mirror of
https://github.com/babysor/Realtime-Voice-Clone-Chinese.git
synced 2026-04-24 02:41:49 +08:00
Init ppg extractor and ppg2mel (#375)
* Init ppg extractor and ppg2mel * add preprocess and training * FIx known issues * Update __init__.py Allow to gen audio * Fix length issue * Fix bug of preparing fid * Fix sample issues * Add UI usage of PPG-vc
This commit is contained in:
Vega
217
ppg_extractor/encoder/encoder.py
Normal file
217
ppg_extractor/encoder/encoder.py
Normal file
@@ -0,0 +1,217 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
# Copyright 2019 Shigeki Karita
|
||||
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
"""Encoder definition."""
|
||||
|
||||
import logging
|
||||
import torch
|
||||
|
||||
from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
|
||||
from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
|
||||
from espnet.nets.pytorch_backend.nets_utils import get_activation
|
||||
from espnet.nets.pytorch_backend.transducer.vgg import VGG2L
|
||||
from espnet.nets.pytorch_backend.transformer.attention import (
|
||||
MultiHeadedAttention, # noqa: H301
|
||||
RelPositionMultiHeadedAttention, # noqa: H301
|
||||
)
|
||||
from espnet.nets.pytorch_backend.transformer.embedding import (
|
||||
PositionalEncoding, # noqa: H301
|
||||
ScaledPositionalEncoding, # noqa: H301
|
||||
RelPositionalEncoding, # noqa: H301
|
||||
)
|
||||
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
|
||||
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear
|
||||
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d
|
||||
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
|
||||
PositionwiseFeedForward, # noqa: H301
|
||||
)
|
||||
from espnet.nets.pytorch_backend.transformer.repeat import repeat
|
||||
from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
|
||||
|
||||
|
||||
class Encoder(torch.nn.Module):
|
||||
"""Conformer encoder module.
|
||||
|
||||
:param int idim: input dim
|
||||
:param int attention_dim: dimention of attention
|
||||
:param int attention_heads: the number of heads of multi head attention
|
||||
:param int linear_units: the number of units of position-wise feed forward
|
||||
:param int num_blocks: the number of decoder blocks
|
||||
:param float dropout_rate: dropout rate
|
||||
:param float attention_dropout_rate: dropout rate in attention
|
||||
:param float positional_dropout_rate: dropout rate after adding positional encoding
|
||||
:param str or torch.nn.Module input_layer: input layer type
|
||||
:param bool normalize_before: whether to use layer_norm before the first block
|
||||
:param bool concat_after: whether to concat attention layer's input and output
|
||||
if True, additional linear will be applied.
|
||||
i.e. x -> x + linear(concat(x, att(x)))
|
||||
if False, no additional linear will be applied. i.e. x -> x + att(x)
|
||||
:param str positionwise_layer_type: linear of conv1d
|
||||
:param int positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
|
||||
:param str encoder_pos_enc_layer_type: encoder positional encoding layer type
|
||||
:param str encoder_attn_layer_type: encoder attention layer type
|
||||
:param str activation_type: encoder activation function type
|
||||
:param bool macaron_style: whether to use macaron style for positionwise layer
|
||||
:param bool use_cnn_module: whether to use convolution module
|
||||
:param int cnn_module_kernel: kernerl size of convolution module
|
||||
:param int padding_idx: padding_idx for input_layer=embed
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
idim,
|
||||
attention_dim=256,
|
||||
attention_heads=4,
|
||||
linear_units=2048,
|
||||
num_blocks=6,
|
||||
dropout_rate=0.1,
|
||||
positional_dropout_rate=0.1,
|
||||
attention_dropout_rate=0.0,
|
||||
input_layer="conv2d",
|
||||
normalize_before=True,
|
||||
concat_after=False,
|
||||
positionwise_layer_type="linear",
|
||||
positionwise_conv_kernel_size=1,
|
||||
macaron_style=False,
|
||||
pos_enc_layer_type="abs_pos",
|
||||
selfattention_layer_type="selfattn",
|
||||
activation_type="swish",
|
||||
use_cnn_module=False,
|
||||
cnn_module_kernel=31,
|
||||
padding_idx=-1,
|
||||
):
|
||||
"""Construct an Encoder object."""
|
||||
super(Encoder, self).__init__()
|
||||
|
||||
activation = get_activation(activation_type)
|
||||
if pos_enc_layer_type == "abs_pos":
|
||||
pos_enc_class = PositionalEncoding
|
||||
elif pos_enc_layer_type == "scaled_abs_pos":
|
||||
pos_enc_class = ScaledPositionalEncoding
|
||||
elif pos_enc_layer_type == "rel_pos":
|
||||
assert selfattention_layer_type == "rel_selfattn"
|
||||
pos_enc_class = RelPositionalEncoding
|
||||
else:
|
||||
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
|
||||
|
||||
if input_layer == "linear":
|
||||
self.embed = torch.nn.Sequential(
|
||||
torch.nn.Linear(idim, attention_dim),
|
||||
torch.nn.LayerNorm(attention_dim),
|
||||
torch.nn.Dropout(dropout_rate),
|
||||
pos_enc_class(attention_dim, positional_dropout_rate),
|
||||
)
|
||||
elif input_layer == "conv2d":
|
||||
self.embed = Conv2dSubsampling(
|
||||
idim,
|
||||
attention_dim,
|
||||
dropout_rate,
|
||||
pos_enc_class(attention_dim, positional_dropout_rate),
|
||||
)
|
||||
elif input_layer == "vgg2l":
|
||||
self.embed = VGG2L(idim, attention_dim)
|
||||
elif input_layer == "embed":
|
||||
self.embed = torch.nn.Sequential(
|
||||
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
|
||||
pos_enc_class(attention_dim, positional_dropout_rate),
|
||||
)
|
||||
elif isinstance(input_layer, torch.nn.Module):
|
||||
self.embed = torch.nn.Sequential(
|
||||
input_layer,
|
||||
pos_enc_class(attention_dim, positional_dropout_rate),
|
||||
)
|
||||
elif input_layer is None:
|
||||
self.embed = torch.nn.Sequential(
|
||||
pos_enc_class(attention_dim, positional_dropout_rate)
|
||||
)
|
||||
else:
|
||||
raise ValueError("unknown input_layer: " + input_layer)
|
||||
self.normalize_before = normalize_before
|
||||
if positionwise_layer_type == "linear":
|
||||
positionwise_layer = PositionwiseFeedForward
|
||||
positionwise_layer_args = (
|
||||
attention_dim,
|
||||
linear_units,
|
||||
dropout_rate,
|
||||
activation,
|
||||
)
|
||||
elif positionwise_layer_type == "conv1d":
|
||||
positionwise_layer = MultiLayeredConv1d
|
||||
positionwise_layer_args = (
|
||||
attention_dim,
|
||||
linear_units,
|
||||
positionwise_conv_kernel_size,
|
||||
dropout_rate,
|
||||
)
|
||||
elif positionwise_layer_type == "conv1d-linear":
|
||||
positionwise_layer = Conv1dLinear
|
||||
positionwise_layer_args = (
|
||||
attention_dim,
|
||||
linear_units,
|
||||
positionwise_conv_kernel_size,
|
||||
dropout_rate,
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError("Support only linear or conv1d.")
|
||||
|
||||
if selfattention_layer_type == "selfattn":
|
||||
logging.info("encoder self-attention layer type = self-attention")
|
||||
encoder_selfattn_layer = MultiHeadedAttention
|
||||
encoder_selfattn_layer_args = (
|
||||
attention_heads,
|
||||
attention_dim,
|
||||
attention_dropout_rate,
|
||||
)
|
||||
elif selfattention_layer_type == "rel_selfattn":
|
||||
assert pos_enc_layer_type == "rel_pos"
|
||||
encoder_selfattn_layer = RelPositionMultiHeadedAttention
|
||||
encoder_selfattn_layer_args = (
|
||||
attention_heads,
|
||||
attention_dim,
|
||||
attention_dropout_rate,
|
||||
)
|
||||
else:
|
||||
raise ValueError("unknown encoder_attn_layer: " + selfattention_layer_type)
|
||||
|
||||
convolution_layer = ConvolutionModule
|
||||
convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
|
||||
|
||||
self.encoders = repeat(
|
||||
num_blocks,
|
||||
lambda lnum: EncoderLayer(
|
||||
attention_dim,
|
||||
encoder_selfattn_layer(*encoder_selfattn_layer_args),
|
||||
positionwise_layer(*positionwise_layer_args),
|
||||
positionwise_layer(*positionwise_layer_args) if macaron_style else None,
|
||||
convolution_layer(*convolution_layer_args) if use_cnn_module else None,
|
||||
dropout_rate,
|
||||
normalize_before,
|
||||
concat_after,
|
||||
),
|
||||
)
|
||||
if self.normalize_before:
|
||||
self.after_norm = LayerNorm(attention_dim)
|
||||
|
||||
def forward(self, xs, masks):
|
||||
"""Encode input sequence.
|
||||
|
||||
:param torch.Tensor xs: input tensor
|
||||
:param torch.Tensor masks: input mask
|
||||
:return: position embedded tensor and mask
|
||||
:rtype Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
if isinstance(self.embed, (Conv2dSubsampling, VGG2L)):
|
||||
xs, masks = self.embed(xs, masks)
|
||||
else:
|
||||
xs = self.embed(xs)
|
||||
|
||||
xs, masks = self.encoders(xs, masks)
|
||||
if isinstance(xs, tuple):
|
||||
xs = xs[0]
|
||||
|
||||
if self.normalize_before:
|
||||
xs = self.after_norm(xs)
|
||||
return xs, masks
|
||||
Reference in New Issue
Block a user