asteroid.models.dprnn_tasnet module¶

class asteroid.models.dprnn_tasnet.DPRNNTasNet(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='sigmoid', bidirectional=True, rnn_type='LSTM', num_layers=1, dropout=0, in_chan=None, fb_name='free', kernel_size=16, n_filters=64, stride=8, encoder_activation=None, **fb_kwargs)[source]¶

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

DPRNN separation model, as described in [1].

Parameters:

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].
in_chan (int, optional) – Number of input channels, should be equal to n_filters.
fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].
n_filters (int) – Number of filters / Input dimension of the masker net.
kernel_size (int) – Length of the filters.
stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.
**fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

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