5ivatej/hindi-tts-190M

Hindi TTS — distilled IndicF5 student (~190M)

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.