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
parakeet-unified vs nemotron-speech-streaming?
#7
by xdevfaheem - opened
how are they different? I'm only focusing english model, which should i go with?
Hi @xdevfaheem , sorry for the delayed answer.
The main differences are:
- The Unified model works well in offline and streaming decoding.
- The Unified model outperforms Nemotron down to 240ms latency (according to the Open ASR leaderboard). But if you need 160ms or 80ms, it is better to use the Nemotron model.
- The Unified model does not support cache-aware decoding yet (only chunked inference with left context recomputation). You can observe 100-300% RTFx degradation at low latencies for streaming decoding compared to Nemotron.
What latency are you targeting for English?