from malaya_speech.supervised import classification
from malaya_speech.utils import describe_availability
import logging
logger = logging.getLogger(__name__)
_nemo_availability = {
'huseinzol05/nemo-speaker-count-speakernet': {
'original from': 'https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_marblenet',
'Size (MB)': 16.2,
},
'huseinzol05/nemo-speaker-count-titanet_large': {
'original from': 'https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet',
'Size (MB)': 88.8,
},
}
labels = [
'0 speaker',
'1 speaker',
'2 speakers',
'3 speakers',
'4 speakers',
'5 speakers',
'more than 5 speakers',
]
[docs]def available_nemo():
"""
List available Nvidia Nemo speaker count models.
"""
return describe_availability(_nemo_availability)
[docs]def nemo(
model: str = 'huseinzol05/nemo-speaker-count-speakernet',
**kwargs,
):
"""
Load Nvidia Nemo speaker count model.
Trained on 300 ms frames.
Parameters
----------
model : str, optional (default='huseinzol05/nemo-speaker-count-speakernet')
Check available models at `malaya_speech.speaker_count.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.speaker_count.available_nemo()`.'
)
return classification.nemo_classification(
model=model,
label=labels,
**kwargs
)
