from malaya_speech.model.webrtc import WebRTC
from malaya_speech.supervised import classification
from malaya_speech.utils import describe_availability
from herpetologist import check_type
import warnings
# https://github.com/huseinzol05/malaya-speech/tree/master/pretrained-model/vad/evaluate
_availability = {
'vggvox-v2': {
'Size (MB)': 31.1,
'Quantized Size (MB)': 7.92,
'macro precision': 0.65847,
'macro recall': 0.82243,
'macro f1-score': 0.67149,
},
'marblenet-factor1': {
'Size (MB)': 0.526,
'Quantized Size (MB)': 0.232,
'macro precision': 0.64789,
'macro recall': 0.64168,
'macro f1-score': 0.64467,
},
'marblenet-factor3': {
'Size (MB)': 3.21,
'Quantized Size (MB)': 0.934,
'macro precision': 0.57310,
'macro recall': 0.61392,
'macro f1-score': 0.58050,
},
'marblenet-factor5': {
'Size (MB)': 8.38,
'Quantized Size (MB)': 2.21,
'macro precision': 0.54572,
'macro recall': 0.58929,
'macro f1-score': 0.53424,
}
}
_nemo_availability = {
'huseinzol05/nemo-vad-marblenet': {
'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-vad-multilingual-marblenet': {
'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,
},
}
silero_vad = {
'original from': 'https://github.com/snakers4/silero-vad',
'macro precision': 0.60044,
'macro recall': 0.72819,
'macro f1-score': 0.49225,
}
webrtc_vad = {
'notebook link': 'https://github.com/huseinzol05/malaya-speech/blob/master/pretrained-model/vad/evaluate/webrtc.ipynb',
'macro precision': 0.47163,
'macro recall': 0.46148,
'macro f1-score': 0.46461,
}
[docs]def available_model():
"""
List available VAD deep models.
"""
return describe_availability(_availability)
[docs]def available_nemo():
"""
List available Nvidia Nemo VAD models.
"""
return describe_availability(_nemo_availability)
[docs]@check_type
def webrtc(
aggressiveness: int = 3,
sample_rate: int = 16000,
minimum_amplitude: int = 100,
):
"""
Load WebRTC VAD model.
WebRTC prefer 30ms frame, https://github.com/wiseman/py-webrtcvad#how-to-use-it
Parameters
----------
aggressiveness: int, optional (default=3)
an integer between 0 and 3.
0 is the least aggressive about filtering out non-speech, 3 is the most aggressive.
sample_rate: int, optional (default=16000)
sample rate for samples.
minimum_amplitude: int, optional (default=100)
abs(minimum_amplitude) to assume a sample is a voice activity. Else, automatically False.
Returns
-------
result : malaya_speech.model.webrtc.WebRTC class
"""
try:
import webrtcvad
except BaseException:
raise ModuleNotFoundError(
'webrtcvad not installed. Please install it by `pip install webrtcvad` and try again.'
)
vad = webrtcvad.Vad(aggressiveness)
return WebRTC(vad, sample_rate, minimum_amplitude)
[docs]@check_type
def deep_model(model: str = 'marblenet-factor1', quantized: bool = False, **kwargs):
"""
Load VAD model.
Prefer 50 ms or bigger frame.
Parameters
----------
model : str, optional (default='vggvox-v2')
Check available models at `malaya_speech.vad.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
"""
warnings.warn(
'`malaya_speech.vad.deep_model` is using Tensorflow, malaya-speech no longer improved it after version 1.4.0',
DeprecationWarning)
model = model.lower()
if model not in _availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.vad.available_model()`.'
)
settings = {
'vggvox-v1': {'frame_len': 0.005, 'frame_step': 0.0005},
'vggvox-v2': {'hop_length': 24, 'concat': False, 'mode': 'eval'},
'speakernet': {'frame_ms': 20, 'stride_ms': 1.0},
'marblenet-factor1': {'feature_type': 'mfcc'},
'marblenet-factor3': {'feature_type': 'mfcc'},
'marblenet-factor5': {'feature_type': 'mfcc'},
}
return classification.load(
model=model,
module='vad',
extra=settings[model],
label=[False, True],
quantized=quantized,
**kwargs
)
[docs]@check_type
def nemo(
model: str = 'huseinzol05/nemo-vad-marblenet',
**kwargs,
):
"""
Load Nemo VAD model.
Nemo VAD prefer 63 ms frame, https://github.com/NVIDIA/NeMo/blob/02cf155b020964992a974e030b9e318426761e33/nemo/collections/asr/data/feature_to_label_dataset.py#L43
Parameters
----------
model : str, optional (default='huseinzol05/vad-marblenet')
Check available models at `malaya_speech.vad.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.vad.available_nemo()`.'
)
return classification.nemo_classification(
model=model,
label=[False, True],
**kwargs
)
