Instructions to use smajji/nemotron-hinglish-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use smajji/nemotron-hinglish-v3 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("smajji/nemotron-hinglish-v3") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Nemotron Hinglish v3
Nemotron-Hinglish-v3 is a fine-tune of nvidia/nemotron-3.5-asr-streaming-0.6b specialized for English, Hindi and Hinglish (Hindi-English code-switching), built for cache-aware streaming ASR, with improved number / phone-number / digit accuracy.
It preserves the base model's FastConformer-Transducer (RNNT) cache-aware streaming architecture (24 layers, 1024 hidden, 600M params) and its multi-lingual 13088 BPE vocab + 128 language prompts, including the auto language-detection prompt.
Model Details
- Base model:
nvidia/nemotron-3.5-asr-streaming-0.6b - Architecture: FastConformer-Transducer (RNNT), cache-aware streaming, 8x subsampling
- Parameters: ~600M
- Sampling rate: 16 kHz mono
- Target languages: English (
en), Hindi (hi), Hinglish code-switched (autoprompt for code-mixing) - Streaming: cache-aware, chunk sizes 80/160/320/560/1120 ms
Training Data
Fine-tuned on a large bilingual + code-mixed mix with number/symbol-dense corpora added for digit & phone-number accuracy:
| Slice | Language | Role |
|---|---|---|
| SPGISpeech | en | clean English |
| IISc_SPICOR | en | Indian-accent English |
| Peoples Speech | en | large English |
| SPRING Hindi-1482Hrs + Shrutilipi | hi | Hindi |
| IndicVoices-R Hindi | hi | Hindi |
| UJS + Hinglish-CC | hinglish | code-mixed |
| FLEURS (en + hi) | en/hi | ~21% digit-dense |
| earnings22 | en | ~22% digit, ~7.7% symbol — phone/number rich |
Trained with auto language self-detection (0.9 ratio) for code-switching, punctuation/casing preserved.
Benchmark
Greedy decode, punctuation-insensitive WER (held-out sample, auto prompt). Compared to previous releases:
| Language | Nemotron-Hinglish-v3 | v2 | v1 |
|---|---|---|---|
| English | 4.0% | 5.1% | 4.2% |
| Hindi | 12.2% | 13.4% | 12.4% |
| Hinglish | 29.2% | 30.0% | 22.6% |
- Digit / phone-number accuracy improved ~32% (fewer digit-sequence errors than v2), from added number-dense corpora.
Usage (NeMo)
import nemo.collections.asr as nemo_asr
model = nemo_asr.models.ASRModel.restore_from("nvidia/nemotron-hinglish-v3")
model.eval()
transcriptions = model.transcribe(["audio.wav"], batch_size=4)
print(transcriptions)
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Base model
nvidia/nemotron-3.5-asr-streaming-0.6b