| from ..filterbanks import make_enc_dec
|
| from ..masknn import DPTransformer
|
| from .base_models import BaseEncoderMaskerDecoder
|
|
|
|
|
| class DPTNet(BaseEncoderMaskerDecoder):
|
| """DPTNet separation model, as described in [1].
|
|
|
| Args:
|
| n_src (int): Number of masks to estimate.
|
| out_chan (int or None): Number of bins in the estimated masks.
|
| 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.
|
| 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).
|
| 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).
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| rnn_type (str, optional): Type of RNN used. Choose between ``'RNN'``,
|
| ``'LSTM'`` and ``'GRU'``.
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| 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.
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| fb_name (str, className): Filterbank family from which to make encoder
|
| and decoder. To choose among [``'free'``, ``'analytic_free'``,
|
| ``'param_sinc'``, ``'stft'``].
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| 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]: Jingjing Chen et al. "Dual-Path Transformer Network: Direct
|
| Context-Aware Modeling for End-to-End Monaural Speech Separation"
|
| Interspeech 2020.
|
| """
|
|
|
| def __init__(
|
| self,
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| n_src,
|
| ff_hid=256,
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| chunk_size=100,
|
| hop_size=None,
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| n_repeats=6,
|
| 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,
|
| in_chan=None,
|
| fb_name="free",
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| kernel_size=16,
|
| n_filters=64,
|
| stride=8,
|
| **fb_kwargs,
|
| ):
|
| encoder, decoder = make_enc_dec(
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| fb_name, kernel_size=kernel_size, n_filters=n_filters, stride=stride, **fb_kwargs
|
| )
|
| n_feats = encoder.n_feats_out
|
| if in_chan is not None:
|
| assert in_chan == n_feats, (
|
| "Number of filterbank output channels"
|
| " and number of input channels should "
|
| "be the same. Received "
|
| f"{n_feats} and {in_chan}"
|
| )
|
|
|
| masker = DPTransformer(
|
| n_feats,
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| n_src,
|
| ff_hid=ff_hid,
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| ff_activation=ff_activation,
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| chunk_size=chunk_size,
|
| hop_size=hop_size,
|
| n_repeats=n_repeats,
|
| norm_type=norm_type,
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| mask_act=mask_act,
|
| bidirectional=bidirectional,
|
| dropout=dropout,
|
| )
|
| super().__init__(encoder, masker, decoder, encoder_activation=encoder_activation)
|
|
|
