You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
202 lines
8.0 KiB
202 lines
8.0 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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)
|
|
from typing import Any
|
|
from typing import Dict
|
|
from typing import List
|
|
from typing import Optional
|
|
|
|
from paddle import nn
|
|
from paddle.nn import functional as F
|
|
|
|
from paddlespeech.t2s.modules.activation import get_activation
|
|
|
|
|
|
class Stretch2D(nn.Layer):
|
|
def __init__(self, w_scale: int, h_scale: int, mode: str="nearest"):
|
|
"""Strech an image (or image-like object) with some interpolation.
|
|
|
|
Args:
|
|
w_scale (int):
|
|
Scalar of width.
|
|
h_scale (int):
|
|
Scalar of the height.
|
|
mode (str, optional):
|
|
Interpolation mode, modes suppored are "nearest", "bilinear",
|
|
"trilinear", "bicubic", "linear" and "area",by default "nearest"
|
|
For more details about interpolation, see
|
|
`paddle.nn.functional.interpolate <https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/nn/functional/interpolate_en.html>`_.
|
|
"""
|
|
super().__init__()
|
|
self.w_scale = w_scale
|
|
self.h_scale = h_scale
|
|
self.mode = mode
|
|
|
|
def forward(self, x):
|
|
"""
|
|
|
|
Args:
|
|
x (Tensor):
|
|
Shape (N, C, H, W)
|
|
|
|
Returns:
|
|
Tensor:
|
|
The stretched image. Shape (N, C, H', W'), where ``H'=h_scale * H``, ``W'=w_scale * W``.
|
|
|
|
"""
|
|
out = F.interpolate(
|
|
x, scale_factor=(self.h_scale, self.w_scale), mode=self.mode)
|
|
return out
|
|
|
|
|
|
class UpsampleNet(nn.Layer):
|
|
"""A Layer to upsample spectrogram by applying consecutive stretch and
|
|
convolutions.
|
|
|
|
Args:
|
|
upsample_scales (List[int]):
|
|
Upsampling factors for each strech.
|
|
nonlinear_activation (Optional[str], optional):
|
|
Activation after each convolution, by default None
|
|
nonlinear_activation_params (Dict[str, Any], optional):
|
|
Parameters passed to construct the activation, by default {}
|
|
interpolate_mode (str, optional):
|
|
Interpolation mode of the strech, by default "nearest"
|
|
freq_axis_kernel_size (int, optional):
|
|
Convolution kernel size along the frequency axis, by default 1
|
|
use_causal_conv (bool, optional):
|
|
Whether to use causal padding before convolution, by default False
|
|
If True, Causal padding is used along the time axis,
|
|
i.e. padding amount is ``receptive field - 1`` and 0 for before and after, respectively.
|
|
If False, "same" padding is used along the time axis.
|
|
"""
|
|
|
|
def __init__(self,
|
|
upsample_scales: List[int],
|
|
nonlinear_activation: Optional[str]=None,
|
|
nonlinear_activation_params: Dict[str, Any]={},
|
|
interpolate_mode: str="nearest",
|
|
freq_axis_kernel_size: int=1,
|
|
use_causal_conv: bool=False):
|
|
super().__init__()
|
|
self.use_causal_conv = use_causal_conv
|
|
self.up_layers = nn.LayerList()
|
|
|
|
for scale in upsample_scales:
|
|
stretch = Stretch2D(scale, 1, interpolate_mode)
|
|
assert freq_axis_kernel_size % 2 == 1
|
|
freq_axis_padding = (freq_axis_kernel_size - 1) // 2
|
|
kernel_size = (freq_axis_kernel_size, scale * 2 + 1)
|
|
if use_causal_conv:
|
|
padding = (freq_axis_padding, scale * 2)
|
|
else:
|
|
padding = (freq_axis_padding, scale)
|
|
conv = nn.Conv2D(
|
|
1, 1, kernel_size, padding=padding, bias_attr=False)
|
|
self.up_layers.extend([stretch, conv])
|
|
if nonlinear_activation is not None:
|
|
# for compatibility
|
|
nonlinear_activation = nonlinear_activation.lower()
|
|
|
|
nonlinear = get_activation(nonlinear_activation,
|
|
**nonlinear_activation_params)
|
|
self.up_layers.append(nonlinear)
|
|
|
|
def forward(self, c):
|
|
"""
|
|
Args:
|
|
c (Tensor):
|
|
spectrogram. Shape (N, F, T)
|
|
|
|
Returns:
|
|
Tensor: upsampled spectrogram.
|
|
Shape (N, F, T'), where ``T' = upsample_factor * T``,
|
|
"""
|
|
c = c.unsqueeze(1)
|
|
for f in self.up_layers:
|
|
if self.use_causal_conv and isinstance(f, nn.Conv2D):
|
|
c = f(c)[:, :, :, c.shape[-1]]
|
|
else:
|
|
c = f(c)
|
|
return c.squeeze(1)
|
|
|
|
|
|
class ConvInUpsampleNet(nn.Layer):
|
|
"""A Layer to upsample spectrogram composed of a convolution and an
|
|
UpsampleNet.
|
|
|
|
Args:
|
|
upsample_scales (List[int]):
|
|
Upsampling factors for each strech.
|
|
nonlinear_activation (Optional[str], optional):
|
|
Activation after each convolution, by default None
|
|
nonlinear_activation_params (Dict[str, Any], optional):
|
|
Parameters passed to construct the activation, by default {}
|
|
interpolate_mode (str, optional):
|
|
Interpolation mode of the strech, by default "nearest"
|
|
freq_axis_kernel_size (int, optional):
|
|
Convolution kernel size along the frequency axis, by default 1
|
|
aux_channels (int, optional):
|
|
Feature size of the input, by default 80
|
|
aux_context_window (int, optional):
|
|
Context window of the first 1D convolution applied to the input. It
|
|
related to the kernel size of the convolution, by default 0
|
|
If use causal convolution, the kernel size is ``window + 1``,
|
|
else the kernel size is ``2 * window + 1``.
|
|
use_causal_conv (bool, optional):
|
|
Whether to use causal padding before convolution, by default False
|
|
If True, Causal padding is used along the time axis, i.e. padding
|
|
amount is ``receptive field - 1`` and 0 for before and after, respectively.
|
|
If False, "same" padding is used along the time axis.
|
|
"""
|
|
|
|
def __init__(self,
|
|
upsample_scales: List[int],
|
|
nonlinear_activation: Optional[str]=None,
|
|
nonlinear_activation_params: Dict[str, Any]={},
|
|
interpolate_mode: str="nearest",
|
|
freq_axis_kernel_size: int=1,
|
|
aux_channels: int=80,
|
|
aux_context_window: int=0,
|
|
use_causal_conv: bool=False):
|
|
super().__init__()
|
|
self.aux_context_window = aux_context_window
|
|
self.use_causal_conv = use_causal_conv and aux_context_window > 0
|
|
kernel_size = aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1
|
|
self.conv_in = nn.Conv1D(
|
|
aux_channels,
|
|
aux_channels,
|
|
kernel_size=kernel_size,
|
|
bias_attr=False)
|
|
self.upsample = UpsampleNet(
|
|
upsample_scales=upsample_scales,
|
|
nonlinear_activation=nonlinear_activation,
|
|
nonlinear_activation_params=nonlinear_activation_params,
|
|
interpolate_mode=interpolate_mode,
|
|
freq_axis_kernel_size=freq_axis_kernel_size,
|
|
use_causal_conv=use_causal_conv)
|
|
|
|
def forward(self, c):
|
|
"""
|
|
Args:
|
|
c (Tensor):
|
|
spectrogram. Shape (N, F, T)
|
|
|
|
Returns:
|
|
Tensors: upsampled spectrogram. Shape (N, F, T'), where ``T' = upsample_factor * T``,
|
|
"""
|
|
c_ = self.conv_in(c)
|
|
c = c_[:, :, :-self.aux_context_window] if self.use_causal_conv else c_
|
|
return self.upsample(c)
|