Spaces:
Runtime error
Runtime error
File size: 5,918 Bytes
c968fc3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from torch import nn
from torch.nn import functional as F
from .modules import Conv1d1x1, ResidualConv1dGLU
from .upsample import ConvInUpsampleNetwork
def receptive_field_size(
total_layers, num_cycles, kernel_size, dilation=lambda x: 2**x
):
"""Compute receptive field size
Args:
total_layers (int): total layers
num_cycles (int): cycles
kernel_size (int): kernel size
dilation (lambda): lambda to compute dilation factor. ``lambda x : 1``
to disable dilated convolution.
Returns:
int: receptive field size in sample
"""
assert total_layers % num_cycles == 0
layers_per_cycle = total_layers // num_cycles
dilations = [dilation(i % layers_per_cycle) for i in range(total_layers)]
return (kernel_size - 1) * sum(dilations) + 1
class WaveNet(nn.Module):
"""The WaveNet model that supports local and global conditioning.
Args:
out_channels (int): Output channels. If input_type is mu-law quantized
one-hot vecror. this must equal to the quantize channels. Other wise
num_mixtures x 3 (pi, mu, log_scale).
layers (int): Number of total layers
stacks (int): Number of dilation cycles
residual_channels (int): Residual input / output channels
gate_channels (int): Gated activation channels.
skip_out_channels (int): Skip connection channels.
kernel_size (int): Kernel size of convolution layers.
dropout (float): Dropout probability.
input_dim (int): Number of mel-spec dimension.
upsample_scales (list): List of upsample scale.
``np.prod(upsample_scales)`` must equal to hop size. Used only if
upsample_conditional_features is enabled.
freq_axis_kernel_size (int): Freq-axis kernel_size for transposed
convolution layers for upsampling. If you only care about time-axis
upsampling, set this to 1.
scalar_input (Bool): If True, scalar input ([-1, 1]) is expected, otherwise
quantized one-hot vector is expected..
"""
def __init__(self, cfg):
super(WaveNet, self).__init__()
self.cfg = cfg
self.scalar_input = self.cfg.VOCODER.SCALAR_INPUT
self.out_channels = self.cfg.VOCODER.OUT_CHANNELS
self.cin_channels = self.cfg.VOCODER.INPUT_DIM
self.residual_channels = self.cfg.VOCODER.RESIDUAL_CHANNELS
self.layers = self.cfg.VOCODER.LAYERS
self.stacks = self.cfg.VOCODER.STACKS
self.gate_channels = self.cfg.VOCODER.GATE_CHANNELS
self.kernel_size = self.cfg.VOCODER.KERNEL_SIZE
self.skip_out_channels = self.cfg.VOCODER.SKIP_OUT_CHANNELS
self.dropout = self.cfg.VOCODER.DROPOUT
self.upsample_scales = self.cfg.VOCODER.UPSAMPLE_SCALES
self.mel_frame_pad = self.cfg.VOCODER.MEL_FRAME_PAD
assert self.layers % self.stacks == 0
layers_per_stack = self.layers // self.stacks
if self.scalar_input:
self.first_conv = Conv1d1x1(1, self.residual_channels)
else:
self.first_conv = Conv1d1x1(self.out_channels, self.residual_channels)
self.conv_layers = nn.ModuleList()
for layer in range(self.layers):
dilation = 2 ** (layer % layers_per_stack)
conv = ResidualConv1dGLU(
self.residual_channels,
self.gate_channels,
kernel_size=self.kernel_size,
skip_out_channels=self.skip_out_channels,
bias=True,
dilation=dilation,
dropout=self.dropout,
cin_channels=self.cin_channels,
)
self.conv_layers.append(conv)
self.last_conv_layers = nn.ModuleList(
[
nn.ReLU(inplace=True),
Conv1d1x1(self.skip_out_channels, self.skip_out_channels),
nn.ReLU(inplace=True),
Conv1d1x1(self.skip_out_channels, self.out_channels),
]
)
self.upsample_net = ConvInUpsampleNetwork(
upsample_scales=self.upsample_scales,
cin_pad=self.mel_frame_pad,
cin_channels=self.cin_channels,
)
self.receptive_field = receptive_field_size(
self.layers, self.stacks, self.kernel_size
)
def forward(self, x, mel, softmax=False):
"""Forward step
Args:
x (Tensor): One-hot encoded audio signal, shape (B x C x T)
mel (Tensor): Local conditioning features,
shape (B x cin_channels x T)
softmax (bool): Whether applies softmax or not.
Returns:
Tensor: output, shape B x out_channels x T
"""
B, _, T = x.size()
mel = self.upsample_net(mel)
assert mel.shape[-1] == x.shape[-1]
x = self.first_conv(x)
skips = 0
for f in self.conv_layers:
x, h = f(x, mel)
skips += h
skips *= math.sqrt(1.0 / len(self.conv_layers))
x = skips
for f in self.last_conv_layers:
x = f(x)
x = F.softmax(x, dim=1) if softmax else x
return x
def clear_buffer(self):
self.first_conv.clear_buffer()
for f in self.conv_layers:
f.clear_buffer()
for f in self.last_conv_layers:
try:
f.clear_buffer()
except AttributeError:
pass
def make_generation_fast_(self):
def remove_weight_norm(m):
try:
nn.utils.remove_weight_norm(m)
except ValueError: # this module didn't have weight norm
return
self.apply(remove_weight_norm)
|