asteroid.masknn.recurrent module¶
-
class
asteroid.masknn.recurrent.
DCCRMaskNet
(encoders, decoders, n_freqs, **kwargs)[source]¶ Bases:
asteroid.masknn.base.BaseDCUMaskNet
Masking part of DCCRNet, as proposed in [1].
Valid architecture values for the
default_architecture
classmethod are: “DCCRN”.Parameters: - encoders (list of length N of tuples of (in_chan, out_chan, kernel_size, stride, padding)) – Arguments of encoders of the u-net
- decoders (list of length N of tuples of (in_chan, out_chan, kernel_size, stride, padding)) – Arguments of decoders of the u-net
- n_freqs (int) – Number of frequencies (dim 1) of input to ``.forward()`. n_freqs - 1 must be divisible by f_0 * f_1 * … * f_N where f_k are the frequency strides of the encoders.
References
[1] : “DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement”, Yanxin Hu et al. https://arxiv.org/abs/2008.00264
-
class
asteroid.masknn.recurrent.
DCCRMaskNetRNN
(in_size, hid_size=128, rnn_type='LSTM', norm_type=None)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
RNN (LSTM) layer between encoders and decoders introduced in [1].
Parameters: - in_size (int) – Number of inputs to the RNN. Must be the product of non-batch, non-time dimensions of output shape of last encoder, i.e. if the last encoder output shape is [batch, n_chans, n_freqs, time], in_size must be n_chans * n_freqs.
- hid_size (int, optional) – Number of units in RNN.
- rnn_type (str, optional) – Type of RNN to use. See
SingleRNN
for valid values. - norm_type (Optional[str], optional) – Norm to use after linear.
See
asteroid.masknn.norms
for valid values. (Not used in [1]).
References
[1] : “DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement”, Yanxin Hu et al. https://arxiv.org/abs/2008.00264
-
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)[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]
-
class
asteroid.masknn.recurrent.
DPRNNBlock
(in_chan, hid_size, norm_type='gLN', bidirectional=True, rnn_type='LSTM', num_layers=1, dropout=0)[source]¶ 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
-
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)[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
-
class
asteroid.masknn.recurrent.
SingleRNN
(rnn_type, input_size, hidden_size, n_layers=1, dropout=0, bidirectional=False)[source]¶ 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
.
- rnn_type (str) – Select from
-
class
asteroid.masknn.recurrent.
StackedResidualBiRNN
(rnn_type, n_units, n_layers=4, dropout=0.0, bidirectional=True)[source]¶ 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)
- rnn_type (str) – Select from
-
class
asteroid.masknn.recurrent.
StackedResidualRNN
(rnn_type, n_units, n_layers=4, dropout=0.0, bidirectional=False)[source]¶ 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)
- rnn_type (str) – Select from