from malaya_speech.supervised import classification
from herpetologist import check_type
from malaya_speech.utils import describe_availability
import logging
logger = logging.getLogger(__name__)
# EER calculation, https://github.com/huseinzol05/malaya-speech/tree/master/pretrained-model/speaker-embedding/calculate-EER
# EER tested on VoxCeleb2 test set.
_availability = {
'deep-speaker': {
'Size (MB)': 96.7,
'Quantized Size (MB)': 24.4,
'Embedding Size': 512,
'EER': 0.2187,
},
'vggvox-v1': {
'Size (MB)': 70.8,
'Quantized Size (MB)': 17.7,
'Embedding Size': 1024,
'EER': 0.13944,
},
'vggvox-v2': {
'Size (MB)': 43.2,
'Quantized Size (MB)': 7.92,
'Embedding Size': 512,
'EER': 0.0446,
},
'speakernet': {
'Size (MB)': 35,
'Quantized Size (MB)': 8.88,
'Embedding Size': 7205,
'EER': 0.3000285,
},
'conformer-base': {
'Size (MB)': 99.4,
'Quantized Size (MB)': 27.2,
'Embedding Size': 512,
'EER': 0.06938,
},
'conformer-tiny': {
'Size (MB)': 20.3,
'Quantized Size (MB)': 6.21,
'Embedding Size': 512,
'EER': 0.08687,
},
}
trillsson_accuracy = {
'trillsson-1': {
'url': 'https://tfhub.dev/google/nonsemantic-speech-benchmark/trillsson1/1',
'EER': 0.3804599,
},
'trillsson-2': {
'url': 'https://tfhub.dev/google/nonsemantic-speech-benchmark/trillsson2/1',
'EER': 0.3898799,
}
}
[docs]def available_model():
"""
List available speaker vector deep models.
"""
logger.info('tested on VoxCeleb2 test set. Lower EER is better.')
return describe_availability(_availability)
[docs]@check_type
def deep_model(model: str = 'vggvox-v2', quantized: bool = False, **kwargs):
"""
Load Speaker2Vec model.
Parameters
----------
model : str, optional (default='speakernet')
Check available models at `malaya_speech.speaker_vector.available_model()`.
quantized : bool, optional (default=False)
if True, will load 8-bit quantized model.
Quantized model not necessary faster, totally depends on the machine.
Returns
-------
result : malaya_speech.supervised.classification.load function
"""
model = model.lower()
if model not in _availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.speaker_vector.available_model()`.'
)
return classification.load(
model=model,
module='speaker-vector',
extra={},
label=None,
quantized=quantized,
**kwargs
)
