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214 lines
7.7 KiB
214 lines
7.7 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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modules for wavenet
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"""
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from __future__ import with_statement, print_function, absolute_import
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import math
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import numpy as np
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from wavenet_vocoder import conv
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from mindspore import nn
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from mindspore.ops import operations as P
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def Conv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
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m = conv.Conv1d(in_channels, out_channels, kernel_size, **kwargs)
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return m
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def Conv1d1x1(in_channels, out_channels, has_bias=True):
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return Conv1d(in_channels, out_channels, kernel_size=1, pad_mode='pad', padding=0, dilation=1, has_bias=has_bias)
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def Embedding(num_embeddings, embedding_dim, padding_idx, std=0.01):
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m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
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return m
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def _conv1x1_forward(conv_, x, is_incremental, is_numpy=True):
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"""
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Conv1x1 forward
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"""
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if is_incremental:
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x = conv_.incremental_forward(x, is_numpy=is_numpy)
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else:
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x = conv_(x)
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return x
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class ResidualConv1dGLU(nn.Cell):
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"""Residual dilated conv1d with gated activation units
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Args:
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residual_channels (int): Residual input / output channels
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gate_channels (int): Gated activation channels.
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kernel_size (int): Kernel size
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skip_out_channels (int): Skip connection channels. If None, it will set to the same as residual_channels.
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cin_channels (int): Local conditioning channels. If given negative value, local conditioning is disabled.
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gin_channels (int): Global conditioning channels. If given negative value, global conditioning is disabled.
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dropout (float): Dropout rate.
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padding (int): Padding for convolution layers. If None, padding value will be computed according to dilation
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and kernel_size.
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dilation (int): Dilation factor.
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"""
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def __init__(self, residual_channels=None, gate_channels=None, kernel_size=None, skip_out_channels=None, bias=True,
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dropout=1 - 0.95, dilation=1, cin_channels=-1, gin_channels=-1, padding=None, causal=True):
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super(ResidualConv1dGLU, self).__init__()
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self.dropout = dropout
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self.dropout_op = nn.Dropout(keep_prob=1. - self.dropout)
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self.eval_split_op = P.Split(axis=-1, output_num=2)
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self.train_split_op = P.Split(axis=1, output_num=2)
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self.tanh = P.Tanh()
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self.sigmoid = P.Sigmoid()
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self.mul = P.Mul()
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self.add = P.Add()
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if skip_out_channels is None:
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skip_out_channels = residual_channels
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if padding is None:
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if causal:
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padding = (kernel_size - 1) * dilation
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else:
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padding = (kernel_size - 1) // 2 * dilation
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self.causal = causal
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self.conv = Conv1d(residual_channels, gate_channels, kernel_size, pad_mode='pad',
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padding=padding, dilation=dilation, has_bias=bias)
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# local conditioning
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if cin_channels > 0:
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self.conv1x1c = Conv1d1x1(cin_channels, gate_channels, has_bias=False)
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else:
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self.conv1x1c = None
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# global conditioning
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if gin_channels > 0:
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self.conv1x1g = Conv1d(gin_channels, gate_channels, has_bias=False, kernel_size=1, dilation=1)
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else:
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self.conv1x1g = None
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gate_out_channels = gate_channels // 2
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self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels, has_bias=bias)
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self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_out_channels, has_bias=bias)
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self.factor = math.sqrt(0.5)
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def construct(self, x, c=None, g=None):
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"""
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Args:
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x(Tensor): One-hot audio signal, the shape is B x C x T
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c(Tensor): local conditional feature, the shape is B x cin_channels x T
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g(Tensor): global conditional feature, not used currently
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Returns:
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Tensor: Output tensor
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"""
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residual = x
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x = self.dropout_op(x)
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x = self.conv(x)
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# remove future time steps
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x = x[:, :, :residual.shape[-1]] if self.causal else x
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split_op = self.train_split_op
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a, b = split_op(x)
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# local conditioning
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if c is not None:
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c = _conv1x1_forward(self.conv1x1c, c, is_incremental=False)
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ca, cb = split_op(c)
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a, b = a + ca, b + cb
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# global conditioning
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if g is not None:
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g = _conv1x1_forward(self.conv1x1g, g, is_incremental=False)
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ga, gb = self.split(g)
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a, b = a + ga, b + gb
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x = self.mul(self.tanh(a), self.sigmoid(b))
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# For skip connection
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s = _conv1x1_forward(self.conv1x1_skip, x, is_incremental=False)
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# For residual connection
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x = _conv1x1_forward(self.conv1x1_out, x, is_incremental=False)
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x = self.add(x, residual) * self.factor
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return x, s
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def sigmoid_numpy(self, x):
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return 1. / (1 + np.exp(-x))
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def incremental_forward(self, x, c=None, g=None, is_numpy=True):
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"""
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Incremental forward. Used for inference stage
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Args:
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x (Tensor): One-hot audio signal, the shape is B x C x T
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c (Tensor): local conditional feature, the shape is B x cin_channels x T
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g (Tensor): global conditional feature, not used currently
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Returns:
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ndarray
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"""
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residual = x
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x = self.conv.incremental_forward(x, is_numpy=is_numpy)
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if is_numpy:
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a, b = np.split(x, indices_or_sections=2, axis=-1)
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else:
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a, b = self.eval_split_op(x)
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# local conditioning
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if c is not None:
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c = _conv1x1_forward(self.conv1x1c, c, is_incremental=True, is_numpy=is_numpy)
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if is_numpy:
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ca, cb = np.split(c, indices_or_sections=2, axis=-1)
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else:
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ca, cb = self.eval_split_op(c)
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a, b = a + ca, b + cb
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# global conditioning
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if g is not None:
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g = _conv1x1_forward(self.conv1x1g, g, is_incremental=True, is_numpy=is_numpy)
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if is_numpy:
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ga, gb = np.split(g, indices_or_sections=2, axis=-1)
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else:
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ga, gb = self.eval_split_op(c)
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a, b = a + ga, b + gb
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if is_numpy:
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x = np.tanh(a) * self.sigmoid_numpy(b)
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else:
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x = self.mul(self.tanh(a), self.sigmoid(b))
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# For skip connection
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s = _conv1x1_forward(self.conv1x1_skip, x, is_incremental=True, is_numpy=is_numpy)
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# For residual connection
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x = _conv1x1_forward(self.conv1x1_out, x, is_incremental=True, is_numpy=is_numpy)
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x = (x + residual) * self.factor
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return x, s
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def clear_buffer(self):
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"""clear buffer"""
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for c in [self.conv, self.conv1x1_out, self.conv1x1_skip,
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self.conv1x1c, self.conv1x1g]:
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if c is not None:
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c.clear_buffer()
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