Hindi Tts 190M
Paper: arXiv:2607.18662 — Staged Depth-Pruning Distillation of a Flow-Matching Text-to-Speech Teacher: A Compact Hindi Speech Synthesizer
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 202 KB–725 MB
- Versão
- vRevision 72c9b4f
- Atualizado
- 19 de out. de 2026
- Downloads
- 272
- Fonte
- Hugging Face
Descrição da fonte
Hindi TTS — distilled IndicF5 student (~190M)
Paper: arXiv:2607.18662 — Staged Depth-Pruning Distillation of a Flow-Matching Text-to-Speech Teacher: A Compact Hindi Speech Synthesizer
A distilled IndicF5 "student" (DiT depth-12 flow-matching, ~190M params) for Hindi
text-to-speech, plus a Vocos mel→waveform vocoder (24 kHz). Voice-cloning: it speaks
in the bundled reference voice (ref_audio.wav + ref_text.txt).
Live demo: https://huggingface.co/spaces/5ivatej/hindi-tts-190M
Files
| File | Description |
|---|---|
model.pt | distilled d12 student weights (fp32, EMA) |
mel_basis.pt | fixed mel filterbank (matches training) |
vocab.txt | IndicF5 character vocab (2545 tokens) |
vocos/ | Vocos vocoder (config.yaml + pytorch_model.bin) |
ref_audio.wav, ref_text.txt | reference voice + its transcript |
Usage
The inference code lives in the demo Space repo. Point it at this model repo (default) and it downloads the weights automatically:
from infer_core import HindiTTS # from the Space repo
import soundfile as sf
tts = HindiTTS() # pulls weights from this repo on first run
sr, wave = tts.synth("नमस्ते, यह एक परीक्षण है।")
sf.write("out.wav", wave, sr)
License
Released under the MIT License. Note this model is derived from IndicF5 / F5-TTS — please also review and respect the upstream model and code licenses for your use case.
Benchmark
50 unique unseen sentences from the FLEURS hi_in test split, identical inputs for all models
(NFE 32, CFG 2.0, fixed seed for flow models). Full methodology, per-sentence scores, and scripts: eval/.
| Model | Params | WER ↓ | CER ↓ | Speaker SIM ↑ | UTMOS ↑ | RTF (median) ↓ |
|---|---|---|---|---|---|---|
| IndicF5 (teacher) | 337M | 0.098 | 0.030 | 0.784 | 3.79 | 5.56 (M4 MPS) |
| hindi-tts-190M (this model) | 190M | 0.170 | 0.077 | 0.750 | 3.65 | 3.13 (M4 MPS) |
| MMS-TTS-hin | 36M | 0.195 | 0.066 | 0.278* | 3.54 | 0.31 (CPU) |
* MMS-TTS is a fixed-voice model (no voice cloning); similarity to this model's reference speaker is shown for completeness.
- WER/CER: ASR judge
Harveenchadha/vakyansh-wav2vec2-hindi-him-4200(greedy CTC), punctuation + nukta normalized. This open judge has a higher intrinsic error floor than the gated IndicWav2Vec-Hindi used in the paper — compare rows to each other, not to the paper's WER table. - Speaker SIM: ECAPA-TDNN cosine similarity vs
ref_audio.wav. - UTMOS: predicted MOS (utmos22_strong).
- RTF: synthesis time ÷ audio duration, fp32, median over 50 sentences after warmup. On a CUDA GPU (paper setting) synthesis is real-time.
Takeaway: the 190M student retains ~96% of the 337M teacher's naturalness and speaker similarity at 56% of the parameters and 1.8× faster synthesis, and is the only compact Hindi model here with voice cloning.
Fontes
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Fontes
1 fonteVerificado 19 de out.
Artefatos de modelo
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Artefatos de modelo
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Trechos de fonte
3 trechos--- license: mit language: - hi pipeline_tag: text-to-speech tags: - text-to-speech - hindi - tts - f5-tts - indicf5 - flow-matching --- # Hindi TTS — distilled IndicF5 student (~190M) **Paper:** [arXiv:2607.18662](https://arxiv.org/abs/2607.18662) — *Staged Depth-Pruning Distillation of a Flow-Matching Text-to-Speech Teacher: A Compact Hindi Speech Synthesizer* A distilled IndicF5 "student" (DiT depth-12 flow-matching, ~190M params) for Hindi text-to-speech, plus a Vocos mel→waveform vocoder (24 kHz). Voice-cloning: it speaks in the bundled reference voice (`ref_audio.wav` + `ref_text.txt`). **Live demo:** https://huggingface.co/spaces/5ivatej/hindi-tts-190M ## Files | File | Description | |---|---| | `model.pt` | distilled d12 student weights (fp32, EMA) | | `mel_basis.pt` | fixed mel filterbank (matches training) | | `vocab.txt` | IndicF5 character vocab (2545 tokens) | | `vocos/` | Vocos vocoder (`config.yaml` + `pytorch_model.bin`) | | `ref_audio.wav`, `ref_text.txt` | reference voice + its transcript | ## Usage The inference code lives in the demo Space repo. Point it at this model repo (default) and it downloads the weights automatically: ```python from infer_core import HindiTTS # from the Space repo import soundfile as sf tts = HindiTTS() # pulls weights from this repo on first run sr, wave = tts.synth("नमस्ते, यह एक परीक्षण है।") sf.write("out.wav", wave, sr) ``` ## License Released under the **MIT License**. Note this model is derived from IndicF5 / F5-TTS — please also review and respect the upstream model and code licenses for your use case. ## Benchmark 50 unique unseen sentences from the FLEURS `hi_in` test split, identical inputs for all models (NFE 32, CFG 2.0, fixed seed for flow models). Full methodology, per-sentence scores, and scripts: [`eval/`](./tree/main/eval). | Model | Params | WER ↓ | CER ↓ | Speaker SIM ↑ | UTMOS ↑ | RTF (median) ↓ | |---|---|---|---|---|---|---| | IndicF5 (teacher) | 337M | 0.098 | 0.030 | 0.784 | 3.79 | 5.56 (M4 MPS) | | **hindi-tts-190M (this model)** | **190M** | **0.170** | **0.077** | **0.750** | **3.65** | **3.13 (M4 MPS)** | | MMS-TTS-hin | 36M | 0.195 | 0.066 | 0.278* | 3.54 | 0.31 (CPU) | \* MMS-TTS is a fixed-voice model (no voice cloning); similarity to this model's reference speaker is shown for completeness. - **WER/CER**: ASR judge `Harveenchadha/vakyansh-wav2vec2-hindi-him-4200` (g...
Source context: 272 downloads · 1 likes · Pipeline text-to-speech · Library f5-tts · Repo 5ivatej/hindi-tts-190M
Source context: 129 downloads · 1 likes · Pipeline text-to-speech · Library f5-tts · Repo 5ivatej/hindi-tts-190M
