def test_constructor(self, asr_model):
asr_model.train()
# TODO: make proper config and assert correct number of weights
# Check to/from config_dict:
confdict = asr_model.to_config_dict()
instance2 = EncDecCTCModel.from_config_dict(confdict)
assert isinstance(instance2, EncDecCTCModel)
def __init__(
self,
encoder_decoder: EncDecCTCModel,
batch_size: int = 1,
dither: float = 0.0,
pad_to: int = 0,
device: str = "cuda",
**kwargs
) -> None:
cfg = encoder_decoder._cfg
OmegaConf.set_struct(cfg.preprocessor, value=False)
cfg.preprocessor.params.dither = dither
cfg.preprocessor.params.pad_to = pad_to
cfg.preprocessor.params.normalize = spectrogram_normalization()
OmegaConf.set_struct(cfg.preprocessor, value=True)
encoder_decoder.preprocessor = encoder_decoder.from_config_dict(
cfg.preprocessor
)
self.params = ASRInferenceParameters.from_omega(cfg, **kwargs)
self.overlap_timesteps = self.params.compute_overlap_timesteps()
self.buffer = np.zeros(self.params.buffer_size, dtype=np.float32)
sample_rate = self.params.sample_rate
self.audio_encoder_decoder = ASRAudioEncoderDecoder(
encoder_decoder, sample_rate, batch_size, device=device,
)
self.prev_char = ""
self.reset()
def test_from_config_dict_without_cls(self):
"""Here we test that instantiation works for configs without cls class path in them.
IMPORTANT: in this case, correct class type should call from_config_dict. This should work for Models."""
preprocessor = {
'cls':
'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor',
'params': dict({})
}
encoder = {
'cls': 'nemo.collections.asr.modules.ConvASREncoder',
'params': {
'feat_in':
64,
'activation':
'relu',
'conv_mask':
True,
'jasper': [{
'filters': 1024,
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
}],
},
}
decoder = {
'cls': 'nemo.collections.asr.modules.ConvASRDecoder',
'params': {
'feat_in':
1024,
'num_classes':
28,
'vocabulary': [
' ',
'a',
'b',
'c',
'd',
'e',
'f',
'g',
'h',
'i',
'j',
'k',
'l',
'm',
'n',
'o',
'p',
'q',
'r',
's',
't',
'u',
'v',
'w',
'x',
'y',
'z',
"'",
],
},
}
modelConfig = DictConfig({
'preprocessor': DictConfig(preprocessor),
'encoder': DictConfig(encoder),
'decoder': DictConfig(decoder)
})
obj = EncDecCTCModel.from_config_dict(config=modelConfig)
assert isinstance(obj, EncDecCTCModel)
