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# 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 torch
from torch.autograd import Variable
import torch.nn.functional as F
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
class Invertible1x1Conv(torch.nn.Module):
"""
The layer outputs both the convolution, and the log determinant
of its weight matrix. If reverse=True it does convolution with
inverse
"""
def __init__(self, c):
super(Invertible1x1Conv, self).__init__()
self.conv = torch.nn.Conv1d(
c, c, kernel_size=1, stride=1, padding=0, bias=False
)
# Sample a random orthonormal matrix to initialize weights
W = torch.linalg.qr(torch.FloatTensor(c, c).normal_())[0]
# Ensure determinant is 1.0 not -1.0
if torch.det(W) < 0:
W[:, 0] = -1 * W[:, 0]
W = W.view(c, c, 1)
self.conv.weight.data = W
def forward(self, z, reverse=False):
# shape
batch_size, group_size, n_of_groups = z.size()
W = self.conv.weight.squeeze()
if reverse:
if not hasattr(self, "W_inverse"):
# Reverse computation
W_inverse = W.float().inverse()
W_inverse = Variable(W_inverse[..., None])
if z.type() == "torch.cuda.HalfTensor":
W_inverse = W_inverse.half()
self.W_inverse = W_inverse
z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
return z
else:
# Forward computation
log_det_W = batch_size * n_of_groups * torch.logdet(W)
z = self.conv(z)
return z, log_det_W
class WN(torch.nn.Module):
"""
This is the WaveNet like layer for the affine coupling. The primary difference
from WaveNet is the convolutions need not be causal. There is also no dilation
size reset. The dilation only doubles on each layer
"""
def __init__(
self, n_in_channels, n_mel_channels, n_layers, n_channels, kernel_size
):
super(WN, self).__init__()
assert kernel_size % 2 == 1
assert n_channels % 2 == 0
self.n_layers = n_layers
self.n_channels = n_channels
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
start = torch.nn.utils.weight_norm(start, name="weight")
self.start = start
# Initializing last layer to 0 makes the affine coupling layers
# do nothing at first. This helps with training stability
end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1)
end.weight.data.zero_()
end.bias.data.zero_()
self.end = end
cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels * n_layers, 1)
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
for i in range(n_layers):
dilation = 2**i
padding = int((kernel_size * dilation - dilation) / 2)
in_layer = torch.nn.Conv1d(
n_channels,
2 * n_channels,
kernel_size,
dilation=dilation,
padding=padding,
)
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
self.in_layers.append(in_layer)
# last one is not necessary
if i < n_layers - 1:
res_skip_channels = 2 * n_channels
else:
res_skip_channels = n_channels
res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
self.res_skip_layers.append(res_skip_layer)
def forward(self, forward_input):
audio, spect = forward_input
audio = self.start(audio)
output = torch.zeros_like(audio)
n_channels_tensor = torch.IntTensor([self.n_channels])
spect = self.cond_layer(spect)
for i in range(self.n_layers):
spect_offset = i * 2 * self.n_channels
acts = fused_add_tanh_sigmoid_multiply(
self.in_layers[i](audio),
spect[:, spect_offset : spect_offset + 2 * self.n_channels, :],
n_channels_tensor,
)
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.n_layers - 1:
audio = audio + res_skip_acts[:, : self.n_channels, :]
output = output + res_skip_acts[:, self.n_channels :, :]
else:
output = output + res_skip_acts
return self.end(output)
class WaveGlow(torch.nn.Module):
def __init__(self, cfg):
super(WaveGlow, self).__init__()
self.cfg = cfg
self.upsample = torch.nn.ConvTranspose1d(
self.cfg.VOCODER.INPUT_DIM,
self.cfg.VOCODER.INPUT_DIM,
1024,
stride=256,
)
assert self.cfg.VOCODER.N_GROUP % 2 == 0
self.n_flows = self.cfg.VOCODER.N_FLOWS
self.n_group = self.cfg.VOCODER.N_GROUP
self.n_early_every = self.cfg.VOCODER.N_EARLY_EVERY
self.n_early_size = self.cfg.VOCODER.N_EARLY_SIZE
self.WN = torch.nn.ModuleList()
self.convinv = torch.nn.ModuleList()
n_half = int(self.cfg.VOCODER.N_GROUP / 2)
# Set up layers with the right sizes based on how many dimensions
# have been output already
n_remaining_channels = self.cfg.VOCODER.N_GROUP
for k in range(self.cfg.VOCODER.N_FLOWS):
if k % self.n_early_every == 0 and k > 0:
n_half = n_half - int(self.n_early_size / 2)
n_remaining_channels = n_remaining_channels - self.n_early_size
self.convinv.append(Invertible1x1Conv(n_remaining_channels))
self.WN.append(
WN(
n_half,
self.cfg.VOCODER.INPUT_DIM * self.cfg.VOCODER.N_GROUP,
self.cfg.VOCODER.N_LAYERS,
self.cfg.VOCODER.N_CHANNELS,
self.cfg.VOCODER.KERNEL_SIZE,
)
)
self.n_remaining_channels = n_remaining_channels # Useful during inference
def forward(self, forward_input):
"""
forward_input[0] = mel_spectrogram: batch x n_mel_channels x frames
forward_input[1] = audio: batch x time
"""
spect, audio = forward_input
# Upsample spectrogram to size of audio
spect = self.upsample(spect)
assert spect.size(2) >= audio.size(1)
if spect.size(2) > audio.size(1):
spect = spect[:, :, : audio.size(1)]
spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
spect = (
spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
)
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
output_audio = []
log_s_list = []
log_det_W_list = []
for k in range(self.n_flows):
if k % self.n_early_every == 0 and k > 0:
output_audio.append(audio[:, : self.n_early_size, :])
audio = audio[:, self.n_early_size :, :]
audio, log_det_W = self.convinv[k](audio)
log_det_W_list.append(log_det_W)
n_half = int(audio.size(1) / 2)
audio_0 = audio[:, :n_half, :]
audio_1 = audio[:, n_half:, :]
output = self.WN[k]((audio_0, spect))
log_s = output[:, n_half:, :]
b = output[:, :n_half, :]
audio_1 = torch.exp(log_s) * audio_1 + b
log_s_list.append(log_s)
audio = torch.cat([audio_0, audio_1], 1)
output_audio.append(audio)
return torch.cat(output_audio, 1), log_s_list, log_det_W_list
@staticmethod
def remove_weightnorm(model):
waveglow = model
for WN in waveglow.WN:
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
WN.in_layers = remove(WN.in_layers)
WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
WN.res_skip_layers = remove(WN.res_skip_layers)
return waveglow
def remove(conv_list):
new_conv_list = torch.nn.ModuleList()
for old_conv in conv_list:
old_conv = torch.nn.utils.remove_weight_norm(old_conv)
new_conv_list.append(old_conv)
return new_conv_list
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