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
config: drop redundant decoder_start_token_id (carried by generation_config)
Browse files- config.json +0 -1
config.json
CHANGED
|
@@ -4,7 +4,6 @@
|
|
| 4 |
],
|
| 5 |
"blank_token_id": 13087,
|
| 6 |
"decoder_hidden_size": 640,
|
| 7 |
-
"decoder_start_token_id": 13087,
|
| 8 |
"dtype": "float32",
|
| 9 |
"durations": [],
|
| 10 |
"encoder_config": {
|
|
|
|
| 4 |
],
|
| 5 |
"blank_token_id": 13087,
|
| 6 |
"decoder_hidden_size": 640,
|
|
|
|
| 7 |
"dtype": "float32",
|
| 8 |
"durations": [],
|
| 9 |
"encoder_config": {
|