[Model] Add IndexTTS2 text-to-speech support (#3838)
Signed-off-by: BeatSeat <wendavid552@gmail.com> Signed-off-by: David <david@podcast.ai> Signed-off-by: wendavid552 <wendavid552@gmail.com> Signed-off-by: WenDavid <davidzhangcf@foxmail.com>
This commit is contained in:
BeatSeat authored and GitHub committed
1 parent
f4d922d8f6
commit
044240d8eb
66 files changed
+8634
-2
No files matched your search
@@ -0,0 +1,2 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
@@ -0,0 +1,117 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from .layers import (
|
||||
BasicResBlock,
|
||||
CAMDenseTDNNBlock,
|
||||
DenseLayer,
|
||||
StatsPool,
|
||||
TDNNLayer,
|
||||
TransitLayer,
|
||||
get_nonlinear,
|
||||
)
|
||||
|
||||
|
||||
class FCM(nn.Module):
|
||||
def __init__(self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80):
|
||||
super().__init__()
|
||||
self.in_planes = m_channels
|
||||
self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(m_channels)
|
||||
|
||||
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
|
||||
self.layer2 = self._make_layer(block, m_channels, num_blocks[1], stride=2)
|
||||
|
||||
self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(m_channels)
|
||||
self.out_channels = m_channels * (feat_dim // 8)
|
||||
|
||||
def _make_layer(self, block, planes, num_blocks, stride):
|
||||
strides = [stride] + [1] * (num_blocks - 1)
|
||||
layers = []
|
||||
for stride in strides:
|
||||
layers.append(block(self.in_planes, planes, stride))
|
||||
self.in_planes = planes * block.expansion
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.unsqueeze(1)
|
||||
out = F.relu(self.bn1(self.conv1(x)))
|
||||
out = self.layer1(out)
|
||||
out = self.layer2(out)
|
||||
out = F.relu(self.bn2(self.conv2(out)))
|
||||
|
||||
shape = out.shape
|
||||
out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
|
||||
return out
|
||||
|
||||
|
||||
class CAMPPlus(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
feat_dim=80,
|
||||
embedding_size=512,
|
||||
growth_rate=32,
|
||||
bn_size=4,
|
||||
init_channels=128,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.head = FCM(feat_dim=feat_dim)
|
||||
channels = self.head.out_channels
|
||||
|
||||
self.xvector = nn.Sequential(
|
||||
OrderedDict(
|
||||
[
|
||||
(
|
||||
"tdnn",
|
||||
TDNNLayer(channels, init_channels, 5, stride=2, dilation=1, padding=-1, config_str=config_str),
|
||||
),
|
||||
]
|
||||
)
|
||||
)
|
||||
channels = init_channels
|
||||
for i, (num_layers, kernel_size, dilation) in enumerate(zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
|
||||
block = CAMDenseTDNNBlock(
|
||||
num_layers=num_layers,
|
||||
in_channels=channels,
|
||||
out_channels=growth_rate,
|
||||
bn_channels=bn_size * growth_rate,
|
||||
kernel_size=kernel_size,
|
||||
dilation=dilation,
|
||||
config_str=config_str,
|
||||
memory_efficient=memory_efficient,
|
||||
)
|
||||
self.xvector.add_module(f"block{i + 1}", block)
|
||||
channels = channels + num_layers * growth_rate
|
||||
self.xvector.add_module(
|
||||
f"transit{i + 1}", TransitLayer(channels, channels // 2, bias=False, config_str=config_str)
|
||||
)
|
||||
channels //= 2
|
||||
|
||||
self.xvector.add_module("out_nonlinear", get_nonlinear(config_str, channels))
|
||||
|
||||
self.xvector.add_module("stats", StatsPool())
|
||||
self.xvector.add_module("dense", DenseLayer(channels * 2, embedding_size, config_str="batchnorm_"))
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, (nn.Conv1d, nn.Linear)):
|
||||
nn.init.kaiming_normal_(m.weight.data)
|
||||
if m.bias is not None:
|
||||
nn.init.zeros_(m.bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
|
||||
x = self.head(x)
|
||||
x = self.xvector(x)
|
||||
return x
|
||||
@@ -0,0 +1,221 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
|
||||
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as cp
|
||||
from torch import nn
|
||||
|
||||
|
||||
def get_nonlinear(config_str, channels):
|
||||
nonlinear = nn.Sequential()
|
||||
for name in config_str.split("-"):
|
||||
if name == "relu":
|
||||
nonlinear.add_module("relu", nn.ReLU(inplace=True))
|
||||
elif name == "prelu":
|
||||
nonlinear.add_module("prelu", nn.PReLU(channels))
|
||||
elif name == "batchnorm":
|
||||
nonlinear.add_module("batchnorm", nn.BatchNorm1d(channels))
|
||||
elif name == "batchnorm_":
|
||||
nonlinear.add_module("batchnorm", nn.BatchNorm1d(channels, affine=False))
|
||||
else:
|
||||
raise ValueError(f"Unexpected module ({name}).")
|
||||
return nonlinear
|
||||
|
||||
|
||||
def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
|
||||
mean = x.mean(dim=dim)
|
||||
std = x.std(dim=dim, unbiased=unbiased)
|
||||
stats = torch.cat([mean, std], dim=-1)
|
||||
if keepdim:
|
||||
stats = stats.unsqueeze(dim=dim)
|
||||
return stats
|
||||
|
||||
|
||||
class StatsPool(nn.Module):
|
||||
def forward(self, x):
|
||||
return statistics_pooling(x)
|
||||
|
||||
|
||||
class TDNNLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
padding=0,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
):
|
||||
super().__init__()
|
||||
if padding < 0:
|
||||
assert kernel_size % 2 == 1, f"Expect equal paddings, but got even kernel size ({kernel_size})"
|
||||
padding = (kernel_size - 1) // 2 * dilation
|
||||
self.linear = nn.Conv1d(
|
||||
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias
|
||||
)
|
||||
self.nonlinear = get_nonlinear(config_str, out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear(x)
|
||||
x = self.nonlinear(x)
|
||||
return x
|
||||
|
||||
|
||||
class CAMLayer(nn.Module):
|
||||
def __init__(self, bn_channels, out_channels, kernel_size, stride, padding, dilation, bias, reduction=2):
|
||||
super().__init__()
|
||||
self.linear_local = nn.Conv1d(
|
||||
bn_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias
|
||||
)
|
||||
self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
|
||||
def forward(self, x):
|
||||
y = self.linear_local(x)
|
||||
context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
|
||||
context = self.relu(self.linear1(context))
|
||||
m = self.sigmoid(self.linear2(context))
|
||||
return y * m
|
||||
|
||||
def seg_pooling(self, x, seg_len=100, stype="avg"):
|
||||
if stype == "avg":
|
||||
seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
||||
elif stype == "max":
|
||||
seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
|
||||
else:
|
||||
raise ValueError("Wrong segment pooling type.")
|
||||
shape = seg.shape
|
||||
seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
|
||||
seg = seg[..., : x.shape[-1]]
|
||||
return seg
|
||||
|
||||
|
||||
class CAMDenseTDNNLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
bn_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=False,
|
||||
):
|
||||
super().__init__()
|
||||
assert kernel_size % 2 == 1, f"Expect equal paddings, but got even kernel size ({kernel_size})"
|
||||
padding = (kernel_size - 1) // 2 * dilation
|
||||
self.memory_efficient = memory_efficient
|
||||
self.nonlinear1 = get_nonlinear(config_str, in_channels)
|
||||
self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
|
||||
self.nonlinear2 = get_nonlinear(config_str, bn_channels)
|
||||
self.cam_layer = CAMLayer(
|
||||
bn_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias
|
||||
)
|
||||
|
||||
def bn_function(self, x):
|
||||
return self.linear1(self.nonlinear1(x))
|
||||
|
||||
def forward(self, x):
|
||||
if self.training and self.memory_efficient:
|
||||
x = cp.checkpoint(self.bn_function, x)
|
||||
else:
|
||||
x = self.bn_function(x)
|
||||
x = self.cam_layer(self.nonlinear2(x))
|
||||
return x
|
||||
|
||||
|
||||
class CAMDenseTDNNBlock(nn.ModuleList):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers,
|
||||
in_channels,
|
||||
out_channels,
|
||||
bn_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
bias=False,
|
||||
config_str="batchnorm-relu",
|
||||
memory_efficient=False,
|
||||
):
|
||||
super().__init__()
|
||||
for i in range(num_layers):
|
||||
layer = CAMDenseTDNNLayer(
|
||||
in_channels=in_channels + i * out_channels,
|
||||
out_channels=out_channels,
|
||||
bn_channels=bn_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
bias=bias,
|
||||
config_str=config_str,
|
||||
memory_efficient=memory_efficient,
|
||||
)
|
||||
self.add_module(f"tdnnd{i + 1}", layer)
|
||||
|
||||
def forward(self, x):
|
||||
for layer in self:
|
||||
x = torch.cat([x, layer(x)], dim=1)
|
||||
return x
|
||||
|
||||
|
||||
class TransitLayer(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, bias=True, config_str="batchnorm-relu"):
|
||||
super().__init__()
|
||||
self.nonlinear = get_nonlinear(config_str, in_channels)
|
||||
self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.nonlinear(x)
|
||||
x = self.linear(x)
|
||||
return x
|
||||
|
||||
|
||||
class DenseLayer(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, bias=False, config_str="batchnorm-relu"):
|
||||
super().__init__()
|
||||
self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
|
||||
self.nonlinear = get_nonlinear(config_str, out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
if len(x.shape) == 2:
|
||||
x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
|
||||
else:
|
||||
x = self.linear(x)
|
||||
x = self.nonlinear(x)
|
||||
return x
|
||||
|
||||
|
||||
class BasicResBlock(nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, in_planes, planes, stride=1):
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(planes)
|
||||
|
||||
self.shortcut = nn.Sequential()
|
||||
if stride != 1 or in_planes != self.expansion * planes:
|
||||
self.shortcut = nn.Sequential(
|
||||
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=(stride, 1), bias=False),
|
||||
nn.BatchNorm2d(self.expansion * planes),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
out = F.relu(self.bn1(self.conv1(x)))
|
||||
out = self.bn2(self.conv2(out))
|
||||
out += self.shortcut(x)
|
||||
out = F.relu(out)
|
||||
return out
|
||||
Reference in new issue
Block a user