Automatic Speech Recognition
NeMo
Safetensors
Transformers
PyTorch
nemotron3_5_asr
feature-extraction
speech-recognition
cache-aware ASR
streaming-asr
multilingual
speech
audio
FastConformer
RNNT
Parakeet
ASR
NeMo
Eval Results (legacy)
Eval Results
Instructions to use nvidia/nemotron-3.5-asr-streaming-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/nemotron-3.5-asr-streaming-0.6b with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/nemotron-3.5-asr-streaming-0.6b") transcriptions = asr_model.transcribe(["file.wav"]) - Transformers
How to use nvidia/nemotron-3.5-asr-streaming-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nvidia/nemotron-3.5-asr-streaming-0.6b")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("nvidia/nemotron-3.5-asr-streaming-0.6b") model = AutoModel.from_pretrained("nvidia/nemotron-3.5-asr-streaming-0.6b", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 9da0a2450146811be6d0e272622eafd42e6ff2d46ca32ba825a289d3ab5b09fd
- Size of remote file:
- 65.5 kB
- SHA256:
- 9a5474f81b64e0ce844c5907f073f7fab56a353d21db9196cd11aba22f787060
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