Spaces:
Running
Running
File size: 6,878 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
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
|