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from ..filterbanks import make_enc_dec
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from ..masknn import DPTransformer
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from .base_models import BaseEncoderMaskerDecoder
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class DPTNet(BaseEncoderMaskerDecoder):
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"""DPTNet separation model, as described in [1].
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Args:
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n_src (int): Number of masks to estimate.
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out_chan (int or None): Number of bins in the estimated masks.
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Defaults to `in_chan`.
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bn_chan (int): Number of channels after the bottleneck.
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Defaults to 128.
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hid_size (int): Number of neurons in the RNNs cell state.
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Defaults to 128.
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chunk_size (int): window size of overlap and add processing.
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Defaults to 100.
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hop_size (int or None): hop size (stride) of overlap and add processing.
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Default to `chunk_size // 2` (50% overlap).
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n_repeats (int): Number of repeats. Defaults to 6.
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norm_type (str, optional): Type of normalization to use. To choose from
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- ``'gLN'``: global Layernorm
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- ``'cLN'``: channelwise Layernorm
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mask_act (str, optional): Which non-linear function to generate mask.
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bidirectional (bool, optional): True for bidirectional Inter-Chunk RNN
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(Intra-Chunk is always bidirectional).
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rnn_type (str, optional): Type of RNN used. Choose between ``'RNN'``,
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``'LSTM'`` and ``'GRU'``.
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num_layers (int, optional): Number of layers in each RNN.
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dropout (float, optional): Dropout ratio, must be in [0,1].
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in_chan (int, optional): Number of input channels, should be equal to
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n_filters.
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fb_name (str, className): Filterbank family from which to make encoder
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and decoder. To choose among [``'free'``, ``'analytic_free'``,
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``'param_sinc'``, ``'stft'``].
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n_filters (int): Number of filters / Input dimension of the masker net.
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kernel_size (int): Length of the filters.
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stride (int, optional): Stride of the convolution.
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If None (default), set to ``kernel_size // 2``.
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**fb_kwargs (dict): Additional kwards to pass to the filterbank
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creation.
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References:
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[1]: Jingjing Chen et al. "Dual-Path Transformer Network: Direct
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Context-Aware Modeling for End-to-End Monaural Speech Separation"
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Interspeech 2020.
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"""
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def __init__(
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self,
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n_src,
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ff_hid=256,
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chunk_size=100,
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hop_size=None,
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n_repeats=6,
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norm_type="gLN",
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ff_activation="relu",
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encoder_activation="relu",
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mask_act="relu",
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bidirectional=True,
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dropout=0,
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in_chan=None,
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fb_name="free",
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kernel_size=16,
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n_filters=64,
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stride=8,
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**fb_kwargs,
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):
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encoder, decoder = make_enc_dec(
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fb_name, kernel_size=kernel_size, n_filters=n_filters, stride=stride, **fb_kwargs
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)
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n_feats = encoder.n_feats_out
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if in_chan is not None:
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assert in_chan == n_feats, (
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"Number of filterbank output channels"
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" and number of input channels should "
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"be the same. Received "
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f"{n_feats} and {in_chan}"
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)
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masker = DPTransformer(
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n_feats,
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n_src,
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ff_hid=ff_hid,
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ff_activation=ff_activation,
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chunk_size=chunk_size,
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hop_size=hop_size,
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n_repeats=n_repeats,
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norm_type=norm_type,
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mask_act=mask_act,
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bidirectional=bidirectional,
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dropout=dropout,
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)
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super().__init__(encoder, masker, decoder, encoder_activation=encoder_activation)
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