268 lines
8.9 KiB
Python
268 lines
8.9 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2021 Jingyong Hou (houjingyong@gmail.com)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class DSDilatedConv1d(nn.Module):
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"""Dilated Depthwise-Separable Convolution"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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dilation: int = 1,
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stride: int = 1,
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bias: bool = True,
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):
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super(DSDilatedConv1d, self).__init__()
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self.receptive_fields = dilation * (kernel_size - 1)
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self.conv = nn.Conv1d(
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in_channels,
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in_channels,
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kernel_size,
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padding=0,
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dilation=dilation,
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stride=stride,
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groups=in_channels,
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bias=bias,
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)
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self.bn = nn.BatchNorm1d(in_channels)
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self.pointwise = nn.Conv1d(in_channels,
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out_channels,
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kernel_size=1,
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padding=0,
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dilation=1,
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bias=bias)
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def forward(self, inputs: torch.Tensor):
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outputs = self.conv(inputs)
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outputs = self.bn(outputs)
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outputs = self.pointwise(outputs)
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return outputs
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class TCNBlock(nn.Module):
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def __init__(
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self,
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in_channels: int,
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res_channels: int,
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kernel_size: int,
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dilation: int,
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causal: bool,
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):
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super(TCNBlock, self).__init__()
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self.in_channels = in_channels
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self.res_channels = res_channels
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self.kernel_size = kernel_size
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self.dilation = dilation
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self.causal = causal
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self.receptive_fields = dilation * (kernel_size - 1)
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self.half_receptive_fields = self.receptive_fields // 2
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self.conv1 = DSDilatedConv1d(
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in_channels=in_channels,
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out_channels=res_channels,
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kernel_size=kernel_size,
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dilation=dilation,
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)
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self.bn1 = nn.BatchNorm1d(res_channels)
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self.relu1 = nn.ReLU()
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self.conv2 = nn.Conv1d(in_channels=res_channels,
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out_channels=res_channels,
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kernel_size=1)
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self.bn2 = nn.BatchNorm1d(res_channels)
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self.relu2 = nn.ReLU()
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def forward(self, inputs: torch.Tensor):
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outputs = self.relu1(self.bn1(self.conv1(inputs)))
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outputs = self.bn2(self.conv2(outputs))
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if self.causal:
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inputs = inputs[:, :, self.receptive_fields:]
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else:
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inputs = inputs[:, :, self.
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half_receptive_fields:-self.half_receptive_fields]
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if self.in_channels == self.res_channels:
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res_out = self.relu2(outputs + inputs)
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else:
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res_out = self.relu2(outputs)
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return res_out
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class TCNStack(nn.Module):
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def __init__(
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self,
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in_channels: int,
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stack_num: int,
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stack_size: int,
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res_channels: int,
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kernel_size: int,
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causal: bool,
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):
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super(TCNStack, self).__init__()
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self.in_channels = in_channels
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self.stack_num = stack_num
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self.stack_size = stack_size
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self.res_channels = res_channels
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self.kernel_size = kernel_size
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self.causal = causal
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self.res_blocks = self.stack_tcn_blocks()
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self.receptive_fields = self.calculate_receptive_fields()
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self.res_blocks = nn.Sequential(*self.res_blocks)
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def calculate_receptive_fields(self):
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receptive_fields = 0
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for block in self.res_blocks:
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receptive_fields += block.receptive_fields
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return receptive_fields
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def build_dilations(self):
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dilations = []
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for s in range(0, self.stack_size):
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for l in range(0, self.stack_num):
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dilations.append(2**l)
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return dilations
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def stack_tcn_blocks(self):
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dilations = self.build_dilations()
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res_blocks = nn.ModuleList()
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res_blocks.append(
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TCNBlock(
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self.in_channels,
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self.res_channels,
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self.kernel_size,
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dilations[0],
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self.causal,
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))
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for dilation in dilations[1:]:
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res_blocks.append(
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TCNBlock(
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self.res_channels,
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self.res_channels,
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self.kernel_size,
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dilation,
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self.causal,
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))
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return res_blocks
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def forward(self, inputs: torch.Tensor):
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outputs = inputs
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outputs = self.res_blocks(outputs)
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return outputs
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class MDTC(nn.Module):
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"""Multi-scale Depthwise Temporal Convolution (MDTC).
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In MDTC, stacked depthwise one-dimensional (1-D) convolution with
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dilated connections is adopted to efficiently model long-range
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dependency of speech. With a large receptive field while
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keeping a small number of model parameters, the structure
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can model temporal context of speech effectively. It aslo
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extracts multi-scale features from different hidden layers
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of MDTC with different receptive fields.
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"""
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def __init__(
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self,
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stack_num: int,
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stack_size: int,
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in_channels: int,
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res_channels: int,
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kernel_size: int,
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causal: bool,
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):
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super(MDTC, self).__init__()
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self.kernel_size = kernel_size
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self.causal = causal
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self.preprocessor = TCNBlock(in_channels,
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res_channels,
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kernel_size,
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dilation=1,
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causal=causal)
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self.relu = nn.ReLU()
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self.blocks = nn.ModuleList()
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self.receptive_fields = self.preprocessor.receptive_fields
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for i in range(stack_num):
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self.blocks.append(
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TCNStack(res_channels, stack_size, 1, res_channels,
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kernel_size, causal))
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self.receptive_fields += self.blocks[-1].receptive_fields
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self.half_receptive_fields = self.receptive_fields // 2
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print('Receptive Fields: %d' % self.receptive_fields)
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def normalize_length_causal(self, skip_connections: list):
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output_size = skip_connections[-1].shape[-1]
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normalized_outputs = []
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for x in skip_connections:
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remove_length = x.shape[-1] - output_size
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if remove_length != 0:
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normalized_outputs.append(x[:, :, remove_length:])
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else:
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normalized_outputs.append(x)
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return normalized_outputs
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def normalize_length(self, skip_connections: list):
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output_size = skip_connections[-1].shape[-1]
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normalized_outputs = []
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for x in skip_connections:
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remove_length = (x.shape[-1] - output_size) // 2
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if remove_length != 0:
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normalized_outputs.append(x[:, :,
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remove_length:-remove_length])
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else:
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normalized_outputs.append(x)
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return normalized_outputs
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def forward(self, x: torch.Tensor):
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if self.causal:
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outputs = F.pad(x, (0, 0, self.receptive_fields, 0, 0, 0),
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'constant')
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else:
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outputs = F.pad(
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x,
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(0, 0, self.half_receptive_fields, self.half_receptive_fields,
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0, 0),
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'constant',
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)
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outputs = outputs.transpose(1, 2)
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outputs_list = []
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outputs = self.relu(self.preprocessor(outputs))
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for i in range(len(self.blocks)):
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outputs = self.blocks[i](outputs)
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outputs_list.append(outputs)
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if self.causal:
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outputs_list = self.normalize_length_causal(outputs_list)
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else:
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outputs_list = self.normalize_length(outputs_list)
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outputs = sum(outputs_list)
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outputs = outputs.transpose(1, 2)
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return outputs, None
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if __name__ == '__main__':
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mdtc = MDTC(3, 4, 80, 64, 5, causal=True)
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print(mdtc)
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num_params = sum(p.numel() for p in mdtc.parameters())
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print('the number of model params: {}'.format(num_params))
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x = torch.zeros(128, 200, 80) # batch-size * time * dim
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y, _ = mdtc(x) # batch-size * time * dim
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print('input shape: {}'.format(x.shape))
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print('output shape: {}'.format(y.shape))
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