from malaya_speech.supervised import classification
from malaya_speech.utils import describe_availability
import logging
logger = logging.getLogger(__name__)
_nemo_availability = {
'huseinzol05/nemo-is-clean-speakernet': {
'original from': 'https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_marblenet',
'Size (MB)': 0.481,
'macro precision': 0.60044,
'macro recall': 0.72819,
'macro f1-score': 0.49225,
},
'huseinzol05/nemo-is-clean-titanet_large': {
'original from': 'https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet',
'Size (MB)': 0.481,
'macro precision': 0.47633,
'macro recall': 0.49895,
'macro f1-score': 0.46915,
},
}
[docs]def available_nemo():
"""
List available Nvidia Nemo is clean models.
"""
return describe_availability(_nemo_availability)
[docs]def nemo(
model: str = 'huseinzol05/nemo-is-clean-speakernet',
**kwargs,
):
"""
Load Nvidia Nemo is clean model.
Trained on 100, 200, 300 ms frames.
Parameters
----------
model : str, optional (default='huseinzol05/nemo-is-clean-speakernet')
Check available models at `malaya_speech.is_clean.available_nemo()`.
Returns
-------
result : malaya_speech.torch_model.nemo.Classification class
"""
if model not in _nemo_availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.is_clean.available_nemo()`.'
)
return classification.nemo_classification(
model=model,
label=[False, True],
**kwargs
)
