import torch
import torchaudio
from torch import nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
from TTS.utils.io import load_fsspec
LRELU_SLOPE = 0.1
def get_padding(k, d):
return int((k * d - d) / 2)
class ResBlock1(torch.nn.Module):
"""Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
Network::
x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
|--------------------------------------------------------------------------------------------------|
Args:
channels (int): number of hidden channels for the convolutional layers.
kernel_size (int): size of the convolution filter in each layer.
dilations (list): list of dilation value for each conv layer in a block.
"""
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super().__init__()
self.convs1 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]),
)
),
]
)
self.convs2 = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
padding=get_padding(kernel_size, 1),
)
),
]
)
def forward(self, x):
"""
Args:
x (Tensor): input tensor.
Returns:
Tensor: output tensor.
Shapes:
x: [B, C, T]
"""
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_parametrizations(l, "weight")
for l in self.convs2:
remove_parametrizations(l, "weight")
class ResBlock2(torch.nn.Module):
"""Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
Network::
x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
|---------------------------------------------------|
Args:
channels (int): number of hidden channels for the convolutional layers.
kernel_size (int): size of the convolution filter in each layer.
dilations (list): list of dilation value for each conv layer in a block.
"""
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super().__init__()
self.convs = nn.ModuleList(
[
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]),
)
),
weight_norm(
Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]),
)
),
]
)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_parametrizations(l, "weight")
class HifiganGenerator(torch.nn.Module):
def __init__(
self,
in_channels,
out_channels,
resblock_type,
resblock_dilation_sizes,
resblock_kernel_sizes,
upsample_kernel_sizes,
upsample_initial_channel,
upsample_factors,
inference_padding=5,
cond_channels=0,
conv_pre_weight_norm=True,
conv_post_weight_norm=True,
conv_post_bias=True,
cond_in_each_up_layer=False,
):
r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
Network:
x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
.. -> zI ---|
resblockN_kNx1 -> zN ---'
Args:
in_channels (int): number of input tensor channels.
out_channels (int): number of output tensor channels.
resblock_type (str): type of the `ResBlock`. '1' or '2'.
resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
for each consecutive upsampling layer.
upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
"""
super().__init__()
self.inference_padding = inference_padding
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_factors)
self.cond_in_each_up_layer = cond_in_each_up_layer
# initial upsampling layers
self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
resblock = ResBlock1 if resblock_type == "1" else ResBlock2
# upsampling layers
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
# MRF blocks
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d))
# post convolution layer
self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
if cond_channels > 0:
self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
if not conv_pre_weight_norm:
remove_parametrizations(self.conv_pre, "weight")
if not conv_post_weight_norm:
remove_parametrizations(self.conv_post, "weight")
if self.cond_in_each_up_layer:
self.conds = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
self.conds.append(nn.Conv1d(cond_channels, ch, 1))
def forward(self, x, g=None):
"""
Args:
x (Tensor): feature input tensor.
g (Tensor): global conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
o = self.conv_pre(x)
if hasattr(self, "cond_layer"):
o = o + self.cond_layer(g)
for i in range(self.num_upsamples):
o = F.leaky_relu(o, LRELU_SLOPE)
o = self.ups[i](o)
if self.cond_in_each_up_layer:
o = o + self.conds[i](g)
z_sum = None
for j in range(self.num_kernels):
if z_sum is None:
z_sum = self.resblocks[i * self.num_kernels + j](o)
else:
z_sum += self.resblocks[i * self.num_kernels + j](o)
o = z_sum / self.num_kernels
o = F.leaky_relu(o)
o = self.conv_post(o)
o = torch.tanh(o)
return o
@torch.no_grad()
def inference(self, c):
"""
Args:
x (Tensor): conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
c = c.to(self.conv_pre.weight.device)
c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
return self.forward(c)
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_parametrizations(l, "weight")
for l in self.resblocks:
l.remove_weight_norm()
remove_parametrizations(self.conv_pre, "weight")
remove_parametrizations(self.conv_post, "weight")
def load_checkpoint(
self, config, checkpoint_path, eval=False, cache=False
): # pylint: disable=unused-argument, redefined-builtin
state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training
self.remove_weight_norm()
class SELayer(nn.Module):
def __init__(self, channel, reduction=8):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel),
nn.Sigmoid(),
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
super(SEBasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def set_init_dict(model_dict, checkpoint_state, c):
# Partial initialization: if there is a mismatch with new and old layer, it is skipped.
for k, v in checkpoint_state.items():
if k not in model_dict:
print(" | > Layer missing in the model definition: {}".format(k))
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
# 2. filter out different size layers
pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
# 3. skip reinit layers
if c.has("reinit_layers") and c.reinit_layers is not None:
for reinit_layer_name in c.reinit_layers:
pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
# 4. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
print(" | > {} / {} layers are restored.".format(len(pretrained_dict), len(model_dict)))
return model_dict
class PreEmphasis(nn.Module):
def __init__(self, coefficient=0.97):
super().__init__()
self.coefficient = coefficient
self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
def forward(self, x):
assert len(x.size()) == 2
x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
class ResNetSpeakerEncoder(nn.Module):
"""This is copied from 🐸TTS to remove it from the dependencies."""
# pylint: disable=W0102
def __init__(
self,
input_dim=64,
proj_dim=512,
layers=[3, 4, 6, 3],
num_filters=[32, 64, 128, 256],
encoder_type="ASP",
log_input=False,
use_torch_spec=False,
audio_config=None,
):
super(ResNetSpeakerEncoder, self).__init__()
self.encoder_type = encoder_type
self.input_dim = input_dim
self.log_input = log_input
self.use_torch_spec = use_torch_spec
self.audio_config = audio_config
self.proj_dim = proj_dim
self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
self.bn1 = nn.BatchNorm2d(num_filters[0])
self.inplanes = num_filters[0]
self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
self.instancenorm = nn.InstanceNorm1d(input_dim)
if self.use_torch_spec:
self.torch_spec = torch.nn.Sequential(
PreEmphasis(audio_config["preemphasis"]),
torchaudio.transforms.MelSpectrogram(
sample_rate=audio_config["sample_rate"],
n_fft=audio_config["fft_size"],
win_length=audio_config["win_length"],
hop_length=audio_config["hop_length"],
window_fn=torch.hamming_window,
n_mels=audio_config["num_mels"],
),
)
else:
self.torch_spec = None
outmap_size = int(self.input_dim / 8)
self.attention = nn.Sequential(
nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
nn.Softmax(dim=2),
)
if self.encoder_type == "SAP":
out_dim = num_filters[3] * outmap_size
elif self.encoder_type == "ASP":
out_dim = num_filters[3] * outmap_size * 2
else:
raise ValueError("Undefined encoder")
self.fc = nn.Linear(out_dim, proj_dim)
self._init_layers()
def _init_layers(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def create_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
# pylint: disable=R0201
def new_parameter(self, *size):
out = nn.Parameter(torch.FloatTensor(*size))
nn.init.xavier_normal_(out)
return out
def forward(self, x, l2_norm=False):
"""Forward pass of the model.
Args:
x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
to compute the spectrogram on-the-fly.
l2_norm (bool): Whether to L2-normalize the outputs.
Shapes:
- x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
"""
x.squeeze_(1)
# if you torch spec compute it otherwise use the mel spec computed by the AP
if self.use_torch_spec:
x = self.torch_spec(x)
if self.log_input:
x = (x + 1e-6).log()
x = self.instancenorm(x).unsqueeze(1)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = x.reshape(x.size()[0], -1, x.size()[-1])
w = self.attention(x)
if self.encoder_type == "SAP":
x = torch.sum(x * w, dim=2)
elif self.encoder_type == "ASP":
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
x = torch.cat((mu, sg), 1)
x = x.view(x.size()[0], -1)
x = self.fc(x)
if l2_norm:
x = torch.nn.functional.normalize(x, p=2, dim=1)
return x
def load_checkpoint(
self,
checkpoint_path: str,
eval: bool = False,
use_cuda: bool = False,
criterion=None,
cache=False,
):
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
try:
self.load_state_dict(state["model"])
print(" > Model fully restored. ")
except (KeyError, RuntimeError) as error:
# If eval raise the error
if eval:
raise error
print(" > Partial model initialization.")
model_dict = self.state_dict()
model_dict = set_init_dict(model_dict, state["model"])
self.load_state_dict(model_dict)
del model_dict
# load the criterion for restore_path
if criterion is not None and "criterion" in state:
try:
criterion.load_state_dict(state["criterion"])
except (KeyError, RuntimeError) as error:
print(" > Criterion load ignored because of:", error)
if use_cuda:
self.cuda()
if criterion is not None:
criterion = criterion.cuda()
if eval:
self.eval()
assert not self.training
if not eval:
return criterion, state["step"]
return criterion
class HifiDecoder(torch.nn.Module):
def __init__(
self,
input_sample_rate=22050,
output_sample_rate=24000,
output_hop_length=256,
ar_mel_length_compression=1024,
decoder_input_dim=1024,
resblock_type_decoder="1",
resblock_dilation_sizes_decoder=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
resblock_kernel_sizes_decoder=[3, 7, 11],
upsample_rates_decoder=[8, 8, 2, 2],
upsample_initial_channel_decoder=512,
upsample_kernel_sizes_decoder=[16, 16, 4, 4],
d_vector_dim=512,
cond_d_vector_in_each_upsampling_layer=True,
speaker_encoder_audio_config={
"fft_size": 512,
"win_length": 400,
"hop_length": 160,
"sample_rate": 16000,
"preemphasis": 0.97,
"num_mels": 64,
},
):
super().__init__()
self.input_sample_rate = input_sample_rate
self.output_sample_rate = output_sample_rate
self.output_hop_length = output_hop_length
self.ar_mel_length_compression = ar_mel_length_compression
self.speaker_encoder_audio_config = speaker_encoder_audio_config
self.waveform_decoder = HifiganGenerator(
decoder_input_dim,
1,
resblock_type_decoder,
resblock_dilation_sizes_decoder,
resblock_kernel_sizes_decoder,
upsample_kernel_sizes_decoder,
upsample_initial_channel_decoder,
upsample_rates_decoder,
inference_padding=0,
cond_channels=d_vector_dim,
conv_pre_weight_norm=False,
conv_post_weight_norm=False,
conv_post_bias=False,
cond_in_each_up_layer=cond_d_vector_in_each_upsampling_layer,
)
self.speaker_encoder = ResNetSpeakerEncoder(
input_dim=64,
proj_dim=512,
log_input=True,
use_torch_spec=True,
audio_config=speaker_encoder_audio_config,
)
@property
def device(self):
return next(self.parameters()).device
def forward(self, latents, g=None):
"""
Args:
x (Tensor): feature input tensor (GPT latent).
g (Tensor): global conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
z = torch.nn.functional.interpolate(
latents.transpose(1, 2),
scale_factor=[self.ar_mel_length_compression / self.output_hop_length],
mode="linear",
).squeeze(1)
# upsample to the right sr
if self.output_sample_rate != self.input_sample_rate:
z = torch.nn.functional.interpolate(
z,
scale_factor=[self.output_sample_rate / self.input_sample_rate],
mode="linear",
).squeeze(0)
o = self.waveform_decoder(z, g=g)
return o
@torch.no_grad()
def inference(self, c, g):
"""
Args:
x (Tensor): feature input tensor (GPT latent).
g (Tensor): global conditioning input tensor.
Returns:
Tensor: output waveform.
Shapes:
x: [B, C, T]
Tensor: [B, 1, T]
"""
return self.forward(c, g=g)
def load_checkpoint(self, checkpoint_path, eval=False): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
# remove unused keys
state = state["model"]
states_keys = list(state.keys())
for key in states_keys:
if "waveform_decoder." not in key and "speaker_encoder." not in key:
del state[key]
self.load_state_dict(state)
if eval:
self.eval()
assert not self.training
self.waveform_decoder.remove_weight_norm()