| | 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`.
|
| | 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]: Jingjing Chen et al. "Dual-Path Transformer Network: Direct
|
| | Context-Aware Modeling for End-to-End Monaural Speech Separation"
|
| | Interspeech 2020.
|
| | """
|
| |
|
| | def __init__(
|
| | self,
|
| | n_src,
|
| | ff_hid=256,
|
| | chunk_size=100,
|
| | hop_size=None,
|
| | n_repeats=6,
|
| | norm_type="gLN",
|
| | ff_activation="relu",
|
| | encoder_activation="relu",
|
| | mask_act="relu",
|
| | bidirectional=True,
|
| | dropout=0,
|
| | in_chan=None,
|
| | fb_name="free",
|
| | kernel_size=16,
|
| | n_filters=64,
|
| | stride=8,
|
| | **fb_kwargs,
|
| | ):
|
| | encoder, decoder = make_enc_dec(
|
| | 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,
|
| | n_src,
|
| | ff_hid=ff_hid,
|
| | ff_activation=ff_activation,
|
| | chunk_size=chunk_size,
|
| | hop_size=hop_size,
|
| | n_repeats=n_repeats,
|
| | norm_type=norm_type,
|
| | mask_act=mask_act,
|
| | bidirectional=bidirectional,
|
| | dropout=dropout,
|
| | )
|
| | super().__init__(encoder, masker, decoder, encoder_activation=encoder_activation)
|
| |
|
