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voice-clone with single audio sample input
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import math
import torch
from torch import nn
from TTS.tts.layers.generic.gated_conv import GatedConvBlock
from TTS.tts.layers.generic.res_conv_bn import ResidualConv1dBNBlock
from TTS.tts.layers.generic.time_depth_sep_conv import TimeDepthSeparableConvBlock
from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
from TTS.tts.layers.glow_tts.glow import ResidualConv1dLayerNormBlock
from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
from TTS.tts.utils.helpers import sequence_mask
class Encoder(nn.Module):
"""Glow-TTS encoder module.
::
embedding -> <prenet> -> encoder_module -> <postnet> --> proj_mean
|
|-> proj_var
|
|-> concat -> duration_predictor
speaker_embed
Args:
num_chars (int): number of characters.
out_channels (int): number of output channels.
hidden_channels (int): encoder's embedding size.
hidden_channels_ffn (int): transformer's feed-forward channels.
kernel_size (int): kernel size for conv layers and duration predictor.
dropout_p (float): dropout rate for any dropout layer.
mean_only (bool): if True, output only mean values and use constant std.
use_prenet (bool): if True, use pre-convolutional layers before transformer layers.
c_in_channels (int): number of channels in conditional input.
Shapes:
- input: (B, T, C)
::
suggested encoder params...
for encoder_type == 'rel_pos_transformer'
encoder_params={
'kernel_size':3,
'dropout_p': 0.1,
'num_layers': 6,
'num_heads': 2,
'hidden_channels_ffn': 768, # 4 times the hidden_channels
'input_length': None
}
for encoder_type == 'gated_conv'
encoder_params={
'kernel_size':5,
'dropout_p': 0.1,
'num_layers': 9,
}
for encoder_type == 'residual_conv_bn'
encoder_params={
"kernel_size": 4,
"dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
"num_conv_blocks": 2,
"num_res_blocks": 13
}
for encoder_type == 'time_depth_separable'
encoder_params={
"kernel_size": 5,
'num_layers': 9,
}
"""
def __init__(
self,
num_chars,
out_channels,
hidden_channels,
hidden_channels_dp,
encoder_type,
encoder_params,
dropout_p_dp=0.1,
mean_only=False,
use_prenet=True,
c_in_channels=0,
):
super().__init__()
# class arguments
self.num_chars = num_chars
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.hidden_channels_dp = hidden_channels_dp
self.dropout_p_dp = dropout_p_dp
self.mean_only = mean_only
self.use_prenet = use_prenet
self.c_in_channels = c_in_channels
self.encoder_type = encoder_type
# embedding layer
self.emb = nn.Embedding(num_chars, hidden_channels)
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
# init encoder module
if encoder_type.lower() == "rel_pos_transformer":
if use_prenet:
self.prenet = ResidualConv1dLayerNormBlock(
hidden_channels, hidden_channels, hidden_channels, kernel_size=5, num_layers=3, dropout_p=0.5
)
self.encoder = RelativePositionTransformer(
hidden_channels, hidden_channels, hidden_channels, **encoder_params
)
elif encoder_type.lower() == "gated_conv":
self.encoder = GatedConvBlock(hidden_channels, **encoder_params)
elif encoder_type.lower() == "residual_conv_bn":
if use_prenet:
self.prenet = nn.Sequential(nn.Conv1d(hidden_channels, hidden_channels, 1), nn.ReLU())
self.encoder = ResidualConv1dBNBlock(hidden_channels, hidden_channels, hidden_channels, **encoder_params)
self.postnet = nn.Sequential(
nn.Conv1d(self.hidden_channels, self.hidden_channels, 1), nn.BatchNorm1d(self.hidden_channels)
)
elif encoder_type.lower() == "time_depth_separable":
if use_prenet:
self.prenet = ResidualConv1dLayerNormBlock(
hidden_channels, hidden_channels, hidden_channels, kernel_size=5, num_layers=3, dropout_p=0.5
)
self.encoder = TimeDepthSeparableConvBlock(
hidden_channels, hidden_channels, hidden_channels, **encoder_params
)
else:
raise ValueError(" [!] Unkown encoder type.")
# final projection layers
self.proj_m = nn.Conv1d(hidden_channels, out_channels, 1)
if not mean_only:
self.proj_s = nn.Conv1d(hidden_channels, out_channels, 1)
# duration predictor
self.duration_predictor = DurationPredictor(
hidden_channels + c_in_channels, hidden_channels_dp, 3, dropout_p_dp
)
def forward(self, x, x_lengths, g=None):
"""
Shapes:
- x: :math:`[B, C, T]`
- x_lengths: :math:`[B]`
- g (optional): :math:`[B, 1, T]`
"""
# embedding layer
# [B ,T, D]
x = self.emb(x) * math.sqrt(self.hidden_channels)
# [B, D, T]
x = torch.transpose(x, 1, -1)
# compute input sequence mask
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
# prenet
if hasattr(self, "prenet") and self.use_prenet:
x = self.prenet(x, x_mask)
# encoder
x = self.encoder(x, x_mask)
# postnet
if hasattr(self, "postnet"):
x = self.postnet(x) * x_mask
# set duration predictor input
if g is not None:
g_exp = g.expand(-1, -1, x.size(-1))
x_dp = torch.cat([x.detach(), g_exp], 1)
else:
x_dp = x.detach()
# final projection layer
x_m = self.proj_m(x) * x_mask
if not self.mean_only:
x_logs = self.proj_s(x) * x_mask
else:
x_logs = torch.zeros_like(x_m)
# duration predictor
logw = self.duration_predictor(x_dp, x_mask)
return x_m, x_logs, logw, x_mask