asteroid.masknn.recurrent module

class asteroid.masknn.recurrent.DPRNN(in_chan, n_src, out_chan=None, bn_chan=128, hid_size=128, chunk_size=100, hop_size=None, n_repeats=6, norm_type='gLN', mask_act='relu', bidirectional=True, rnn_type='LSTM', num_layers=1, dropout=0)

Bases: sphinx.ext.autodoc.importer._MockObject

Dual-path RNN Network for Single-Channel Source Separation

introduced in [1].

Parameters:

• in_chan (int) – Number of input filters.
• n_src (int) – Number of masks to estimate.
• out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.
• bn_chan (int) – Number of channels after the bottleneck. Defaults to 128.
• hid_size (int) – Number of neurons in the RNNs cell state. Defaults to 128.
• chunk_size (int) – window size of overlap and add processing. Defaults to 100.
• hop_size (int or None) – hop size (stride) of overlap and add processing. Default to chunk_size // 2 (50% overlap).
• n_repeats (int) – Number of repeats. Defaults to 6.
• norm_type (str, optional) –

  Type of normalization to use. To choose from

  • 'gLN': global Layernorm
  • 'cLN': channelwise Layernorm
• mask_act (str, optional) – Which non-linear function to generate mask.
• bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN (Intra-Chunk is always bidirectional).
• rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.
• num_layers (int, optional) – Number of layers in each RNN.
• dropout (float, optional) – Dropout ratio, must be in [0,1].

References

[1] “Dual-path RNN: efficient long sequence modeling for

time-domain single-channel speech separation”, Yi Luo, Zhuo Chen and Takuya Yoshioka. https://arxiv.org/abs/1910.06379

forward(mixture_w)

Parameters:mixture_w (torch.Tensor) – Tensor of shape [batch, n_filters, n_frames] Returns:

torch.Tensor

estimated mask of shape [batch, n_src, n_filters, n_frames]

get_config()

class asteroid.masknn.recurrent.DPRNNBlock(in_chan, hid_size, norm_type='gLN', bidirectional=True, rnn_type='LSTM', num_layers=1, dropout=0)

Bases: sphinx.ext.autodoc.importer._MockObject

Dual-Path RNN Block as proposed in [1].

Parameters:

• in_chan (int) – Number of input channels.
• hid_size (int) – Number of hidden neurons in the RNNs.
• norm_type (str, optional) – Type of normalization to use. To choose from - 'gLN': global Layernorm - 'cLN': channelwise Layernorm
• bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN.
• rnn_type (str, optional) – Type of RNN used. Choose from 'RNN', 'LSTM' and 'GRU'.
• num_layers (int, optional) – Number of layers used in each RNN.
• dropout (float, optional) – Dropout ratio. Must be in [0, 1].

References

[1] “Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation”, Yi Luo, Zhuo Chen and Takuya Yoshioka. https://arxiv.org/abs/1910.06379

forward(x)

Input shape : [batch, feats, chunk_size, num_chunks]

class asteroid.masknn.recurrent.LSTMMasker(in_chan, n_src, out_chan=None, rnn_type='lstm', n_layers=4, hid_size=512, dropout=0.3, mask_act='sigmoid', bidirectional=True)

Bases: sphinx.ext.autodoc.importer._MockObject

LSTM mask network introduced in [1], without skip connections.

Parameters:

• in_chan (int) – Number of input filters.
• n_src (int) – Number of masks to estimate.
• out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.
• rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.
• n_layers (int, optional) – Number of layers in each RNN.
• hid_size (int) – Number of neurons in the RNNs cell state.
• mask_act (str, optional) – Which non-linear function to generate mask.
• bidirectional (bool, optional) – Whether to use BiLSTM
• dropout (float, optional) – Dropout ratio, must be in [0,1].

References

[1]: Yi Luo et al. “Real-time Single-channel Dereverberation and Separation

with Time-domain Audio Separation Network”, Interspeech 2018

forward(x)

get_config()

class asteroid.masknn.recurrent.SingleRNN(rnn_type, input_size, hidden_size, n_layers=1, dropout=0, bidirectional=False)

Bases: sphinx.ext.autodoc.importer._MockObject

Module for a RNN block.

Inspired from https://github.com/yluo42/TAC/blob/master/utility/models.py Licensed under CC BY-NC-SA 3.0 US.

Parameters:

• rnn_type (str) – Select from 'RNN', 'LSTM', 'GRU'. Can also be passed in lowercase letters.
• input_size (int) – Dimension of the input feature. The input should have shape [batch, seq_len, input_size].
• hidden_size (int) – Dimension of the hidden state.
• n_layers (int, optional) – Number of layers used in RNN. Default is 1.
• dropout (float, optional) – Dropout ratio. Default is 0.
• bidirectional (bool, optional) – Whether the RNN layers are bidirectional. Default is False.

forward(inp)

Input shape [batch, seq, feats]

class asteroid.masknn.recurrent.StackedResidualBiRNN(rnn_type, n_units, n_layers=4, dropout=0.0, bidirectional=True)

Bases: sphinx.ext.autodoc.importer._MockObject

Stacked Bidirectional RNN with builtin residual connection. Residual connections are applied on both RNN directions. Only supports bidiriectional RNNs. See StackedResidualRNN for unidirectional ones.

Parameters:

• rnn_type (str) – Select from 'RNN', 'LSTM', 'GRU'. Can also be passed in lowercase letters.
• n_units (int) – Number of units in recurrent layers. This will also be the expected input size.
• n_layers (int) – Number of recurrent layers.
• dropout (float) – Dropout value, between 0. and 1. (Default: 0.)
• bidirectional (bool) – If True, use bidirectional RNN, else unidirectional. (Default: False)

forward(x)

Builtin residual connections + dropout applied before residual. Input shape : [batch, time_axis, feat_axis]

class asteroid.masknn.recurrent.StackedResidualRNN(rnn_type, n_units, n_layers=4, dropout=0.0, bidirectional=False)

Bases: sphinx.ext.autodoc.importer._MockObject

Stacked RNN with builtin residual connection. Only supports forward RNNs. See StackedResidualBiRNN for bidirectional ones.

Parameters:

• rnn_type (str) – Select from 'RNN', 'LSTM', 'GRU'. Can also be passed in lowercase letters.
• n_units (int) – Number of units in recurrent layers. This will also be the expected input size.
• n_layers (int) – Number of recurrent layers.
• dropout (float) – Dropout value, between 0. and 1. (Default: 0.)
• bidirectional (bool) – If True, use bidirectional RNN, else unidirectional. (Default: False)

forward(x)

Builtin residual connections + dropout applied before residual. Input shape : [batch, time_axis, feat_axis]