tryorato/orato-asr-hindi-v1

Orato ASR — Hindi v1

Full-parameter supervised fine-tune of Qwen/Qwen3-ASR-0.6B (audio tower frozen) on ~1,000 hours of Hindi/English/Hinglish calling-domain audio (appointment booking, support, insurance, sales).

Training

• Method: full SFT, decoder + projector updated, audio encoder frozen
• Params: ~0.8B total, ~0.6B trained (audio tower frozen)
• Data: ~1,000 hours, calling-domain Hindi/Hinglish
• LR 1e-5, cosine schedule, warmup 0.03, max_grad_norm 1.0, 1 epoch, 1x H100

Evaluation (base vs. fine-tuned, same held-out sets)

Benchmark	Base WER	Fine-tuned WER	Relative improvement
Kathbath	15.24%	11.49%	24.6%
Gramvaani	39.07%	37.66%	3.6%
Lahaja	25.09%	18.68%	25.5%
Fleurs	19.12%	16.58%	13.3%

Fine-tuning improved WER on every benchmark tested, most on the benchmarks closest to the real product domain (telephony, accents, conversational speech).

Comparison with the Hindi ASR market

System	FLEURS	CommonVoice	IndicTTS	Kathbath	Kathbath-Hard	Gramvaani	Lahaja
Orato ASR	16.98	20.63	13.23	11.49	13.21	37.66	18.68
Azure STT	24.30	14.60	—	13.60	15.10	42.30	28.60
Deepgram	17.14	20.04	13.65	—	—	—	—
ElevenLabs Scribe	11.99	16.59	16.06	—	—	—	—
Google Chirp	—	—	—	—	—	—	22.30
Google STT	19.40	20.80	18.30	14.30	16.70	59.90	—
IndicASR M1 (Conformer-L)	—	—	—	—	—	—	19.40
IndicWav2Vec	—	—	—	—	16.20	42.10	—
IndicWhisper	11.40	15.00	7.60	10.30	12.00	26.80	—
MMS (300M)	—	—	—	—	—	—	34.40
Nvidia Conformer-M	—	—	—	14.00	15.60	41.30	—
Sarvam Saarika	16.00	18.21	15.37	—	—	—	—
Whisper large-v3 (1.55B)	—	—	—	—	—	—	32.40

Sources: AI4Bharat Vistaar benchmark suite (IndicWhisper, Nvidia Conformer-M, IndicWav2Vec, Google STT, Azure STT on FLEURS/CommonVoice/IndicTTS/Kathbath/ Kathbath-Hard/Gramvaani); AI4Bharat Lahaja benchmark; third-party published WER for Sarvam Saarika/Deepgram/ElevenLabs Scribe on FLEURS/CommonVoice/IndicTTS. Normalization may differ slightly across sources — treat as directional.

Loading

import qwen_asr, torch
wrapper = qwen_asr.Qwen3ASRModel.from_pretrained(
    "tryorato/orato-asr-hindi-v1", dtype=torch.bfloat16, device_map=None,
    attn_implementation="sdpa",
)
wrapper.model = wrapper.model.to("cuda")
result = wrapper.transcribe(audio=(wav, 16000), language="Hindi")

Must load via qwen_asr.Qwen3ASRModel, not transformers.AutoModel — this is the non--hf wrapper checkpoint; loading it as a plain Transformers model will silently misbehave.

Intended use / limitations

Beta. Evaluated on Hindi calling-domain audio; not evaluated outside that domain. Access is currently private.

Data rights

Training data includes a mix of public research datasets — Rasa Hindi, Gram Vaani, MUCS — used under their respective published licenses, plus proprietary Hindi/Hinglish calling-domain audio collected by Orato.

• No raw audio, transcripts, or personally identifiable data are included in this repository — only model weights are published.
• Commercial-use licensing confirmation for the public source datasets, and consent/PII review for the proprietary calling data, are in progress. Released for internal/beta evaluation only until that review completes.
• This repository does not convey redistribution rights to any underlying training data.