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asteroid.models.demask module

class asteroid.models.demask.DeMask(input_type='mag', output_type='mag', hidden_dims=[1024], dropout=0, activation='relu', mask_act='relu', norm_type='gLN', fb_type='stft', n_filters=512, stride=256, kernel_size=512, **fb_kwargs)[source]

Bases: asteroid.models.base_models.BaseModel

Simple MLP model for surgical mask speech enhancement A transformed-domain masking approach is used. :param input_type: whether the magnitude spectrogram “mag” or both real imaginary parts “reim” are

passed as features to the masker network. Concatenation of “mag” and “reim” also can be used by using “cat”.
Parameters:
  • output_type (str, optional) – whether the masker ouputs a mask for magnitude spectrogram “mag” or both real imaginary parts “reim”.
  • hidden_dims (list, optional) – list of MLP hidden layer sizes.
  • dropout (float, optional) – dropout probability.
  • activation (str, optional) – type of activation used in hidden MLP layers.
  • mask_act (str, optional) – Which non-linear function to generate mask.
  • norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.
  • fb_name (str) – type of analysis and synthesis filterbanks used, choose between [“stft”, “free”, “analytic_free”].
  • n_filters (int) – number of filters in the analysis and synthesis filterbanks.
  • stride (int) – filterbank filters stride.
  • kernel_size (int) – length of filters in the filterbank.
  • encoder_activation (str) –
  • **fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.
forward(wav)[source]
get_model_args()[source]

Arguments needed to re-instantiate the model.

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