Medix-AI/coqui-tts
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coqui-tts
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Merge branch 'dev' of https://github.com/mozilla/TTS into dev

This commit is contained in:
erogol 2020-06-05 13:28:39 +02:00
parent 81af469be0 72fe5a8217
commit d00b91710a
13 changed files with 399 additions and 165 deletions
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9
README.md
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@ -17,9 +17,12 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
[Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
## Features
- High performance Text2Speech models on Torch and Tensorflow 2.0.
- High performance Speaker Encoder to compute speaker embeddings efficiently.
- Integration with various Neural Vocoders (PWGAN, MelGAN, WaveRNN)
- High performance Deep Learning models for Text2Speech related tasks.
- Text2Speech models (Tacotron, Tacotron2).
- Speaker Encoder to compute speaker embeddings efficiently.
- Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
- Support for multi-speaker TTS training.
- Ability to convert Torch models to Tensorflow 2.0 for inference.
- Released trained models.
- Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
- Codes to convert Torch models to Tensorflow 2.0.

2
config.json
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@ -96,6 +96,8 @@
"transition_agent": false, // enable/disable transition agent of forward attention.
"location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
"bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
"ddc_r": 7, // reduction rate for coarse decoder.
// STOPNET
"stopnet": true, // Train stopnet predicting the end of synthesis.

11
layers/losses.py
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@ -184,7 +184,7 @@ class TacotronLoss(torch.nn.Module):
def forward(self, postnet_output, decoder_output, mel_input, linear_input,
stopnet_output, stopnet_target, output_lens, decoder_b_output,
alignments, alignment_lens, input_lens):
alignments, alignment_lens, alignments_backwards, input_lens):
return_dict = {}
# decoder and postnet losses
@ -226,6 +226,15 @@ class TacotronLoss(torch.nn.Module):
return_dict['decoder_b_loss'] = decoder_b_loss
return_dict['decoder_c_loss'] = decoder_c_loss
# double decoder consistency loss (if enabled)
if self.config.double_decoder_consistency:
decoder_b_loss = self.criterion(decoder_b_output, mel_input, output_lens)
# decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
loss += decoder_b_loss + attention_c_loss
return_dict['decoder_coarse_loss'] = decoder_b_loss
return_dict['decoder_ddc_loss'] = attention_c_loss
# guided attention loss (if enabled)
if self.config.ga_alpha > 0:
ga_loss = self.criterion_ga(alignments, input_lens, alignment_lens)

135
models/tacotron.py
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@ -1,23 +1,21 @@
# coding: utf-8
import torch
import copy
from torch import nn
from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
from TTS.utils.generic_utils import sequence_mask
from TTS.layers.gst_layers import GST
from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
from TTS.models.tacotron_abstract import TacotronAbstract
class Tacotron(nn.Module):
class Tacotron(TacotronAbstract):
def __init__(self,
num_chars,
num_speakers,
r=5,
postnet_output_dim=1025,
decoder_output_dim=80,
memory_size=5,
attn_type='original',
attn_win=False,
gst=False,
attn_norm="sigmoid",
prenet_type="original",
prenet_dropout=True,
@ -27,38 +25,41 @@ class Tacotron(nn.Module):
location_attn=True,
attn_K=5,
separate_stopnet=True,
bidirectional_decoder=False):
super(Tacotron, self).__init__()
self.r = r
self.decoder_output_dim = decoder_output_dim
self.postnet_output_dim = postnet_output_dim
self.gst = gst
self.num_speakers = num_speakers
self.bidirectional_decoder = bidirectional_decoder
decoder_dim = 512 if num_speakers > 1 else 256
encoder_dim = 512 if num_speakers > 1 else 256
bidirectional_decoder=False,
double_decoder_consistency=False,
ddc_r=None,
gst=False,
memory_size=5):
super(Tacotron,
self).__init__(num_chars, num_speakers, r, postnet_output_dim,
decoder_output_dim, attn_type, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, attn_K, separate_stopnet,
bidirectional_decoder, double_decoder_consistency,
ddc_r, gst)
decoder_in_features = 512 if num_speakers > 1 else 256
encoder_in_features = 512 if num_speakers > 1 else 256
speaker_embedding_dim = 256
proj_speaker_dim = 80 if num_speakers > 1 else 0
# embedding layer
# base model layers
self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
self.embedding.weight.data.normal_(0, 0.3)
# boilerplate model
self.encoder = Encoder(encoder_dim)
self.decoder = Decoder(decoder_dim, decoder_output_dim, r, memory_size, attn_type, attn_win,
self.encoder = Encoder(encoder_in_features)
self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, attn_K, separate_stopnet,
proj_speaker_dim)
if self.bidirectional_decoder:
self.decoder_backward = copy.deepcopy(self.decoder)
self.postnet = PostCBHG(decoder_output_dim)
self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
postnet_output_dim)
# speaker embedding layers
if num_speakers > 1:
self.speaker_embedding = nn.Embedding(num_speakers, 256)
self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
self.speaker_embedding.weight.data.normal_(0, 0.3)
self.speaker_project_mel = nn.Sequential(
nn.Linear(256, proj_speaker_dim), nn.Tanh())
nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
# global style token layers
@ -68,28 +69,15 @@ class Tacotron(nn.Module):
num_heads=4,
num_style_tokens=10,
embedding_dim=gst_embedding_dim)
# backward pass decoder
if self.bidirectional_decoder:
self._init_backward_decoder()
# setup DDC
if self.double_decoder_consistency:
self._init_coarse_decoder()
def _init_states(self):
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
def compute_speaker_embedding(self, speaker_ids):
if hasattr(self, "speaker_embedding") and speaker_ids is None:
raise RuntimeError(
" [!] Model has speaker embedding layer but speaker_id is not provided"
)
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
self.speaker_embeddings = self._compute_speaker_embedding(
speaker_ids)
self.speaker_embeddings_projected = self.speaker_project_mel(
self.speaker_embeddings).squeeze(1)
def compute_gst(self, inputs, mel_specs):
gst_outputs = self.gst_layer(mel_specs)
inputs = self._add_speaker_embedding(inputs, gst_outputs)
return inputs
def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
"""
Shapes:
- characters: B x T_in
@ -98,45 +86,59 @@ class Tacotron(nn.Module):
- speaker_ids: B x 1
"""
self._init_states()
mask = sequence_mask(text_lengths).to(characters.device)
input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
# B x T_in x embed_dim
inputs = self.embedding(characters)
# B x speaker_embed_dim
self.compute_speaker_embedding(speaker_ids)
if speaker_ids is not None:
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
# B x T_in x embed_dim + speaker_embed_dim
inputs = self._concat_speaker_embedding(inputs,
self.speaker_embeddings)
# B x T_in x encoder_dim
# B x T_in x encoder_in_features
encoder_outputs = self.encoder(inputs)
# sequence masking
encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
# global style token
if self.gst:
# B x gst_dim
encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
if self.num_speakers > 1:
encoder_outputs = self._concat_speaker_embedding(
encoder_outputs, self.speaker_embeddings)
# decoder_outputs: B x decoder_dim x T_out
# alignments: B x T_in x encoder_dim
# decoder_outputs: B x decoder_in_features x T_out
# alignments: B x T_in x encoder_in_features
# stop_tokens: B x T_in
decoder_outputs, alignments, stop_tokens = self.decoder(
encoder_outputs, mel_specs, mask,
encoder_outputs, mel_specs, input_mask,
self.speaker_embeddings_projected)
# B x T_out x decoder_dim
# sequence masking
if output_mask is not None:
decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
# B x T_out x decoder_in_features
postnet_outputs = self.postnet(decoder_outputs)
# sequence masking
if output_mask is not None:
postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
# B x T_out x posnet_dim
postnet_outputs = self.last_linear(postnet_outputs)
# B x T_out x decoder_dim
# B x T_out x decoder_in_features
decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
if self.bidirectional_decoder:
decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
if self.double_decoder_consistency:
decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
return decoder_outputs, postnet_outputs, alignments, stop_tokens
@torch.no_grad()
def inference(self, characters, speaker_ids=None, style_mel=None):
inputs = self.embedding(characters)
self._init_states()
self.compute_speaker_embedding(speaker_ids)
if speaker_ids is not None:
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
inputs = self._concat_speaker_embedding(inputs,
self.speaker_embeddings)
@ -152,28 +154,3 @@ class Tacotron(nn.Module):
postnet_outputs = self.last_linear(postnet_outputs)
decoder_outputs = decoder_outputs.transpose(1, 2)
return decoder_outputs, postnet_outputs, alignments, stop_tokens
def _backward_inference(self, mel_specs, encoder_outputs, mask):
decoder_outputs_b, alignments_b, _ = self.decoder_backward(
encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
self.speaker_embeddings_projected)
decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
return decoder_outputs_b, alignments_b
def _compute_speaker_embedding(self, speaker_ids):
speaker_embeddings = self.speaker_embedding(speaker_ids)
return speaker_embeddings.unsqueeze_(1)
@staticmethod
def _add_speaker_embedding(outputs, speaker_embeddings):
speaker_embeddings_ = speaker_embeddings.expand(
outputs.size(0), outputs.size(1), -1)
outputs = outputs + speaker_embeddings_
return outputs
@staticmethod
def _concat_speaker_embedding(outputs, speaker_embeddings):
speaker_embeddings_ = speaker_embeddings.expand(
outputs.size(0), outputs.size(1), -1)
outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
return outputs

136
models/tacotron2.py
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@ -1,13 +1,15 @@
import copy
import torch
from math import sqrt
import torch
from torch import nn
from TTS.layers.tacotron2 import Encoder, Decoder, Postnet
from TTS.utils.generic_utils import sequence_mask
from TTS.layers.gst_layers import GST
from TTS.layers.tacotron2 import Decoder, Encoder, Postnet
from TTS.models.tacotron_abstract import TacotronAbstract
# TODO: match function arguments with tacotron
class Tacotron2(nn.Module):
class Tacotron2(TacotronAbstract):
def __init__(self,
num_chars,
num_speakers,
@ -25,16 +27,22 @@ class Tacotron2(nn.Module):
location_attn=True,
attn_K=5,
separate_stopnet=True,
bidirectional_decoder=False):
super(Tacotron2, self).__init__()
self.postnet_output_dim = postnet_output_dim
self.decoder_output_dim = decoder_output_dim
self.r = r
self.bidirectional_decoder = bidirectional_decoder
decoder_dim = 512 if num_speakers > 1 else 512
encoder_dim = 512 if num_speakers > 1 else 512
bidirectional_decoder=False,
double_decoder_consistency=False,
ddc_r=None,
gst=False):
super(Tacotron2,
self).__init__(num_chars, num_speakers, r, postnet_output_dim,
decoder_output_dim, attn_type, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, attn_K, separate_stopnet,
bidirectional_decoder, double_decoder_consistency,
ddc_r, gst)
decoder_in_features = 512 if num_speakers > 1 else 512
encoder_in_features = 512 if num_speakers > 1 else 512
proj_speaker_dim = 80 if num_speakers > 1 else 0
# embedding layer
# base layers
self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
std = sqrt(2.0 / (num_chars + 512))
val = sqrt(3.0) * std # uniform bounds for std
@ -42,20 +50,25 @@ class Tacotron2(nn.Module):
if num_speakers > 1:
self.speaker_embedding = nn.Embedding(num_speakers, 512)
self.speaker_embedding.weight.data.normal_(0, 0.3)
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
self.encoder = Encoder(encoder_dim)
self.decoder = Decoder(decoder_dim, self.decoder_output_dim, r, attn_type, attn_win,
self.encoder = Encoder(encoder_in_features)
self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
attn_norm, prenet_type, prenet_dropout,
forward_attn, trans_agent, forward_attn_mask,
location_attn, attn_K, separate_stopnet, proj_speaker_dim)
if self.bidirectional_decoder:
self.decoder_backward = copy.deepcopy(self.decoder)
self.postnet = Postnet(self.postnet_output_dim)
def _init_states(self):
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
# global style token layers
if self.gst:
gst_embedding_dim = encoder_in_features
self.gst_layer = GST(num_mel=80,
num_heads=4,
num_style_tokens=10,
embedding_dim=gst_embedding_dim)
# backward pass decoder
if self.bidirectional_decoder:
self._init_backward_decoder()
# setup DDC
if self.double_decoder_consistency:
self._init_coarse_decoder()
@staticmethod
def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
@ -63,31 +76,60 @@ class Tacotron2(nn.Module):
mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
return mel_outputs, mel_outputs_postnet, alignments
def forward(self, text, text_lengths, mel_specs=None, speaker_ids=None):
def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
self._init_states()
# compute mask for padding
mask = sequence_mask(text_lengths).to(text.device)
# B x T_in_max (boolean)
input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
# B x D_embed x T_in_max
embedded_inputs = self.embedding(text).transpose(1, 2)
# B x T_in_max x D_en
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
# adding speaker embeddding to encoder output
# TODO: multi-speaker
# B x speaker_embed_dim
if speaker_ids is not None:
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
# B x T_in x embed_dim + speaker_embed_dim
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
self.speaker_embeddings)
encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
# global style token
if self.gst:
# B x gst_dim
encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
# B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
decoder_outputs, alignments, stop_tokens = self.decoder(
encoder_outputs, mel_specs, mask)
encoder_outputs, mel_specs, input_mask)
# sequence masking
if mel_lengths is not None:
decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
# B x mel_dim x T_out
postnet_outputs = self.postnet(decoder_outputs)
postnet_outputs = decoder_outputs + postnet_outputs
# sequence masking
if output_mask is not None:
postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
# B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
decoder_outputs, postnet_outputs, alignments)
if self.bidirectional_decoder:
decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
if self.double_decoder_consistency:
decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
return decoder_outputs, postnet_outputs, alignments, stop_tokens
@torch.no_grad()
def inference(self, text, speaker_ids=None):
embedded_inputs = self.embedding(text).transpose(1, 2)
encoder_outputs = self.encoder.inference(embedded_inputs)
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
speaker_ids)
if speaker_ids is not None:
self.compute_speaker_embedding(speaker_ids)
if self.num_speakers > 1:
encoder_outputs = self._add_speaker_embedding(encoder_outputs,
self.speaker_embeddings)
mel_outputs, alignments, stop_tokens = self.decoder.inference(
encoder_outputs)
mel_outputs_postnet = self.postnet(mel_outputs)
@ -112,22 +154,16 @@ class Tacotron2(nn.Module):
mel_outputs, mel_outputs_postnet, alignments)
return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
def _backward_inference(self, mel_specs, encoder_outputs, mask):
decoder_outputs_b, alignments_b, _ = self.decoder_backward(
encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
self.speaker_embeddings_projected)
decoder_outputs_b = decoder_outputs_b.transpose(1, 2)
return decoder_outputs_b, alignments_b
def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
if hasattr(self, "speaker_embedding") and speaker_ids is None:
raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
speaker_embeddings = self.speaker_embedding(speaker_ids)
def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
# TODO: multi-speaker
# if hasattr(self, "speaker_embedding") and speaker_ids is None:
# raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
# if hasattr(self, "speaker_embedding") and speaker_ids is not None:
speaker_embeddings.unsqueeze_(1)
speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
encoder_outputs.size(1),
-1)
encoder_outputs = encoder_outputs + speaker_embeddings
return encoder_outputs
# speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
# encoder_outputs.size(1),
# -1)
# encoder_outputs = encoder_outputs + speaker_embeddings
# return encoder_outputs
pass

180
models/tacotron_abstract.py Normal file
View File

@ -0,0 +1,180 @@
import copy
from abc import ABC, abstractmethod
import torch
from torch import nn
from TTS.utils.generic_utils import sequence_mask
class TacotronAbstract(ABC, nn.Module):
def __init__(self,
num_chars,
num_speakers,
r,
postnet_output_dim=80,
decoder_output_dim=80,
attn_type='original',
attn_win=False,
attn_norm="softmax",
prenet_type="original",
prenet_dropout=True,
forward_attn=False,
trans_agent=False,
forward_attn_mask=False,
location_attn=True,
attn_K=5,
separate_stopnet=True,
bidirectional_decoder=False,
double_decoder_consistency=False,
ddc_r=None,
gst=False):
""" Abstract Tacotron class """
super().__init__()
self.num_chars = num_chars
self.r = r
self.decoder_output_dim = decoder_output_dim
self.postnet_output_dim = postnet_output_dim
self.gst = gst
self.num_speakers = num_speakers
self.bidirectional_decoder = bidirectional_decoder
self.double_decoder_consistency = double_decoder_consistency
self.ddc_r = ddc_r
self.attn_type = attn_type
self.attn_win = attn_win
self.attn_norm = attn_norm
self.prenet_type = prenet_type
self.prenet_dropout = prenet_dropout
self.forward_attn = forward_attn
self.trans_agent = trans_agent
self.forward_attn_mask = forward_attn_mask
self.location_attn = location_attn
self.attn_K = attn_K
self.separate_stopnet = separate_stopnet
# layers
self.embedding = None
self.encoder = None
self.decoder = None
self.postnet = None
# global style token
if self.gst:
self.gst_layer = None
# model states
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
# additional layers
self.decoder_backward = None
self.coarse_decoder = None
#############################
# INIT FUNCTIONS
#############################
def _init_states(self):
self.speaker_embeddings = None
self.speaker_embeddings_projected = None
def _init_backward_decoder(self):
self.decoder_backward = copy.deepcopy(self.decoder)
def _init_coarse_decoder(self):
self.coarse_decoder = copy.deepcopy(self.decoder)
self.coarse_decoder.r_init = self.ddc_r
self.coarse_decoder.set_r(self.ddc_r)
#############################
# CORE FUNCTIONS
#############################
@abstractmethod
def forward(self):
pass
@abstractmethod
def inference(self):
pass
#############################
# COMMON COMPUTE FUNCTIONS
#############################
def compute_masks(self, text_lengths, mel_lengths):
"""Compute masks against sequence paddings."""
# B x T_in_max (boolean)
device = text_lengths.device
input_mask = sequence_mask(text_lengths).to(device)
output_mask = None
if mel_lengths is not None:
max_len = mel_lengths.max()
r = self.decoder.r
max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len
output_mask = sequence_mask(mel_lengths, max_len=max_len).to(device)
return input_mask, output_mask
def _backward_pass(self, mel_specs, encoder_outputs, mask):
""" Run backwards decoder """
decoder_outputs_b, alignments_b, _ = self.decoder_backward(
encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
self.speaker_embeddings_projected)
decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
return decoder_outputs_b, alignments_b
def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments,
input_mask):
""" Double Decoder Consistency """
T = mel_specs.shape[1]
if T % self.coarse_decoder.r > 0:
padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
mel_specs = torch.nn.functional.pad(mel_specs,
(0, 0, 0, padding_size, 0, 0))
decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
encoder_outputs.detach(), mel_specs, input_mask)
# scale_factor = self.decoder.r_init / self.decoder.r
alignments_backward = torch.nn.functional.interpolate(
alignments_backward.transpose(1, 2),
size=alignments.shape[1],
mode='nearest').transpose(1, 2)
decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2)
decoder_outputs_backward = decoder_outputs_backward[:, :T, :]
return decoder_outputs_backward, alignments_backward
#############################
# EMBEDDING FUNCTIONS
#############################
def compute_speaker_embedding(self, speaker_ids):
""" Compute speaker embedding vectors """
if hasattr(self, "speaker_embedding") and speaker_ids is None:
raise RuntimeError(
" [!] Model has speaker embedding layer but speaker_id is not provided"
)
if hasattr(self, "speaker_embedding") and speaker_ids is not None:
self.speaker_embeddings = self.speaker_embedding(speaker_ids).unsqueeze(1)
if hasattr(self, "speaker_project_mel") and speaker_ids is not None:
self.speaker_embeddings_projected = self.speaker_project_mel(
self.speaker_embeddings).squeeze(1)
def compute_gst(self, inputs, mel_specs):
""" Compute global style token """
# pylint: disable=not-callable
gst_outputs = self.gst_layer(mel_specs)
inputs = self._add_speaker_embedding(inputs, gst_outputs)
return inputs
@staticmethod
def _add_speaker_embedding(outputs, speaker_embeddings):
speaker_embeddings_ = speaker_embeddings.expand(
outputs.size(0), outputs.size(1), -1)
outputs = outputs + speaker_embeddings_
return outputs
@staticmethod
def _concat_speaker_embedding(outputs, speaker_embeddings):
speaker_embeddings_ = speaker_embeddings.expand(
outputs.size(0), outputs.size(1), -1)
outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
return outputs

2
tests/outputs/dummy_model_config.json
View File

@ -55,6 +55,8 @@
"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"use_gst": false,
"double_decoder_consistency": true, // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
"ddc_r": 7, // reduction rate for coarse decoder.
"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
"eval_batch_size":16,

2
tests/test_tacotron2_model.py
View File

@ -51,7 +51,7 @@ class TacotronTrainTest(unittest.TestCase):
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for i in range(5):
mel_out, mel_postnet_out, align, stop_tokens = model.forward(
input, input_lengths, mel_spec, speaker_ids)
input, input_lengths, mel_spec, mel_lengths, speaker_ids)
assert torch.sigmoid(stop_tokens).data.max() <= 1.0
assert torch.sigmoid(stop_tokens).data.min() >= 0.0
optimizer.zero_grad()

5
tests/test_tacotron_model.py
View File

@ -66,7 +66,7 @@ class TacotronTrainTest(unittest.TestCase):
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, speaker_ids)
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
@ -95,6 +95,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
mel_lengths[-1] = 120
stop_targets = torch.zeros(8, 120, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
@ -130,7 +131,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(10):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, speaker_ids)
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)

10
train.py
View File

@ -158,13 +158,14 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
optimizer_st.zero_grad()
# forward pass model
if c.bidirectional_decoder:
if c.bidirectional_decoder or c.double_decoder_consistency:
decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
else:
decoder_output, postnet_output, alignments, stop_tokens = model(
text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
decoder_backward_output = None
alignments_backward = None
# set the alignment lengths wrt reduction factor for guided attention
if mel_lengths.max() % model.decoder.r != 0:
@ -176,7 +177,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
loss_dict = criterion(postnet_output, decoder_output, mel_input,
linear_input, stop_tokens, stop_targets,
mel_lengths, decoder_backward_output,
alignments, alignment_lengths, text_lengths)
alignments, alignment_lengths, alignments_backward,
text_lengths)
if c.bidirectional_decoder:
keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})

11
utils/generic_utils.py
View File

@ -160,13 +160,16 @@ def setup_model(num_chars, num_speakers, c):
location_attn=c.location_attn,
attn_K=c.attention_heads,
separate_stopnet=c.separate_stopnet,
bidirectional_decoder=c.bidirectional_decoder)
bidirectional_decoder=c.bidirectional_decoder,
double_decoder_consistency=c.double_decoder_consistency,
ddc_r=c.ddc_r)
elif c.model.lower() == "tacotron2":
model = MyModel(num_chars=num_chars,
num_speakers=num_speakers,
r=c.r,
postnet_output_dim=c.audio['num_mels'],
decoder_output_dim=c.audio['num_mels'],
gst=c.use_gst,
attn_type=c.attention_type,
attn_win=c.windowing,
attn_norm=c.attention_norm,
@ -178,7 +181,9 @@ def setup_model(num_chars, num_speakers, c):
location_attn=c.location_attn,
attn_K=c.attention_heads,
separate_stopnet=c.separate_stopnet,
bidirectional_decoder=c.bidirectional_decoder)
bidirectional_decoder=c.bidirectional_decoder,
double_decoder_consistency=c.double_decoder_consistency,
ddc_r=c.ddc_r)
return model
class KeepAverage():
@ -313,6 +318,8 @@ def check_config(c):
_check_argument('transition_agent', c, restricted=True, val_type=bool)
_check_argument('location_attn', c, restricted=True, val_type=bool)
_check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
_check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
_check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
# stopnet
_check_argument('stopnet', c, restricted=True, val_type=bool)

10
vocoder/layers/losses.py
View File

@ -77,6 +77,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
""" Multiscale STFT loss for multi band model outputs """
# pylint: disable=no-self-use
def forward(self, y_hat, y):
y_hat = y_hat.view(-1, 1, y_hat.shape[2])
y = y.view(-1, 1, y.shape[2])
@ -85,6 +86,7 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
class MSEGLoss(nn.Module):
""" Mean Squared Generator Loss """
# pylint: disable=no-self-use
def forward(self, score_fake):
loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
return loss_fake
@ -92,6 +94,7 @@ class MSEGLoss(nn.Module):
class HingeGLoss(nn.Module):
""" Hinge Discriminator Loss """
# pylint: disable=no-self-use
def forward(self, score_fake):
loss_fake = torch.mean(F.relu(1. + score_fake))
return loss_fake
@ -104,6 +107,7 @@ class HingeGLoss(nn.Module):
class MSEDLoss(nn.Module):
""" Mean Squared Discriminator Loss """
# pylint: disable=no-self-use
def forward(self, score_fake, score_real):
loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@ -113,6 +117,7 @@ class MSEDLoss(nn.Module):
class HingeDLoss(nn.Module):
""" Hinge Discriminator Loss """
# pylint: disable=no-self-use
def forward(self, score_fake, score_real):
loss_real = torch.mean(F.relu(1. - score_real))
loss_fake = torch.mean(F.relu(1. + score_fake))
@ -121,6 +126,7 @@ class HingeDLoss(nn.Module):
class MelganFeatureLoss(nn.Module):
# pylint: disable=no-self-use
def forward(self, fake_feats, real_feats):
loss_feats = 0
for fake_feat, real_feat in zip(fake_feats, real_feats):
@ -193,8 +199,8 @@ class GeneratorLoss(nn.Module):
self.stft_loss_weight = C.stft_loss_weight
self.subband_stft_loss_weight = C.subband_stft_loss_weight
self.mse_gan_loss_weight = C.mse_gan_loss_weight
self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
self.mse_gan_loss_weight = C.mse_G_loss_weight
self.hinge_gan_loss_weight = C.hinge_G_loss_weight
self.feat_match_loss_weight = C.feat_match_loss_weight
if C.use_stft_loss:

21
vocoder/train.py
View File

@ -52,7 +52,7 @@ def setup_loader(ap, is_val=False, verbose=False):
# sampler = DistributedSampler(dataset) if num_gpus > 1 else None
loader = DataLoader(dataset,
batch_size=1 if is_val else c.batch_size,
shuffle=False,
shuffle=True,
drop_last=False,
sampler=None,
num_workers=c.num_val_loader_workers
@ -120,11 +120,13 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
y_hat = model_G(c_G)
y_hat_sub = None
y_G_sub = None
y_hat_vis = y_hat # for visualization
# PQMF formatting
if y_hat.shape[1] > 1:
y_hat_sub = y_hat
y_hat = model_G.pqmf_synthesis(y_hat)
y_hat_vis = y_hat
y_G_sub = model_G.pqmf_analysis(y_G)
if global_step > c.steps_to_start_discriminator:
@ -171,7 +173,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
loss_dict = dict()
for key, value in loss_G_dict.items():
loss_dict[key] = value.item()
if isinstance(value, int):
loss_dict[key] = value
else:
loss_dict[key] = value.item()
##############################
# DISCRIMINATOR
@ -265,12 +270,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
model_losses=loss_dict)
# compute spectrograms
figures = plot_results(y_hat, y_G, ap, global_step,
figures = plot_results(y_hat_vis, y_G, ap, global_step,
'train')
tb_logger.tb_train_figures(global_step, figures)
# Sample audio
sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
sample_voice = y_hat_vis[0].squeeze(0).detach().cpu().numpy()
tb_logger.tb_train_audios(global_step,
{'train/audio': sample_voice},
c.audio["sample_rate"])
@ -322,8 +327,12 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
y_hat = model_G.pqmf_synthesis(y_hat)
y_G_sub = model_G.pqmf_analysis(y_G)
D_out_fake = model_D(y_hat)
if len(signature(model_D.forward).parameters) == 2:
D_out_fake = model_D(y_hat, c_G)
else:
D_out_fake = model_D(y_hat)
D_out_real = None
if c.use_feat_match_loss:
with torch.no_grad():
D_out_real = model_D(y_G)
@ -354,7 +363,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
for key, value in loss_G_dict.items():
update_eval_values['avg_' + key] = value.item()
update_eval_values['avg_loader_time'] = loader_time
update_eval_values['avgP_step_time'] = step_time
update_eval_values['avg_step_time'] = step_time
keep_avg.update_values(update_eval_values)
# print eval stats