mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
258 lines
9.5 KiB
Python
258 lines
9.5 KiB
Python
# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
|
|
# 2024 Alibaba Inc (Xiang Lyu)
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# Modified from ESPnet(https://github.com/espnet/espnet)
|
|
"""ConvolutionModule definition."""
|
|
|
|
from typing import Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class ConvolutionModule(nn.Module):
|
|
"""ConvolutionModule in Conformer model."""
|
|
|
|
def __init__(self,
|
|
channels: int,
|
|
kernel_size: int = 15,
|
|
activation: nn.Module = nn.ReLU(),
|
|
norm: str = "batch_norm",
|
|
causal: bool = False,
|
|
bias: bool = True):
|
|
"""Construct an ConvolutionModule object.
|
|
Args:
|
|
channels (int): The number of channels of conv layers.
|
|
kernel_size (int): Kernel size of conv layers.
|
|
causal (int): Whether use causal convolution or not
|
|
"""
|
|
super().__init__()
|
|
|
|
self.pointwise_conv1 = nn.Conv1d(
|
|
channels,
|
|
2 * channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias=bias,
|
|
)
|
|
# self.lorder is used to distinguish if it's a causal convolution,
|
|
# if self.lorder > 0: it's a causal convolution, the input will be
|
|
# padded with self.lorder frames on the left in forward.
|
|
# else: it's a symmetrical convolution
|
|
if causal:
|
|
padding = 0
|
|
self.lorder = kernel_size - 1
|
|
else:
|
|
# kernel_size should be an odd number for none causal convolution
|
|
assert (kernel_size - 1) % 2 == 0
|
|
padding = (kernel_size - 1) // 2
|
|
self.lorder = 0
|
|
self.depthwise_conv = nn.Conv1d(
|
|
channels,
|
|
channels,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=padding,
|
|
groups=channels,
|
|
bias=bias,
|
|
)
|
|
|
|
assert norm in ['batch_norm', 'layer_norm']
|
|
if norm == "batch_norm":
|
|
self.use_layer_norm = False
|
|
self.norm = nn.BatchNorm1d(channels)
|
|
else:
|
|
self.use_layer_norm = True
|
|
self.norm = nn.LayerNorm(channels)
|
|
|
|
self.pointwise_conv2 = nn.Conv1d(
|
|
channels,
|
|
channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias=bias,
|
|
)
|
|
self.activation = activation
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
|
cache: torch.Tensor = torch.zeros((0, 0, 0)),
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Compute convolution module.
|
|
Args:
|
|
x (torch.Tensor): Input tensor (#batch, time, channels).
|
|
mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
|
|
(0, 0, 0) means fake mask.
|
|
cache (torch.Tensor): left context cache, it is only
|
|
used in causal convolution (#batch, channels, cache_t),
|
|
(0, 0, 0) meas fake cache.
|
|
Returns:
|
|
torch.Tensor: Output tensor (#batch, time, channels).
|
|
"""
|
|
# exchange the temporal dimension and the feature dimension
|
|
x = x.transpose(1, 2) # (#batch, channels, time)
|
|
|
|
# mask batch padding
|
|
if mask_pad.size(2) > 0: # time > 0
|
|
x.masked_fill_(~mask_pad, 0.0)
|
|
|
|
if self.lorder > 0:
|
|
if cache.size(2) == 0: # cache_t == 0
|
|
x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
|
|
else:
|
|
assert cache.size(0) == x.size(0) # equal batch
|
|
assert cache.size(1) == x.size(1) # equal channel
|
|
x = torch.cat((cache, x), dim=2)
|
|
assert (x.size(2) > self.lorder)
|
|
new_cache = x[:, :, -self.lorder:]
|
|
else:
|
|
# It's better we just return None if no cache is required,
|
|
# However, for JIT export, here we just fake one tensor instead of
|
|
# None.
|
|
new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
|
|
|
|
# GLU mechanism
|
|
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
|
|
x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
|
|
|
|
# 1D Depthwise Conv
|
|
x = self.depthwise_conv(x)
|
|
if self.use_layer_norm:
|
|
x = x.transpose(1, 2)
|
|
x = self.activation(self.norm(x))
|
|
if self.use_layer_norm:
|
|
x = x.transpose(1, 2)
|
|
x = self.pointwise_conv2(x)
|
|
# mask batch padding
|
|
if mask_pad.size(2) > 0: # time > 0
|
|
x.masked_fill_(~mask_pad, 0.0)
|
|
|
|
return x.transpose(1, 2), new_cache
|
|
|
|
|
|
# NOTE(Xiang Lyu) causal conv module used in convolution-based vocoder
|
|
class CausalConv1d(torch.nn.Conv1d):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: int,
|
|
stride: int = 1,
|
|
dilation: int = 1,
|
|
groups: int = 1,
|
|
bias: bool = True,
|
|
padding_mode: str = 'zeros',
|
|
causal_type: str = 'left',
|
|
device=None,
|
|
dtype=None
|
|
) -> None:
|
|
super(CausalConv1d, self).__init__(in_channels, out_channels,
|
|
kernel_size, stride=1,
|
|
padding=0, dilation=dilation,
|
|
groups=groups, bias=bias,
|
|
padding_mode=padding_mode,
|
|
device=device, dtype=dtype)
|
|
assert stride == 1
|
|
self.causal_padding = int((kernel_size * dilation - dilation) / 2) * 2 + (kernel_size + 1) % 2
|
|
assert causal_type in ['left', 'right']
|
|
self.causal_type = causal_type
|
|
|
|
def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor]:
|
|
input_timestep = x.shape[2]
|
|
if cache.size(2) == 0:
|
|
cache = torch.zeros(x.shape[0], x.shape[1], self.causal_padding).to(x)
|
|
assert cache.size(2) == self.causal_padding
|
|
if self.causal_type == 'left':
|
|
x = torch.concat([cache, x], dim=2)
|
|
else:
|
|
x = torch.concat([x, cache], dim=2)
|
|
x = super(CausalConv1d, self).forward(x)
|
|
assert x.shape[2] == input_timestep
|
|
return x
|
|
|
|
|
|
class CausalConv1dDownSample(torch.nn.Conv1d):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: int,
|
|
stride: int = 1,
|
|
dilation: int = 1,
|
|
groups: int = 1,
|
|
bias: bool = True,
|
|
padding_mode: str = 'zeros',
|
|
device=None,
|
|
dtype=None
|
|
) -> None:
|
|
super(CausalConv1dDownSample, self).__init__(in_channels, out_channels,
|
|
kernel_size, stride,
|
|
padding=0, dilation=dilation,
|
|
groups=groups, bias=bias,
|
|
padding_mode=padding_mode,
|
|
device=device, dtype=dtype)
|
|
assert stride != 1 and dilation == 1
|
|
assert kernel_size % stride == 0
|
|
self.causal_padding = stride - 1
|
|
|
|
def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if cache.size(2) == 0:
|
|
x = F.pad(x, (self.causal_padding, 0), value=0.0)
|
|
else:
|
|
assert cache.size(2) == self.causal_padding
|
|
x = torch.concat([cache, x], dim=2)
|
|
x = super(CausalConv1dDownSample, self).forward(x)
|
|
return x
|
|
|
|
|
|
class CausalConv1dUpsample(torch.nn.Conv1d):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
kernel_size: int,
|
|
stride: int = 1,
|
|
dilation: int = 1,
|
|
groups: int = 1,
|
|
bias: bool = True,
|
|
padding_mode: str = 'zeros',
|
|
device=None,
|
|
dtype=None
|
|
) -> None:
|
|
super(CausalConv1dUpsample, self).__init__(in_channels, out_channels,
|
|
kernel_size, 1,
|
|
padding=0, dilation=dilation,
|
|
groups=groups, bias=bias,
|
|
padding_mode=padding_mode,
|
|
device=device, dtype=dtype)
|
|
assert dilation == 1
|
|
self.causal_padding = kernel_size - 1
|
|
self.upsample = torch.nn.Upsample(scale_factor=stride, mode='nearest')
|
|
|
|
def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
x = self.upsample(x)
|
|
input_timestep = x.shape[2]
|
|
if cache.size(2) == 0:
|
|
x = F.pad(x, (self.causal_padding, 0), value=0.0)
|
|
else:
|
|
assert cache.size(2) == self.causal_padding
|
|
x = torch.concat([cache, x], dim=2)
|
|
x = super(CausalConv1dUpsample, self).forward(x)
|
|
assert input_timestep == x.shape[2]
|
|
return x |