Instructions to use nvidia/NVIDIA-Nemotron-3.5-ASR-Streaming-Multilingual-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/NVIDIA-Nemotron-3.5-ASR-Streaming-Multilingual-0.6b with NeMo:
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- Notebooks
- Google Colab
- Kaggle
Can i export onnx models and use it in sherpa-onnx
Hi, thanks for making this model public.
Since access to it is currently restricted, would it be okay if I export it to sherpa-onnx and share the ONNX version of this repository for others to use?
Yeah, I also wonder... Since access is currently restricted, I was waiting for this to be fully public... π
Based on my tests, it works really nice by the way and it's really fast even on CPU! Ty to make this model as public.
@csukuangfj , @altunenes - The model is still WIP, mainly WER improvements for some of the languages. It will be released very soon with slightly better quality.
@Amargolin curious about languages support, is there a list or something or not yet ?
its interesting because model card officially certifies 36 languages. But if I inspect the model's internal prompt_dictionary actually has slots for more I mean like 100+ (including e.g. Quechua, Maori, Hawaiian langs too)...
it's also worth noting that the model seems quite sensitive to accents. I tested a real world Swedish recordings (couple of, not that much) last night with two speakers both speaking Swedish, but one is a native German speaker with a clear German accent (naturally π ). With auto detection, the model occasionally transcribed his Swedish as German instead. Not sure if that's a feature (the LID network honestly catching the non-native pronunciation) or a limitation... If I gave the language hint beforehand, of course there would be no problem. It might be a very rare occurrence of course I haven't tested that much with that...