def __init__(self,
name: str = 'quartznet_15x5',
encoder_cfg_func: Optional[Callable[[], List[Any]]] = None):
if name not in EncDecCTCModelConfigBuilder.VALID_CONFIGS:
raise ValueError("`name` must be one of : \n"
f"{EncDecCTCModelConfigBuilder.VALID_CONFIGS}")
self.name = name
if 'quartznet_15x5' in name:
if encoder_cfg_func is None:
encoder_cfg_func = qn_15x5
model_cfg = QuartzNetModelConfig(
repeat=5,
separable=True,
spec_augment=SpectrogramAugmentationConfig(rect_masks=5,
rect_freq=50,
rect_time=120),
encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(),
activation="relu"),
decoder=ConvASRDecoderConfig(),
)
elif 'jasper_10x5' in name:
if encoder_cfg_func is None:
encoder_cfg_func = jasper_10x5_dr
model_cfg = JasperModelConfig(
repeat=5,
separable=False,
spec_augment=SpectrogramAugmentationConfig(rect_masks=5,
rect_freq=50,
rect_time=120),
encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(),
activation="relu"),
decoder=ConvASRDecoderConfig(),
)
else:
raise ValueError(
f"Invalid config name submitted to {self.__class__.__name__}")
super(EncDecCTCModelConfigBuilder, self).__init__(model_cfg)
self.model_cfg: ctc_cfg.EncDecCTCConfig = model_cfg # enable type hinting
if 'zh' in name:
self.set_dataset_normalize(normalize=False)
class JasperModelConfig(ctc_cfg.EncDecCTCConfig):
# Model global arguments
sample_rate: int = 16000
repeat: int = 1
dropout: float = 0.0
separable: bool = False
labels: List[str] = MISSING
# Dataset configs
train_ds: ctc_cfg.ASRDatasetConfig = ctc_cfg.ASRDatasetConfig(
manifest_filepath=None, shuffle=True, trim_silence=True)
validation_ds: ctc_cfg.ASRDatasetConfig = ctc_cfg.ASRDatasetConfig(
manifest_filepath=None, shuffle=False)
test_ds: ctc_cfg.ASRDatasetConfig = ctc_cfg.ASRDatasetConfig(
manifest_filepath=None, shuffle=False)
# Optimizer / Scheduler config
optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(
sched=model_cfg.SchedConfig())
# Model general component configs
preprocessor: AudioToMelSpectrogramPreprocessorConfig = AudioToMelSpectrogramPreprocessorConfig(
)
spec_augment: Optional[
SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig()
encoder: ConvASREncoderConfig = ConvASREncoderConfig(activation="relu")
decoder: ConvASRDecoderConfig = ConvASRDecoderConfig()
class EncDecClassificationConfig(model_cfg.ModelConfig):
# Model global arguments
sample_rate: int = 16000
repeat: int = 1
dropout: float = 0.0
separable: bool = True
kernel_size_factor: float = 1.0
labels: List[str] = MISSING
timesteps: int = MISSING
# Dataset configs
train_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=True, trim_silence=False)
validation_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=False)
test_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=False)
# Optimizer / Scheduler config
optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(
sched=model_cfg.SchedConfig())
# Model component configs
preprocessor: AudioToMFCCPreprocessorConfig = AudioToMFCCPreprocessorConfig(
)
spec_augment: Optional[
SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig()
crop_or_pad_augment: Optional[
CropOrPadSpectrogramAugmentationConfig] = CropOrPadSpectrogramAugmentationConfig(
audio_length=timesteps)
encoder: ConvASREncoderConfig = ConvASREncoderConfig()
decoder: ConvASRDecoderClassificationConfig = ConvASRDecoderClassificationConfig(
)
def __init__(self,
name: str = 'matchboxnet_3x1x64',
encoder_cfg_func: Optional[Callable[[], List[Any]]] = None):
if name not in EncDecClassificationModelConfigBuilder.VALID_CONFIGS:
raise ValueError(
"`name` must be one of : \n"
f"{EncDecClassificationModelConfigBuilder.VALID_CONFIGS}")
self.name = name
if 'matchboxnet_3x1x64_vad' in name:
if encoder_cfg_func is None:
encoder_cfg_func = matchboxnet_3x1x64_vad
model_cfg = MatchboxNetVADModelConfig(
repeat=1,
separable=True,
encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(),
activation="relu"),
decoder=ConvASRDecoderClassificationConfig(),
)
elif 'matchboxnet_3x1x64' in name:
if encoder_cfg_func is None:
encoder_cfg_func = matchboxnet_3x1x64
model_cfg = MatchboxNetModelConfig(
repeat=1,
separable=False,
spec_augment=SpectrogramAugmentationConfig(rect_masks=5,
rect_freq=50,
rect_time=120),
encoder=ConvASREncoderConfig(jasper=encoder_cfg_func(),
activation="relu"),
decoder=ConvASRDecoderClassificationConfig(),
)
else:
raise ValueError(
f"Invalid config name submitted to {self.__class__.__name__}")
super(EncDecClassificationModelConfigBuilder, self).__init__(model_cfg)
self.model_cfg: clf_cfg.EncDecClassificationConfig = model_cfg # enable type hinting
class MatchboxNetModelConfig(clf_cfg.EncDecClassificationConfig):
# Model global arguments
sample_rate: int = 16000
repeat: int = 1
dropout: float = 0.0
separable: bool = True
kernel_size_factor: float = 1.0
timesteps: int = 128
labels: List[str] = MISSING
# Dataset configs
train_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=True, trim_silence=False)
validation_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=False)
test_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
manifest_filepath=None, shuffle=False)
# Optimizer / Scheduler config
optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(
sched=model_cfg.SchedConfig())
# Model general component configs
preprocessor: AudioToMFCCPreprocessorConfig = AudioToMFCCPreprocessorConfig(
window_size=0.025)
spec_augment: Optional[
SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig(
freq_masks=2,
time_masks=2,
freq_width=15,
time_width=25,
rect_masks=5,
rect_time=25,
rect_freq=15)
crop_or_pad_augment: Optional[
CropOrPadSpectrogramAugmentationConfig] = CropOrPadSpectrogramAugmentationConfig(
audio_length=128)
encoder: ConvASREncoderConfig = ConvASREncoderConfig(activation="relu")
decoder: ConvASRDecoderClassificationConfig = ConvASRDecoderClassificationConfig(
)
