Automatic Speech Recognition
NeMo
PyTorch
speaker-diarization
speech-recognition
multitalker-ASR
multispeaker-ASR
speech
audio
FastConformer
RNNT
Conformer
NEST
NeMo
Eval Results (legacy)
Instructions to use nvidia/multitalker-parakeet-streaming-0.6b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/multitalker-parakeet-streaming-0.6b-v1 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/multitalker-parakeet-streaming-0.6b-v1") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Dynamic Context / Hot words Support for this model just like nvidia/parakeet-tdt-0.6b-v3
#4
by vedapani1 - opened
Based on the Source files, blogs and resources i looked into, nvidia/multitalker-parakeet-streaming-0.6b-v1 deos not support dynamic context/ hot words for better transcriptions. Is that True? if not, can anyone tell me how to pass on the context words / hot words for better trasncriptions.
If this is not supported, it would be great to add this feature in this model, which will make it the ultimate model for ASR with Speaker diarization even for overlapping audio.