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"duration_predictor_num_flows": 4,
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"duration_predictor_tail_bound": 5.0,
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"ffn_dim": 768,
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"ffn_kernel_size": 3,
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"flow_size": 192,
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"hidden_act": "relu",
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"hidden_dropout": 0.1,
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"hidden_size": 192,
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"initializer_range": 0.02,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.0,
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"leaky_relu_slope": 0.1,
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"model_type": "vits",
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"noise_scale": 0.667,
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"noise_scale_duration": 0.8,
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"num_attention_heads": 2,
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"num_hidden_layers": 6,
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"num_speakers": 1,
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"posterior_encoder_num_wavenet_layers": 16,
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"prior_encoder_num_flows": 4,
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"prior_encoder_num_wavenet_layers": 4,
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"resblock_dilation_sizes": [
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[
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1,
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3,
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5
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],
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[
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1,
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3,
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5
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],
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[
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1,
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5
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]
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],
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"resblock_kernel_sizes": [
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3,
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7,
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11
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],
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"sampling_rate": 16000,
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"speaker_embedding_size": 0,
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"speaking_rate": 1.0,
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"spectrogram_bins": 513,
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"torch_dtype": "float32",
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"transformers_version": "4.48.0",
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"upsample_initial_channel": 512,
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"upsample_kernel_sizes": [
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16,
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16,
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4,
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4
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],
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"upsample_rates": [
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8,
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8,
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2,
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2
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],
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"use_bias": true,
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"use_stochastic_duration_prediction": false,
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"vocab_size": 87,
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"wavenet_dilation_rate": 1,
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"wavenet_dropout": 0.0,
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"wavenet_kernel_size": 5,
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"window_size": 4
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}
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gcp_job_manifest.json
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{
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"model_name": "ninada",
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"project": "hemanth-hm",
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| 4 |
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"cloud_run_execution": "sntts-l4-trainer-8fqss",
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| 5 |
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"region": "us-east4",
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| 6 |
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"hostname": "localhost",
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| 7 |
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"platform": "Linux-6.9.12-x86_64-with-glibc2.35",
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| 8 |
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"pytorch_version": "2.3.1+cu121",
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| 9 |
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"cuda_available": true,
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| 10 |
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"cuda_version": "12.1",
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| 11 |
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"gpu_name": "NVIDIA L4",
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| 12 |
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"gpu_vram_gb": 22.03,
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| 13 |
+
"train_utterances": 1061,
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| 14 |
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"val_utterances": 133,
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| 15 |
+
"epochs": 24,
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| 16 |
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"batch_size": 8,
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| 17 |
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"total_steps": 3192,
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| 18 |
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"best_epoch": 22,
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| 19 |
+
"total_elapsed_sec": 672.68,
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| 20 |
+
"parameter_counts": {
|
| 21 |
+
"total_params": 36690242,
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| 22 |
+
"trainable_params": 15136898,
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| 23 |
+
"frozen_vocoder_posterior_params": 21553344,
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| 24 |
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"head_group_params": 1753730,
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| 25 |
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"backbone_group_params": 13383168
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| 26 |
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},
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| 27 |
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"pretrain_baseline": {
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| 28 |
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"val_loss": 64.54123,
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| 29 |
+
"val_kl_loss": 0.06189,
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| 30 |
+
"val_latent_loss": 0.57918,
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| 31 |
+
"val_dur_loss": 20.15345,
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| 32 |
+
"val_mel_l1": 1.34037,
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| 33 |
+
"val_mr_stft": 2.91459,
|
| 34 |
+
"val_dur_mae_frames": 8.88917,
|
| 35 |
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"val_utt_dur_mae_sec": 19.51226,
|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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"n": 13,
|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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},
|
| 58 |
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"ta": {
|
| 59 |
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"n": 18,
|
| 60 |
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"val_kl": 0.0603,
|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
+
},
|
| 65 |
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"te": {
|
| 66 |
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"n": 18,
|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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},
|
| 72 |
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"ml": {
|
| 73 |
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"n": 18,
|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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}
|
| 79 |
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}
|
| 80 |
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},
|
| 81 |
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"best_epoch_metrics": {
|
| 82 |
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"epoch": 22,
|
| 83 |
+
"step": 2926,
|
| 84 |
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"epoch_sec": 25.73,
|
| 85 |
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"lr_head": 3.78e-05,
|
| 86 |
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"lr_backbone": 5.7e-06,
|
| 87 |
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"is_best": true,
|
| 88 |
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"train_loss": 7.69144,
|
| 89 |
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|
| 90 |
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|
| 91 |
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"train_dur_loss": 0.68123,
|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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"per_lang": {
|
| 105 |
+
"sa": {
|
| 106 |
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"n": 48,
|
| 107 |
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|
| 108 |
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|
| 109 |
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"val_dur_mae_frames": 2.0371,
|
| 110 |
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|
| 111 |
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},
|
| 112 |
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"hi": {
|
| 113 |
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"n": 18,
|
| 114 |
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"val_kl": 0.0358,
|
| 115 |
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|
| 116 |
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|
| 117 |
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"val_mel_l1": 0.9212
|
| 118 |
+
},
|
| 119 |
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"kn": {
|
| 120 |
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"n": 13,
|
| 121 |
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"val_kl": 0.0333,
|
| 122 |
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|
| 123 |
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|
| 124 |
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"val_mel_l1": 0.8907
|
| 125 |
+
},
|
| 126 |
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"ta": {
|
| 127 |
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"n": 18,
|
| 128 |
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|
| 129 |
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"val_latent": 0.2417,
|
| 130 |
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"val_dur_mae_frames": 1.4519,
|
| 131 |
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"val_mel_l1": 0.9477
|
| 132 |
+
},
|
| 133 |
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"te": {
|
| 134 |
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"n": 18,
|
| 135 |
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|
| 136 |
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"val_latent": 0.2302,
|
| 137 |
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"val_dur_mae_frames": 1.5841,
|
| 138 |
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"val_mel_l1": 1.0068
|
| 139 |
+
},
|
| 140 |
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"ml": {
|
| 141 |
+
"n": 18,
|
| 142 |
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"val_kl": 0.0452,
|
| 143 |
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"val_latent": 0.2962,
|
| 144 |
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"val_dur_mae_frames": 1.6324,
|
| 145 |
+
"val_mel_l1": 1.0299
|
| 146 |
+
}
|
| 147 |
+
}
|
| 148 |
+
}
|
| 149 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d284470231fe61015d3a02074ee9fb47b8a7adb3e7fc813bc3d0af7bcb853af1
|
| 3 |
+
size 146815112
|
ninada_config.json
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"vocab_size": 87,
|
| 3 |
+
"num_languages": 6,
|
| 4 |
+
"hidden_size": 192,
|
| 5 |
+
"flow_size": 192,
|
| 6 |
+
"speaker_embedding_size": 192,
|
| 7 |
+
"spectrogram_bins": 513,
|
| 8 |
+
"sampling_rate": 16000,
|
| 9 |
+
"n_fft": 1024,
|
| 10 |
+
"hop_length": 256,
|
| 11 |
+
"win_length": 1024,
|
| 12 |
+
"segment_frames": 32,
|
| 13 |
+
"base_checkpoint": "facebook/mms-tts-hin",
|
| 14 |
+
"speaking_rate": 1.0,
|
| 15 |
+
"noise_scale": 0.32
|
| 16 |
+
}
|
onnx/export_report.json
ADDED
|
@@ -0,0 +1,111 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"fp16_params": 29380160,
|
| 3 |
+
"fp32_params": 16962,
|
| 4 |
+
"fp32_size_mb": 112.61,
|
| 5 |
+
"quantized_size_mb": 56.61,
|
| 6 |
+
"compression_ratio": 1.99,
|
| 7 |
+
"op_types": [
|
| 8 |
+
"Add",
|
| 9 |
+
"Cast",
|
| 10 |
+
"Clip",
|
| 11 |
+
"Concat",
|
| 12 |
+
"Constant",
|
| 13 |
+
"ConstantOfShape",
|
| 14 |
+
"Conv",
|
| 15 |
+
"ConvTranspose",
|
| 16 |
+
"CumSum",
|
| 17 |
+
"Div",
|
| 18 |
+
"Exp",
|
| 19 |
+
"Floor",
|
| 20 |
+
"Gather",
|
| 21 |
+
"LayerNormalization",
|
| 22 |
+
"LeakyRelu",
|
| 23 |
+
"Less",
|
| 24 |
+
"MatMul",
|
| 25 |
+
"Mul",
|
| 26 |
+
"Neg",
|
| 27 |
+
"Pad",
|
| 28 |
+
"RandomNormalLike",
|
| 29 |
+
"Range",
|
| 30 |
+
"Reciprocal",
|
| 31 |
+
"ReduceMax",
|
| 32 |
+
"ReduceSum",
|
| 33 |
+
"Relu",
|
| 34 |
+
"Reshape",
|
| 35 |
+
"Shape",
|
| 36 |
+
"Sigmoid",
|
| 37 |
+
"Slice",
|
| 38 |
+
"Softmax",
|
| 39 |
+
"Split",
|
| 40 |
+
"Squeeze",
|
| 41 |
+
"Sub",
|
| 42 |
+
"Tanh",
|
| 43 |
+
"Transpose",
|
| 44 |
+
"Unsqueeze"
|
| 45 |
+
],
|
| 46 |
+
"mean_quant_snr_db": 41.79,
|
| 47 |
+
"min_pearson_r": 0.99993,
|
| 48 |
+
"export_elapsed_sec": 10.59,
|
| 49 |
+
"parity_by_language": [
|
| 50 |
+
{
|
| 51 |
+
"lang": "sa",
|
| 52 |
+
"seq_len": 103,
|
| 53 |
+
"samples": 77824,
|
| 54 |
+
"duration_sec": 4.864,
|
| 55 |
+
"fp32_snr_db": 100.48,
|
| 56 |
+
"quant_snr_db": 40.7,
|
| 57 |
+
"pearson_r": 0.999957,
|
| 58 |
+
"max_abs_diff_fp32": 1.9118189811706543e-05
|
| 59 |
+
},
|
| 60 |
+
{
|
| 61 |
+
"lang": "hi",
|
| 62 |
+
"seq_len": 107,
|
| 63 |
+
"samples": 71168,
|
| 64 |
+
"duration_sec": 4.448,
|
| 65 |
+
"fp32_snr_db": 101.92,
|
| 66 |
+
"quant_snr_db": 42.44,
|
| 67 |
+
"pearson_r": 0.999972,
|
| 68 |
+
"max_abs_diff_fp32": 1.780688762664795e-05
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"lang": "kn",
|
| 72 |
+
"seq_len": 71,
|
| 73 |
+
"samples": 55808,
|
| 74 |
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"duration_sec": 3.488,
|
| 75 |
+
"fp32_snr_db": 92.56,
|
| 76 |
+
"quant_snr_db": 38.55,
|
| 77 |
+
"pearson_r": 0.99993,
|
| 78 |
+
"max_abs_diff_fp32": 6.508827209472656e-05
|
| 79 |
+
},
|
| 80 |
+
{
|
| 81 |
+
"lang": "ta",
|
| 82 |
+
"seq_len": 51,
|
| 83 |
+
"samples": 32512,
|
| 84 |
+
"duration_sec": 2.032,
|
| 85 |
+
"fp32_snr_db": 100.93,
|
| 86 |
+
"quant_snr_db": 39.8,
|
| 87 |
+
"pearson_r": 0.999948,
|
| 88 |
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"max_abs_diff_fp32": 2.8461217880249023e-05
|
| 89 |
+
},
|
| 90 |
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{
|
| 91 |
+
"lang": "te",
|
| 92 |
+
"seq_len": 53,
|
| 93 |
+
"samples": 38656,
|
| 94 |
+
"duration_sec": 2.416,
|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
+
},
|
| 100 |
+
{
|
| 101 |
+
"lang": "ml",
|
| 102 |
+
"seq_len": 49,
|
| 103 |
+
"samples": 43008,
|
| 104 |
+
"duration_sec": 2.688,
|
| 105 |
+
"fp32_snr_db": 96.27,
|
| 106 |
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|
| 107 |
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|
| 108 |
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"max_abs_diff_fp32": 4.512816667556763e-05
|
| 109 |
+
}
|
| 110 |
+
]
|
| 111 |
+
}
|
onnx/model_quantized.onnx
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1c183bb7bc21b1a722a2a93d88a0717399e563a09cd72a92ae0625ca8f7dfb3c
|
| 3 |
+
size 59360070
|
onnx/vocab.json
ADDED
|
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"vocab_tokens": [
|
| 3 |
+
"फ",
|
| 4 |
+
"4",
|
| 5 |
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"1",
|
| 6 |
+
"-",
|
| 7 |
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"अ",
|
| 8 |
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"इ",
|
| 9 |
+
"8",
|
| 10 |
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|
| 11 |
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"0",
|
| 12 |
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"छ",
|
| 13 |
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"न",
|
| 14 |
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"ए",
|
| 15 |
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"ऐ",
|
| 16 |
+
"़",
|
| 17 |
+
"ष",
|
| 18 |
+
"ि",
|
| 19 |
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"ँ",
|
| 20 |
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"च",
|
| 21 |
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"2",
|
| 22 |
+
"ऑ",
|
| 23 |
+
"थ",
|
| 24 |
+
"भ",
|
| 25 |
+
"ी",
|
| 26 |
+
"",
|
| 27 |
+
"ॅ",
|
| 28 |
+
"3",
|
| 29 |
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"ञ",
|
| 30 |
+
"ै",
|
| 31 |
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"ु",
|
| 32 |
+
"ठ",
|
| 33 |
+
"ं",
|
| 34 |
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"ॉ",
|
| 35 |
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"उ",
|
| 36 |
+
"_",
|
| 37 |
+
"ई",
|
| 38 |
+
"ः",
|
| 39 |
+
"ह",
|
| 40 |
+
"ध",
|
| 41 |
+
"ल",
|
| 42 |
+
"र",
|
| 43 |
+
"स",
|
| 44 |
+
"ब",
|
| 45 |
+
"ख",
|
| 46 |
+
"ण",
|
| 47 |
+
"'",
|
| 48 |
+
"`",
|
| 49 |
+
"व",
|
| 50 |
+
"घ",
|
| 51 |
+
"प",
|
| 52 |
+
"ग",
|
| 53 |
+
"ढ",
|
| 54 |
+
"य",
|
| 55 |
+
"े",
|
| 56 |
+
"्",
|
| 57 |
+
"ा",
|
| 58 |
+
"आ",
|
| 59 |
+
"ड",
|
| 60 |
+
"ज",
|
| 61 |
+
"झ",
|
| 62 |
+
"श",
|
| 63 |
+
"औ",
|
| 64 |
+
"ो",
|
| 65 |
+
"द",
|
| 66 |
+
"ृ",
|
| 67 |
+
"ौ",
|
| 68 |
+
"ऊ",
|
| 69 |
+
"ू",
|
| 70 |
+
"ओ",
|
| 71 |
+
"ट",
|
| 72 |
+
"त",
|
| 73 |
+
"क",
|
| 74 |
+
"म",
|
| 75 |
+
"<unk>",
|
| 76 |
+
"ळ",
|
| 77 |
+
"ऴ",
|
| 78 |
+
"ऱ",
|
| 79 |
+
"ऩ",
|
| 80 |
+
"ऎ",
|
| 81 |
+
"ऒ",
|
| 82 |
+
"ॆ",
|
| 83 |
+
"ॊ",
|
| 84 |
+
"ॠ",
|
| 85 |
+
"ॄ",
|
| 86 |
+
"।",
|
| 87 |
+
"॥",
|
| 88 |
+
"ऋ",
|
| 89 |
+
"ङ"
|
| 90 |
+
],
|
| 91 |
+
"language_ids": {
|
| 92 |
+
"sa": 0,
|
| 93 |
+
"hi": 1,
|
| 94 |
+
"kn": 2,
|
| 95 |
+
"ta": 3,
|
| 96 |
+
"te": 4,
|
| 97 |
+
"ml": 5
|
| 98 |
+
},
|
| 99 |
+
"sampling_rate": 16000,
|
| 100 |
+
"hop_length": 256
|
| 101 |
+
}
|
training_history.json
ADDED
|
@@ -0,0 +1,1690 @@
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|
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|
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|
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|
| 1687 |
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|
| 1688 |
+
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|
| 1689 |
+
}
|
| 1690 |
+
]
|
vocab.json
ADDED
|
@@ -0,0 +1,101 @@
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|
| 1 |
+
{
|
| 2 |
+
"vocab_tokens": [
|
| 3 |
+
"फ",
|
| 4 |
+
"4",
|
| 5 |
+
"1",
|
| 6 |
+
"-",
|
| 7 |
+
"अ",
|
| 8 |
+
"इ",
|
| 9 |
+
"8",
|
| 10 |
+
" ",
|
| 11 |
+
"0",
|
| 12 |
+
"छ",
|
| 13 |
+
"न",
|
| 14 |
+
"ए",
|
| 15 |
+
"ऐ",
|
| 16 |
+
"़",
|
| 17 |
+
"ष",
|
| 18 |
+
"ि",
|
| 19 |
+
"ँ",
|
| 20 |
+
"च",
|
| 21 |
+
"2",
|
| 22 |
+
"ऑ",
|
| 23 |
+
"थ",
|
| 24 |
+
"भ",
|
| 25 |
+
"ी",
|
| 26 |
+
"",
|
| 27 |
+
"ॅ",
|
| 28 |
+
"3",
|
| 29 |
+
"ञ",
|
| 30 |
+
"ै",
|
| 31 |
+
"ु",
|
| 32 |
+
"ठ",
|
| 33 |
+
"ं",
|
| 34 |
+
"ॉ",
|
| 35 |
+
"उ",
|
| 36 |
+
"_",
|
| 37 |
+
"ई",
|
| 38 |
+
"ः",
|
| 39 |
+
"ह",
|
| 40 |
+
"ध",
|
| 41 |
+
"ल",
|
| 42 |
+
"र",
|
| 43 |
+
"स",
|
| 44 |
+
"ब",
|
| 45 |
+
"ख",
|
| 46 |
+
"ण",
|
| 47 |
+
"'",
|
| 48 |
+
"`",
|
| 49 |
+
"व",
|
| 50 |
+
"घ",
|
| 51 |
+
"प",
|
| 52 |
+
"ग",
|
| 53 |
+
"ढ",
|
| 54 |
+
"य",
|
| 55 |
+
"े",
|
| 56 |
+
"्",
|
| 57 |
+
"ा",
|
| 58 |
+
"आ",
|
| 59 |
+
"ड",
|
| 60 |
+
"ज",
|
| 61 |
+
"झ",
|
| 62 |
+
"श",
|
| 63 |
+
"औ",
|
| 64 |
+
"ो",
|
| 65 |
+
"द",
|
| 66 |
+
"ृ",
|
| 67 |
+
"ौ",
|
| 68 |
+
"ऊ",
|
| 69 |
+
"ू",
|
| 70 |
+
"ओ",
|
| 71 |
+
"ट",
|
| 72 |
+
"त",
|
| 73 |
+
"क",
|
| 74 |
+
"म",
|
| 75 |
+
"<unk>",
|
| 76 |
+
"ळ",
|
| 77 |
+
"ऴ",
|
| 78 |
+
"ऱ",
|
| 79 |
+
"ऩ",
|
| 80 |
+
"ऎ",
|
| 81 |
+
"ऒ",
|
| 82 |
+
"ॆ",
|
| 83 |
+
"ॊ",
|
| 84 |
+
"ॠ",
|
| 85 |
+
"ॄ",
|
| 86 |
+
"।",
|
| 87 |
+
"॥",
|
| 88 |
+
"ऋ",
|
| 89 |
+
"ङ"
|
| 90 |
+
],
|
| 91 |
+
"language_ids": {
|
| 92 |
+
"sa": 0,
|
| 93 |
+
"hi": 1,
|
| 94 |
+
"kn": 2,
|
| 95 |
+
"ta": 3,
|
| 96 |
+
"te": 4,
|
| 97 |
+
"ml": 5
|
| 98 |
+
},
|
| 99 |
+
"sampling_rate": 16000,
|
| 100 |
+
"hop_length": 256
|
| 101 |
+
}
|
wise_ft_selection.json
ADDED
|
@@ -0,0 +1,86 @@
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"split_used": "val_meta.jsonl (strictly zero test-set access)",
|
| 3 |
+
"selected_alpha": 0.7,
|
| 4 |
+
"selected_metrics": {
|
| 5 |
+
"alpha": 0.7,
|
| 6 |
+
"val_mcd_db": 5.575,
|
| 7 |
+
"val_f0_cents": 566.8,
|
| 8 |
+
"val_dur_mae_sec": 0.868,
|
| 9 |
+
"val_sa_cer_pct": 14.08,
|
| 10 |
+
"pareto_cost": 2.6884,
|
| 11 |
+
"per_lang_mcd": {
|
| 12 |
+
"sa": 7.953,
|
| 13 |
+
"hi": 4.077,
|
| 14 |
+
"kn": 4.302,
|
| 15 |
+
"ta": 4.008,
|
| 16 |
+
"te": 4.412,
|
| 17 |
+
"ml": 4.385
|
| 18 |
+
}
|
| 19 |
+
},
|
| 20 |
+
"sweep": [
|
| 21 |
+
{
|
| 22 |
+
"alpha": 0.55,
|
| 23 |
+
"val_mcd_db": 5.782,
|
| 24 |
+
"val_f0_cents": 588.6,
|
| 25 |
+
"val_dur_mae_sec": 0.886,
|
| 26 |
+
"val_sa_cer_pct": 14.55,
|
| 27 |
+
"pareto_cost": 2.7797,
|
| 28 |
+
"per_lang_mcd": {
|
| 29 |
+
"sa": 8.023,
|
| 30 |
+
"hi": 4.42,
|
| 31 |
+
"kn": 4.711,
|
| 32 |
+
"ta": 4.325,
|
| 33 |
+
"te": 4.613,
|
| 34 |
+
"ml": 4.572
|
| 35 |
+
}
|
| 36 |
+
},
|
| 37 |
+
{
|
| 38 |
+
"alpha": 0.65,
|
| 39 |
+
"val_mcd_db": 5.643,
|
| 40 |
+
"val_f0_cents": 576.7,
|
| 41 |
+
"val_dur_mae_sec": 0.872,
|
| 42 |
+
"val_sa_cer_pct": 14.62,
|
| 43 |
+
"pareto_cost": 2.7449,
|
| 44 |
+
"per_lang_mcd": {
|
| 45 |
+
"sa": 7.983,
|
| 46 |
+
"hi": 4.179,
|
| 47 |
+
"kn": 4.43,
|
| 48 |
+
"ta": 4.12,
|
| 49 |
+
"te": 4.473,
|
| 50 |
+
"ml": 4.433
|
| 51 |
+
}
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"alpha": 0.7,
|
| 55 |
+
"val_mcd_db": 5.575,
|
| 56 |
+
"val_f0_cents": 566.8,
|
| 57 |
+
"val_dur_mae_sec": 0.868,
|
| 58 |
+
"val_sa_cer_pct": 14.08,
|
| 59 |
+
"pareto_cost": 2.6884,
|
| 60 |
+
"per_lang_mcd": {
|
| 61 |
+
"sa": 7.953,
|
| 62 |
+
"hi": 4.077,
|
| 63 |
+
"kn": 4.302,
|
| 64 |
+
"ta": 4.008,
|
| 65 |
+
"te": 4.412,
|
| 66 |
+
"ml": 4.385
|
| 67 |
+
}
|
| 68 |
+
},
|
| 69 |
+
{
|
| 70 |
+
"alpha": 0.75,
|
| 71 |
+
"val_mcd_db": 5.518,
|
| 72 |
+
"val_f0_cents": 558.9,
|
| 73 |
+
"val_dur_mae_sec": 0.86,
|
| 74 |
+
"val_sa_cer_pct": 15.77,
|
| 75 |
+
"pareto_cost": 2.7819,
|
| 76 |
+
"per_lang_mcd": {
|
| 77 |
+
"sa": 7.93,
|
| 78 |
+
"hi": 4.013,
|
| 79 |
+
"kn": 4.161,
|
| 80 |
+
"ta": 3.901,
|
| 81 |
+
"te": 4.368,
|
| 82 |
+
"ml": 4.335
|
| 83 |
+
}
|
| 84 |
+
}
|
| 85 |
+
]
|
| 86 |
+
}
|