mirror of https://github.com/coqui-ai/TTS.git
update glow-tts layers and add some comments
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erogol
parent
29cf933831
commit
29f4329d7f
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@ -60,9 +60,11 @@ class ResidualConvBNBlock(nn.Module):
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self.res_blocks.append(block)
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def forward(self, x, x_mask=None):
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o = x
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o = x * x_mask
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for block in self.res_blocks:
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res = o
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o = block(o * x_mask if x_mask is not None else o)
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o = block(o)
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o = o + res
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if x_mask is not None:
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o = o * x_mask
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return o
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@ -6,6 +6,13 @@ from TTS.tts.layers.generic.normalization import ActNorm
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def squeeze(x, x_mask=None, num_sqz=2):
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"""GlowTTS squeeze operation
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Increase number of channels and reduce number of time steps
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by the same factor.
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Note:
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each 's' is a n-dimensional vector.
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[s1,s2,s3,s4,s5,s6] --> [[s1, s3, s5], [s2, s4, s6]]"""
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b, c, t = x.size()
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t = (t // num_sqz) * num_sqz
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@ -23,6 +30,11 @@ def squeeze(x, x_mask=None, num_sqz=2):
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def unsqueeze(x, x_mask=None, num_sqz=2):
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"""GlowTTS unsqueeze operation
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Note:
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each 's' is a n-dimensional vector.
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[[s1, s3, s5], [s2, s4, s6]] --> [[s1, s3, s5], [s2, s4, s6]] """
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b, c, t = x.size()
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x_unsqz = x.view(b, num_sqz, c // num_sqz, t)
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@ -40,7 +52,19 @@ def unsqueeze(x, x_mask=None, num_sqz=2):
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class Decoder(nn.Module):
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"""Stack of Glow Modules"""
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"""Stack of Glow Decoder Modules.
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Squeeze -> ActNorm -> InvertibleConv1x1 -> AffineCoupling -> Unsqueeze
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Args:
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in_channels (int): channels of input tensor.
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hidden_channels (int): hidden decoder channels.
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kernel_size (int): Coupling block kernel size. (Wavenet filter kernel size.)
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dilation_rate (int): rate to increase dilation by each layer in a decoder block.
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num_flow_blocks (int): number of decoder blocks.
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num_coupling_layers (int): number coupling layers. (number of wavenet layers.)
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dropout_p (float): wavenet dropout rate.
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sigmoid_scale (bool): enable/disable sigmoid scaling in coupling layer.
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"""
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def __init__(self,
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in_channels,
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hidden_channels,
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@ -50,7 +74,7 @@ class Decoder(nn.Module):
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num_coupling_layers,
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dropout_p=0.,
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num_splits=4,
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num_sqz=2,
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num_squeeze=2,
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sigmoid_scale=False,
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c_in_channels=0):
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super().__init__()
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@ -63,18 +87,18 @@ class Decoder(nn.Module):
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self.num_coupling_layers = num_coupling_layers
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self.dropout_p = dropout_p
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self.num_splits = num_splits
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self.num_sqz = num_sqz
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self.num_squeeze = num_squeeze
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self.sigmoid_scale = sigmoid_scale
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self.c_in_channels = c_in_channels
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self.flows = nn.ModuleList()
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for _ in range(num_flow_blocks):
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self.flows.append(ActNorm(channels=in_channels * num_sqz))
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self.flows.append(ActNorm(channels=in_channels * num_squeeze))
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self.flows.append(
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InvConvNear(channels=in_channels * num_sqz,
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InvConvNear(channels=in_channels * num_squeeze,
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num_splits=num_splits))
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self.flows.append(
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CouplingBlock(in_channels * num_sqz,
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CouplingBlock(in_channels * num_squeeze,
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hidden_channels,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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@ -91,16 +115,16 @@ class Decoder(nn.Module):
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flows = reversed(self.flows)
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logdet_tot = None
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if self.num_sqz > 1:
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x, x_mask = squeeze(x, x_mask, self.num_sqz)
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if self.num_squeeze > 1:
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x, x_mask = squeeze(x, x_mask, self.num_squeeze)
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for f in flows:
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if not reverse:
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x, logdet = f(x, x_mask, g=g, reverse=reverse)
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logdet_tot += logdet
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else:
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x, logdet = f(x, x_mask, g=g, reverse=reverse)
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if self.num_sqz > 1:
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x, x_mask = unsqueeze(x, x_mask, self.num_sqz)
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if self.num_squeeze > 1:
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x, x_mask = unsqueeze(x, x_mask, self.num_squeeze)
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return x, logdet_tot
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def store_inverse(self):
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@ -2,7 +2,7 @@ import math
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import torch
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from torch import nn
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from TTS.tts.layers.glow_tts.transformer import Transformer
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from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
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from TTS.tts.layers.generic.gated_conv import GatedConvBlock
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from TTS.tts.utils.generic_utils import sequence_mask
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from TTS.tts.layers.glow_tts.glow import ConvLayerNorm
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@ -12,16 +12,20 @@ from TTS.tts.layers.generic.res_conv_bn import ResidualConvBNBlock
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class Encoder(nn.Module):
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"""Glow-TTS encoder module. It uses Transformer with Relative Pos.Encoding
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as in the original paper or GatedConvBlock as a faster alternative.
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"""Glow-TTS encoder module.
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embedding -> <prenet> -> encoder_module -> <postnet> --> proj_mean
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|-> proj_var
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|-> concat -> duration_predictor
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↑
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speaker_embed
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Args:
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num_chars (int): number of characters.
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out_channels (int): number of output channels.
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hidden_channels (int): encoder's embedding size.
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hidden_channels_ffn (int): transformer's feed-forward channels.
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num_head (int): number of attention heads in transformer.
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num_layers (int): number of transformer encoder stack.
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kernel_size (int): kernel size for conv layers and duration predictor.
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dropout_p (float): dropout rate for any dropout layer.
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mean_only (bool): if True, output only mean values and use constant std.
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@ -30,19 +34,49 @@ class Encoder(nn.Module):
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Shapes:
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- input: (B, T, C)
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Notes:
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suggested encoder params...
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for encoder_type == 'rel_pos_transformer'
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encoder_params={
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'kernel_size':3,
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'dropout_p': 0.1,
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'num_layers': 6,
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'num_heads': 2,
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'hidden_channels_ffn': 768, # 4 times the hidden_channels
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'input_length': None
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}
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for encoder_type == 'gated_conv'
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encoder_params={
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'kernel_size':5,
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'dropout_p': 0.1,
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'num_layers': 9,
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}
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for encoder_type == 'residual_conv_bn'
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encoder_params={
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"kernel_size": 4,
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"dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 13
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}
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for encoder_type == 'time_depth_separable'
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encoder_params={
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"kernel_size": 5,
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'num_layers': 9,
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}
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"""
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def __init__(self,
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num_chars,
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out_channels,
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hidden_channels,
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hidden_channels_ffn,
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hidden_channels_dp,
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encoder_type,
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num_heads,
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num_layers,
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dropout_p,
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rel_attn_window_size=None,
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input_length=None,
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encoder_params,
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dropout_p_dp=0.1,
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mean_only=False,
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use_prenet=True,
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c_in_channels=0):
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@ -51,11 +85,8 @@ class Encoder(nn.Module):
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self.num_chars = num_chars
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.hidden_channels_ffn = hidden_channels_ffn
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self.hidden_channels_dp = hidden_channels_dp
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.dropout_p = dropout_p
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self.dropout_p_dp = dropout_p_dp
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self.mean_only = mean_only
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self.use_prenet = use_prenet
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self.c_in_channels = c_in_channels
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