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)
Instructions to use PhysShell/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 PhysShell/nemotron-3.5-asr-streaming-0.6b with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("PhysShell/nemotron-3.5-asr-streaming-0.6b") transcriptions = asr_model.transcribe(["file.wav"]) - Transformers
How to use PhysShell/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="PhysShell/nemotron-3.5-asr-streaming-0.6b")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("PhysShell/nemotron-3.5-asr-streaming-0.6b") model = AutoModel.from_pretrained("PhysShell/nemotron-3.5-asr-streaming-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,519 Bytes
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"blank_token": "<blank>",
"default_num_lookahead_tokens": 3,
"feature_extractor": {
"feature_extractor_type": "NemotronAsrStreamingFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"n_fft": 512,
"padding_side": "right",
"padding_value": 0.0,
"preemphasis": 0.97,
"return_attention_mask": true,
"sampling_rate": 16000,
"win_length": 400
},
"num_prompts": 128,
"processor_class": "Nemotron3_5AsrProcessor",
"prompt_dictionary": {
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"am-ET": 49,
"ar": 7,
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"fa-IR": 38,
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"fr": 8,
"fr-CA": 100,
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"it": 15,
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"lv": 61,
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"nah-MX": 83,
"nb": 103,
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"ne-NP": 46,
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"nn": 104,
"nn-NO": 104,
"no": 27,
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"ny-MW": 57,
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"pl": 17,
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"rw-RW": 55,
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"zu-ZA": 51
},
"supported_num_lookahead_tokens": [
3,
0,
6,
13
]
}
|