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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.FastDiff.module.util import calc_noise_scale_embedding
def swish(x):
return x * torch.sigmoid(x)
# dilated conv layer with kaiming_normal initialization
# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1):
super(Conv, self).__init__()
self.padding = dilation * (kernel_size - 1) // 2
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, dilation=dilation, padding=self.padding)
self.conv = nn.utils.weight_norm(self.conv)
nn.init.kaiming_normal_(self.conv.weight)
def forward(self, x):
out = self.conv(x)
return out
# conv1x1 layer with zero initialization
# from https://github.com/ksw0306/FloWaveNet/blob/master/modules.py but the scale parameter is removed
class ZeroConv1d(nn.Module):
def __init__(self, in_channel, out_channel):
super(ZeroConv1d, self).__init__()
self.conv = nn.Conv1d(in_channel, out_channel, kernel_size=1, padding=0)
self.conv.weight.data.zero_()
self.conv.bias.data.zero_()
def forward(self, x):
out = self.conv(x)
return out
# every residual block (named residual layer in paper)
# contains one noncausal dilated conv
class Residual_block(nn.Module):
def __init__(self, res_channels, skip_channels, dilation,
noise_scale_embed_dim_out, multiband=True):
super(Residual_block, self).__init__()
self.res_channels = res_channels
# the layer-specific fc for noise scale embedding
self.fc_t = nn.Linear(noise_scale_embed_dim_out, self.res_channels)
# dilated conv layer
self.dilated_conv_layer = Conv(self.res_channels, 2 * self.res_channels, kernel_size=3, dilation=dilation)
# add mel spectrogram upsampler and conditioner conv1x1 layer
self.upsample_conv2d = torch.nn.ModuleList()
if multiband is True:
params = 8
else:
params = 16
for s in [params, params]: ####### Very Important!!!!! #######
conv_trans2d = torch.nn.ConvTranspose2d(1, 1, (3, 2 * s), padding=(1, s // 2), stride=(1, s))
conv_trans2d = torch.nn.utils.weight_norm(conv_trans2d)
torch.nn.init.kaiming_normal_(conv_trans2d.weight)
self.upsample_conv2d.append(conv_trans2d)
self.mel_conv = Conv(80, 2 * self.res_channels, kernel_size=1) # 80 is mel bands
# residual conv1x1 layer, connect to next residual layer
self.res_conv = nn.Conv1d(res_channels, res_channels, kernel_size=1)
self.res_conv = nn.utils.weight_norm(self.res_conv)
nn.init.kaiming_normal_(self.res_conv.weight)
# skip conv1x1 layer, add to all skip outputs through skip connections
self.skip_conv = nn.Conv1d(res_channels, skip_channels, kernel_size=1)
self.skip_conv = nn.utils.weight_norm(self.skip_conv)
nn.init.kaiming_normal_(self.skip_conv.weight)
def forward(self, input_data):
x, mel_spec, noise_scale_embed = input_data
h = x
B, C, L = x.shape # B, res_channels, L
assert C == self.res_channels
# add in noise scale embedding
part_t = self.fc_t(noise_scale_embed)
part_t = part_t.view([B, self.res_channels, 1])
h += part_t
# dilated conv layer
h = self.dilated_conv_layer(h)
# add mel spectrogram as (local) conditioner
assert mel_spec is not None
# Upsample spectrogram to size of audio
mel_spec = torch.unsqueeze(mel_spec, dim=1) # (B, 1, 80, T')
mel_spec = F.leaky_relu(self.upsample_conv2d[0](mel_spec), 0.4)
mel_spec = F.leaky_relu(self.upsample_conv2d[1](mel_spec), 0.4)
mel_spec = torch.squeeze(mel_spec, dim=1)
assert(mel_spec.size(2) >= L)
if mel_spec.size(2) > L:
mel_spec = mel_spec[:, :, :L]
mel_spec = self.mel_conv(mel_spec)
h += mel_spec
# gated-tanh nonlinearity
out = torch.tanh(h[:,:self.res_channels,:]) * torch.sigmoid(h[:,self.res_channels:,:])
# residual and skip outputs
res = self.res_conv(out)
assert x.shape == res.shape
skip = self.skip_conv(out)
return (x + res) * math.sqrt(0.5), skip # normalize for training stability
class Residual_group(nn.Module):
def __init__(self, res_channels, skip_channels, num_res_layers, dilation_cycle,
noise_scale_embed_dim_in,
noise_scale_embed_dim_mid,
noise_scale_embed_dim_out, multiband):
super(Residual_group, self).__init__()
self.num_res_layers = num_res_layers
self.noise_scale_embed_dim_in = noise_scale_embed_dim_in
# the shared two fc layers for noise scale embedding
self.fc_t1 = nn.Linear(noise_scale_embed_dim_in, noise_scale_embed_dim_mid)
self.fc_t2 = nn.Linear(noise_scale_embed_dim_mid, noise_scale_embed_dim_out)
# stack all residual blocks with dilations 1, 2, ... , 512, ... , 1, 2, ..., 512
self.residual_blocks = nn.ModuleList()
for n in range(self.num_res_layers):
self.residual_blocks.append(Residual_block(res_channels, skip_channels,
dilation=2 ** (n % dilation_cycle),
noise_scale_embed_dim_out=noise_scale_embed_dim_out, multiband=multiband))
def forward(self, input_data):
x, mel_spectrogram, noise_scales = input_data
# embed noise scale
noise_scale_embed = calc_noise_scale_embedding(noise_scales, self.noise_scale_embed_dim_in)
noise_scale_embed = swish(self.fc_t1(noise_scale_embed))
noise_scale_embed = swish(self.fc_t2(noise_scale_embed))
# pass all residual layers
h = x
skip = 0
for n in range(self.num_res_layers):
h, skip_n = self.residual_blocks[n]((h, mel_spectrogram, noise_scale_embed)) # use the output from last residual layer
skip += skip_n # accumulate all skip outputs
return skip * math.sqrt(1.0 / self.num_res_layers) # normalize for training stability
class WaveNet_vocoder(nn.Module):
def __init__(self, in_channels, res_channels, skip_channels, out_channels,
num_res_layers, dilation_cycle,
noise_scale_embed_dim_in,
noise_scale_embed_dim_mid,
noise_scale_embed_dim_out, multiband):
super(WaveNet_vocoder, self).__init__()
# initial conv1x1 with relu
self.init_conv = nn.Sequential(Conv(in_channels, res_channels, kernel_size=1), nn.ReLU())
# all residual layers
self.residual_layer = Residual_group(res_channels=res_channels,
skip_channels=skip_channels,
num_res_layers=num_res_layers,
dilation_cycle=dilation_cycle,
noise_scale_embed_dim_in=noise_scale_embed_dim_in,
noise_scale_embed_dim_mid=noise_scale_embed_dim_mid,
noise_scale_embed_dim_out=noise_scale_embed_dim_out, multiband=multiband)
# final conv1x1 -> relu -> zeroconv1x1
self.final_conv = nn.Sequential(Conv(skip_channels, skip_channels, kernel_size=1),
nn.ReLU(),
ZeroConv1d(skip_channels, out_channels))
def forward(self, input_data):
audio, mel_spectrogram, noise_scales = input_data # b x band x T, b x 80 x T', b x 1
x = audio
x = self.init_conv(x)
x = self.residual_layer((x, mel_spectrogram, noise_scales))
x = self.final_conv(x)
return x
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