Instructions to use smajji/nemotron-hinglish-v12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use smajji/nemotron-hinglish-v12 with NeMo:
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- Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Nemotron-Hinglish-v12 (Mevine ASR — En + Hi + Hinglish)
A bilingual Hindi/English Hinglish streaming ASR model, fine-tuned from
nvidia/nemotron-3.5-asr-streaming-0.6b (cache-aware FastConformer-RNNT, 0.6B).
This is the flagship (12th) iteration. It is warm-started from the best v11 checkpoint and tuned for strong code-mixed Hindi-English performance while keeping clean English / Hindi strong.
Full benchmark report (all Mevine versions v1→v12 across 12 datasets): see
BENCHMARKS.md.
Training data (2,905,937 utts / 6,451 h)
| Corpus / dataset | utts | hours | lang |
|---|---|---|---|
| Hinglish code-mixed (hinglish_cc) | 802,866 | 1832.2 | hinglish |
| Shrutilipi (Hindi) | 428,446 | 849.5 | hi |
| Indian-English NPTEL | 348,217 | 744.7 | en |
| Hindi-1482hrs | 253,373 | 400.0 | hi |
| People's Speech | 213,435 | 800.6 | en |
| TEDLIUM | 119,216 | 259.9 | en |
| SPGISpeech | 117,927 | 300.0 | en |
| IndicTTS-English | 113,702 | 172.8 | en |
| India-Accent Common Voice | 91,510 | 144.5 | en |
| OpenSLR104 (code-mixed, NEW) | 52,825 | 89.6 | hinglish |
| Hinglish UJS | 52,812 | 89.4 | hinglish |
| OpenSLR Hinglish | 52,812 | 89.4 | hinglish |
| MUCS Hinglish | 52,809 | 97.4 | hinglish |
| earnings22 | 51,578 | 103.2 | en |
| IISc SPICOR | 49,186 | 96.9 | en |
| Hinglish Casual | 33,275 | 100.7 | hinglish |
| NPTEL Tech English | 27,259 | 50.5 | en |
| Hindi Audio Stories | 25,314 | 188.5 | hi |
| ASR Task EN | 8,000 | 13.9 | en |
| Roopa code-mixed numbers | 4,022 | 6.6 | hinglish |
| IndicVoices-R Hindi | 2,632 | 7.1 | hi |
| FLEURS (en_us / hi_in) | 2,601 / 2,120 | 7.5 / 6.7 | en/hi |
- Devanagari digits + spelled numbers normalized to ASCII digits for cleaner, consistent targets.
- Code-mixed share ~36% (raised without cutting English), plus fresh OpenSLR104 code-mixed data.
- tarred audio (16 kHz FLAC), Lhotse dynamic bucketing,
batch_duration=300,lr 8e-5(Cosine→1e-6, 5% warmup),max_steps=30000.
Evaluation (authoritative 8-type battery, 300/type, median WER)
| type | v10 | v11 | v12 |
|---|---|---|---|
| code-mixed numbers (roopa) | 12.9% | 11.1% | 10.5% |
| code-mixed OpenSLR104 | 39.4% | 36.9% | 33.3% |
| en_clean | 3.9% | 4.3% | 4.5% |
| en_indian | 14.0% | 15.4% | 15.5% |
| en_tech | 15.4% | 14.3% | 15.4% |
| hi_read | 12.0% | 12.5% | 12.1% |
| hi_unseen | 7.1% | 7.1% | 7.1% |
| hinglish (mucs) | 44.4% | 44.4% | 42.9% |
| OVERALL | 12.5% | 12.5% | 12.5% |
v12 = best code-mixed performance to date (roopa 10.5%, OpenSLR104 33.3%, hinglish 42.9%) while holding overall at 12.5%.
Usage
Same streaming cache-aware interface as the base Nemotron-3.5-ASR model (prompt
conditioning + language-ID, chunk sizes 80/160/320/560/1120 ms, default_prompt_mode
unified, unified_auto_ratio 0.9).
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