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asteroid.masknn package

class asteroid.masknn.TDConvNet(in_chan, n_src, out_chan=None, n_blocks=8, n_repeats=3, bn_chan=128, hid_chan=512, skip_chan=128, conv_kernel_size=3, norm_type='gLN', mask_act='relu', kernel_size=None)[source]

Bases: sphinx.ext.autodoc.importer._MockObject

Temporal Convolutional network used in ConvTasnet.

Parameters:
  • in_chan (int) – Number of input filters.
  • n_src (int) – Number of masks to estimate.
  • out_chan (int, optional) – Number of bins in the estimated masks. If None, out_chan = in_chan.
  • n_blocks (int, optional) – Number of convolutional blocks in each repeat. Defaults to 8.
  • n_repeats (int, optional) – Number of repeats. Defaults to 3.
  • bn_chan (int, optional) – Number of channels after the bottleneck.
  • hid_chan (int, optional) – Number of channels in the convolutional blocks.
  • skip_chan (int, optional) – Number of channels in the skip connections. If 0 or None, TDConvNet won’t have any skip connections and the masks will be computed from the residual output. Corresponds to the ConvTasnet architecture in v1 or the paper.
  • conv_kernel_size (int, optional) – Kernel size in convolutional blocks.
  • norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.
  • mask_act (str, optional) – Which non-linear function to generate mask.

References

[1] : “Conv-TasNet: Surpassing ideal time-frequency magnitude masking for speech separation” TASLP 2019 Yi Luo, Nima Mesgarani https://arxiv.org/abs/1809.07454

forward(mixture_w)[source]
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()[source]
class asteroid.masknn.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)[source]

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)[source]
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()[source]
class asteroid.masknn.DPTransformer(in_chan, n_src, n_heads=4, ff_hid=256, chunk_size=100, hop_size=None, n_repeats=6, norm_type='gLN', ff_activation='relu', mask_act='relu', bidirectional=True, dropout=0)[source]

Bases: sphinx.ext.autodoc.importer._MockObject

Dual-path Transformer
introduced in [1].
Parameters:
  • in_chan (int) – Number of input filters.
  • n_src (int) – Number of masks to estimate.
  • n_heads (int) – Number of attention heads.
  • hid_ff (int) – Number of neurons in the RNNs cell state. Defaults to 256.
  • 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.
  • ff_activation (str, optional) – activation function applied at the output of RNN.
  • 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).
  • dropout (float, optional) – Dropout ratio, must be in [0,1].

References

[1] Chen, Jingjing, Qirong Mao, and Dong Liu. “Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation.”

arXiv preprint arXiv:2007.13975 (2020).
forward(mixture_w)[source]
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()[source]
class asteroid.masknn.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)[source]

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)[source]
get_config()[source]
class asteroid.masknn.SuDORMRF(in_chan, n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='softmax')[source]

Bases: sphinx.ext.autodoc.importer._MockObject

SuDORMRF mask network, as described in [1].

Parameters:
  • in_chan (int) – Number of input channels. Also number of output channels.
  • n_src (int) – Number of sources in the input mixtures.
  • bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.
  • num_blocks (int) – Number of of UBlocks.
  • upsampling_depth (int) – Depth of upsampling.
  • mask_act (str) – Name of output activation.

References

[1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”,
Tzinis et al. MLSP 2020.
forward(x)[source]
get_config()[source]
class asteroid.masknn.SuDORMRFImproved(in_chan, n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='relu')[source]

Bases: sphinx.ext.autodoc.importer._MockObject

Improved SuDORMRF mask network, as described in [1].

Parameters:
  • in_chan (int) – Number of input channels. Also number of output channels.
  • n_src (int) – Number of sources in the input mixtures.
  • bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.
  • num_blocks (int) – Number of of UBlocks
  • upsampling_depth (int) – Depth of upsampling
  • mask_act (str) – Name of output activation.

References

[1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”,
Tzinis et al. MLSP 2020.
forward(x)[source]
get_config()[source]
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