def deep_model(model: str = 'speakernet', quantized: bool = False, **kwargs): """ Load Speaker2Vec model. Parameters ---------- model : str, optional (default='speakernet') Model architecture supported. Allowed values: * ``'vggvox-v1'`` - VGGVox V1, embedding size 1024, exported from https://github.com/linhdvu14/vggvox-speaker-identification * ``'vggvox-v2'`` - VGGVox V2, embedding size 512, exported from https://github.com/WeidiXie/VGG-Speaker-Recognition * ``'deep-speaker'`` - Deep Speaker, embedding size 512, exported from https://github.com/philipperemy/deep-speaker * ``'speakernet'`` - SpeakerNet, embedding size 7205, exported from https://github.com/NVIDIA/NeMo/tree/main/examples/speaker_recognition 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 Exception( 'model not supported, please check supported models from `malaya_speech.speaker_vector.available_model()`.' ) return classification.load(model=model, module='speaker-vector', extra={}, label={}, quantized=quantized, **kwargs)
def deep_model(model: str = 'vggvox-v2', quantized: bool = False, **kwargs): """ Load VAD model. Parameters ---------- model : str, optional (default='vggvox-v2') Model architecture supported. Allowed values: * ``'vggvox-v1'`` - finetuned VGGVox V1. * ``'vggvox-v2'`` - finetuned VGGVox V2. 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 Exception( '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 }, } return classification.load(model=model, module='vad', extra=settings[model], label=[False, True], quantized=quantized, **kwargs)
def deep_model(model: str = 'speakernet', quantized: bool = False, **kwargs): """ Load speaker change deep model. Parameters ---------- model : str, optional (default='vggvox-v2') Model architecture supported. Allowed values: * ``'vggvox-v2'`` - finetuned VGGVox V2. * ``'speakernet'`` - finetuned SpeakerNet. 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 Exception( 'model not supported, please check supported models from `malaya_speech.speaker_change.available_model()`.' ) settings = { 'vggvox-v2': { 'hop_length': 50, 'concat': False, 'mode': 'eval' }, 'speakernet': { 'frame_ms': 20, 'stride_ms': 2 }, } return classification.load(path=PATH_SPEAKER_CHANGE, s3_path=S3_PATH_SPEAKER_CHANGE, model=model, name='speaker-change', extra=settings[model], label=[False, True], quantized=quantized, **kwargs)
def deep_model(model: str = 'vggvox-v2', quantized: bool = False, **kwargs): """ Load age detection deep model. Parameters ---------- model : str, optional (default='vggvox-v2') Model architecture supported. Allowed values: * ``'vggvox-v2'`` - finetuned VGGVox V2. * ``'deep-speaker'`` - finetuned Deep Speaker. 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 Exception( 'model not supported, please check supported models from `malaya_speech.age_detection.available_model()`.' ) settings = { 'vggvox-v2': { 'concat': False }, 'deep-speaker': { 'voice_only': False }, } return classification.load(path=PATH_AGE_DETECTION, s3_path=S3_PATH_AGE_DETECTION, model=model, name='age-detection', extra=settings[model], label=labels, quantized=quantized, **kwargs)