Automatic Speech Recognition
NeMo
PyTorch
speech-recognition
unified-asr
offline-asr
streaming-asr
speech
audio
FastConformer
RNNT
Parakeet
ASR
NeMo
Eval Results (legacy)
Instructions to use nvidia/parakeet-unified-en-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/parakeet-unified-en-0.6b with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-unified-en-0.6b") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Great model but missing cache-aware feature
#6
by janvliet - opened
This is a great model making streaming and offline with same model a reality.
Currently it is missing the cache-aware feature.
This results in streaming with small chunks having high overhead as needs recomputing the whole left context.
Would be nice this gets added in an updated model, making streaming with this model more practical.
Hi @janvliet , thank you for the feedback!
The cache-aware decoding can be added later in 2-3 months. We have a multilingual priority now.
BTW, what latency are you targeting for streaming inference?
Latency 300ms or less is usually preferred.