Automatic Speech Recognition
NeMo
Safetensors
Transformers
Persian
nemotron3_5_asr
feature-extraction
streaming-asr
cache-aware ASR
FastConformer
RNNT
NeMo
persian
farsi
Eval Results (legacy)
Instructions to use mehdi-hf/nemotron-asr-streaming-farsi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use mehdi-hf/nemotron-asr-streaming-farsi with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("mehdi-hf/nemotron-asr-streaming-farsi") transcriptions = asr_model.transcribe(["file.wav"]) - Transformers
How to use mehdi-hf/nemotron-asr-streaming-farsi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mehdi-hf/nemotron-asr-streaming-farsi")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mehdi-hf/nemotron-asr-streaming-farsi") model = AutoModel.from_pretrained("mehdi-hf/nemotron-asr-streaming-farsi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 616 Bytes
5201feb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"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": {
"auto": 38,
"fa": 38,
"fa-IR": 38
},
"supported_num_lookahead_tokens": [
3,
0,
6,
13
]
}
|