# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Joint text-to-wav module for end-to-end training."""
from typing import Any, Dict
import torch
from typeguard import typechecked
from espnet2.gan_tts.abs_gan_tts import AbsGANTTS
from espnet2.gan_tts.hifigan import (
HiFiGANGenerator,
HiFiGANMultiPeriodDiscriminator,
HiFiGANMultiScaleDiscriminator,
HiFiGANMultiScaleMultiPeriodDiscriminator,
HiFiGANPeriodDiscriminator,
HiFiGANScaleDiscriminator,
)
from espnet2.gan_tts.hifigan.loss import (
DiscriminatorAdversarialLoss,
FeatureMatchLoss,
GeneratorAdversarialLoss,
MelSpectrogramLoss,
)
from espnet2.gan_tts.melgan import MelGANGenerator, MelGANMultiScaleDiscriminator
from espnet2.gan_tts.melgan.pqmf import PQMF
from espnet2.gan_tts.parallel_wavegan import (
ParallelWaveGANDiscriminator,
ParallelWaveGANGenerator,
)
from espnet2.gan_tts.style_melgan import StyleMelGANDiscriminator, StyleMelGANGenerator
from espnet2.gan_tts.utils import get_random_segments, get_segments
from espnet2.torch_utils.device_funcs import force_gatherable
from espnet2.tts.fastspeech import FastSpeech
from espnet2.tts.fastspeech2 import FastSpeech2
from espnet2.tts.tacotron2 import Tacotron2
from espnet2.tts.transformer import Transformer
AVAILABLE_TEXT2MEL = {
"tacotron2": Tacotron2,
"transformer": Transformer,
"fastspeech": FastSpeech,
"fastspeech2": FastSpeech2,
}
AVAILABLE_VOCODER = {
"hifigan_generator": HiFiGANGenerator,
"melgan_generator": MelGANGenerator,
"parallel_wavegan_generator": ParallelWaveGANGenerator,
"style_melgan_generator": StyleMelGANGenerator,
}
AVAILABLE_DISCRIMINATORS = {
"hifigan_period_discriminator": HiFiGANPeriodDiscriminator,
"hifigan_scale_discriminator": HiFiGANScaleDiscriminator,
"hifigan_multi_period_discriminator": HiFiGANMultiPeriodDiscriminator,
"hifigan_multi_scale_discriminator": HiFiGANMultiScaleDiscriminator,
"hifigan_multi_scale_multi_period_discriminator": HiFiGANMultiScaleMultiPeriodDiscriminator, # NOQA
"melgan_multi_scale_discriminator": MelGANMultiScaleDiscriminator,
"parallel_wavegan_discriminator": ParallelWaveGANDiscriminator,
"style_melgan_discriminator": StyleMelGANDiscriminator,
}
[docs]class JointText2Wav(AbsGANTTS):
"""General class to jointly train text2mel and vocoder parts."""
@typechecked
def __init__(
self,
# generator (text2mel + vocoder) related
idim: int,
odim: int,
segment_size: int = 32,
sampling_rate: int = 22050,
text2mel_type: str = "fastspeech2",
text2mel_params: Dict[str, Any] = {
"adim": 384,
"aheads": 2,
"elayers": 4,
"eunits": 1536,
"dlayers": 4,
"dunits": 1536,
"postnet_layers": 5,
"postnet_chans": 512,
"postnet_filts": 5,
"postnet_dropout_rate": 0.5,
"positionwise_layer_type": "conv1d",
"positionwise_conv_kernel_size": 1,
"use_scaled_pos_enc": True,
"use_batch_norm": True,
"encoder_normalize_before": True,
"decoder_normalize_before": True,
"encoder_concat_after": False,
"decoder_concat_after": False,
"reduction_factor": 1,
"encoder_type": "conformer",
"decoder_type": "conformer",
"transformer_enc_dropout_rate": 0.1,
"transformer_enc_positional_dropout_rate": 0.1,
"transformer_enc_attn_dropout_rate": 0.1,
"transformer_dec_dropout_rate": 0.1,
"transformer_dec_positional_dropout_rate": 0.1,
"transformer_dec_attn_dropout_rate": 0.1,
"conformer_rel_pos_type": "latest",
"conformer_pos_enc_layer_type": "rel_pos",
"conformer_self_attn_layer_type": "rel_selfattn",
"conformer_activation_type": "swish",
"use_macaron_style_in_conformer": True,
"use_cnn_in_conformer": True,
"zero_triu": False,
"conformer_enc_kernel_size": 7,
"conformer_dec_kernel_size": 31,
"duration_predictor_layers": 2,
"duration_predictor_chans": 384,
"duration_predictor_kernel_size": 3,
"duration_predictor_dropout_rate": 0.1,
"energy_predictor_layers": 2,
"energy_predictor_chans": 384,
"energy_predictor_kernel_size": 3,
"energy_predictor_dropout": 0.5,
"energy_embed_kernel_size": 1,
"energy_embed_dropout": 0.5,
"stop_gradient_from_energy_predictor": False,
"pitch_predictor_layers": 5,
"pitch_predictor_chans": 384,
"pitch_predictor_kernel_size": 5,
"pitch_predictor_dropout": 0.5,
"pitch_embed_kernel_size": 1,
"pitch_embed_dropout": 0.5,
"stop_gradient_from_pitch_predictor": True,
"spks": -1,
"langs": -1,
"spk_embed_dim": None,
"spk_embed_integration_type": "add",
"use_gst": False,
"gst_tokens": 10,
"gst_heads": 4,
"gst_conv_layers": 6,
"gst_conv_chans_list": [32, 32, 64, 64, 128, 128],
"gst_conv_kernel_size": 3,
"gst_conv_stride": 2,
"gst_gru_layers": 1,
"gst_gru_units": 128,
"init_type": "xavier_uniform",
"init_enc_alpha": 1.0,
"init_dec_alpha": 1.0,
"use_masking": False,
"use_weighted_masking": False,
},
vocoder_type: str = "hifigan_generator",
vocoder_params: Dict[str, Any] = {
"out_channels": 1,
"channels": 512,
"global_channels": -1,
"kernel_size": 7,
"upsample_scales": [8, 8, 2, 2],
"upsample_kernel_sizes": [16, 16, 4, 4],
"resblock_kernel_sizes": [3, 7, 11],
"resblock_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
"use_additional_convs": True,
"bias": True,
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
},
use_pqmf: bool = False,
pqmf_params: Dict[str, Any] = {
"subbands": 4,
"taps": 62,
"cutoff_ratio": 0.142,
"beta": 9.0,
},
# discriminator related
discriminator_type: str = "hifigan_multi_scale_multi_period_discriminator",
discriminator_params: Dict[str, Any] = {
"scales": 1,
"scale_downsample_pooling": "AvgPool1d",
"scale_downsample_pooling_params": {
"kernel_size": 4,
"stride": 2,
"padding": 2,
},
"scale_discriminator_params": {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [15, 41, 5, 3],
"channels": 128,
"max_downsample_channels": 1024,
"max_groups": 16,
"bias": True,
"downsample_scales": [2, 2, 4, 4, 1],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
"use_spectral_norm": False,
},
"follow_official_norm": False,
"periods": [2, 3, 5, 7, 11],
"period_discriminator_params": {
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [5, 3],
"channels": 32,
"downsample_scales": [3, 3, 3, 3, 1],
"max_downsample_channels": 1024,
"bias": True,
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
"use_spectral_norm": False,
},
},
# loss related
generator_adv_loss_params: Dict[str, Any] = {
"average_by_discriminators": False,
"loss_type": "mse",
},
discriminator_adv_loss_params: Dict[str, Any] = {
"average_by_discriminators": False,
"loss_type": "mse",
},
use_feat_match_loss: bool = True,
feat_match_loss_params: Dict[str, Any] = {
"average_by_discriminators": False,
"average_by_layers": False,
"include_final_outputs": True,
},
use_mel_loss: bool = True,
mel_loss_params: Dict[str, Any] = {
"fs": 22050,
"n_fft": 1024,
"hop_length": 256,
"win_length": None,
"window": "hann",
"n_mels": 80,
"fmin": 0,
"fmax": None,
"log_base": None,
},
lambda_text2mel: float = 1.0,
lambda_adv: float = 1.0,
lambda_feat_match: float = 2.0,
lambda_mel: float = 45.0,
cache_generator_outputs: bool = False,
):
"""Initialize JointText2Wav module.
Args:
idim (int): Input vocabrary size.
odim (int): Acoustic feature dimension. The actual output channels will
be 1 since the model is the end-to-end text-to-wave model but for the
compatibility odim is used to indicate the acoustic feature dimension.
segment_size (int): Segment size for random windowed inputs.
sampling_rate (int): Sampling rate, not used for the training but it will
be referred in saving waveform during the inference.
text2mel_type (str): The text2mel model type.
text2mel_params (Dict[str, Any]): Parameter dict for text2mel model.
use_pqmf (bool): Whether to use PQMF for multi-band vocoder.
pqmf_params (Dict[str, Any]): Parameter dict for PQMF module.
vocoder_type (str): The vocoder model type.
vocoder_params (Dict[str, Any]): Parameter dict for vocoder model.
discriminator_type (str): Discriminator type.
discriminator_params (Dict[str, Any]): Parameter dict for discriminator.
generator_adv_loss_params (Dict[str, Any]): Parameter dict for generator
adversarial loss.
discriminator_adv_loss_params (Dict[str, Any]): Parameter dict for
discriminator adversarial loss.
use_feat_match_loss (bool): Whether to use feat match loss.
feat_match_loss_params (Dict[str, Any]): Parameter dict for feat match loss.
use_mel_loss (bool): Whether to use mel loss.
mel_loss_params (Dict[str, Any]): Parameter dict for mel loss.
lambda_text2mel (float): Loss scaling coefficient for text2mel model loss.
lambda_adv (float): Loss scaling coefficient for adversarial loss.
lambda_feat_match (float): Loss scaling coefficient for feat match loss.
lambda_mel (float): Loss scaling coefficient for mel loss.
cache_generator_outputs (bool): Whether to cache generator outputs.
"""
super().__init__()
self.segment_size = segment_size
self.use_pqmf = use_pqmf
# define modules
self.generator = torch.nn.ModuleDict()
text2mel_class = AVAILABLE_TEXT2MEL[text2mel_type]
text2mel_params.update(idim=idim, odim=odim)
self.generator["text2mel"] = text2mel_class(
**text2mel_params,
)
vocoder_class = AVAILABLE_VOCODER[vocoder_type]
if vocoder_type in ["hifigan_generator", "melgan_generator"]:
vocoder_params.update(in_channels=odim)
elif vocoder_type in ["parallel_wavegan_generator", "style_melgan_generator"]:
vocoder_params.update(aux_channels=odim)
self.generator["vocoder"] = vocoder_class(
**vocoder_params,
)
if self.use_pqmf:
self.pqmf = PQMF(**pqmf_params)
discriminator_class = AVAILABLE_DISCRIMINATORS[discriminator_type]
self.discriminator = discriminator_class(
**discriminator_params,
)
self.generator_adv_loss = GeneratorAdversarialLoss(
**generator_adv_loss_params,
)
self.discriminator_adv_loss = DiscriminatorAdversarialLoss(
**discriminator_adv_loss_params,
)
self.use_feat_match_loss = use_feat_match_loss
if self.use_feat_match_loss:
self.feat_match_loss = FeatureMatchLoss(
**feat_match_loss_params,
)
self.use_mel_loss = use_mel_loss
if self.use_mel_loss:
self.mel_loss = MelSpectrogramLoss(
**mel_loss_params,
)
# coefficients
self.lambda_text2mel = lambda_text2mel
self.lambda_adv = lambda_adv
if self.use_feat_match_loss:
self.lambda_feat_match = lambda_feat_match
if self.use_mel_loss:
self.lambda_mel = lambda_mel
# cache
self.cache_generator_outputs = cache_generator_outputs
self._cache = None
# store sampling rate for saving wav file
# (not used for the training)
self.fs = sampling_rate
# store parameters for test compatibility
self.spks = self.generator["text2mel"].spks
self.langs = self.generator["text2mel"].langs
self.spk_embed_dim = self.generator["text2mel"].spk_embed_dim
@property
def require_raw_speech(self):
"""Return whether or not speech is required."""
return True
@property
def require_vocoder(self):
"""Return whether or not vocoder is required."""
return False
[docs] def forward(
self,
text: torch.Tensor,
text_lengths: torch.Tensor,
feats: torch.Tensor,
feats_lengths: torch.Tensor,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
forward_generator: bool = True,
**kwargs,
) -> Dict[str, Any]:
"""Perform generator forward.
Args:
text (Tensor): Text index tensor (B, T_text).
text_lengths (Tensor): Text length tensor (B,).
feats (Tensor): Feature tensor (B, T_feats, aux_channels).
feats_lengths (Tensor): Feature length tensor (B,).
speech (Tensor): Speech waveform tensor (B, T_wav).
speech_lengths (Tensor): Speech length tensor (B,).
forward_generator (bool): Whether to forward generator.
Returns:
Dict[str, Any]:
- loss (Tensor): Loss scalar tensor.
- stats (Dict[str, float]): Statistics to be monitored.
- weight (Tensor): Weight tensor to summarize losses.
- optim_idx (int): Optimizer index (0 for G and 1 for D).
"""
if forward_generator:
return self._forward_generator(
text=text,
text_lengths=text_lengths,
feats=feats,
feats_lengths=feats_lengths,
speech=speech,
speech_lengths=speech_lengths,
**kwargs,
)
else:
return self._forward_discrminator(
text=text,
text_lengths=text_lengths,
feats=feats,
feats_lengths=feats_lengths,
speech=speech,
speech_lengths=speech_lengths,
**kwargs,
)
def _forward_generator(
self,
text: torch.Tensor,
text_lengths: torch.Tensor,
feats: torch.Tensor,
feats_lengths: torch.Tensor,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
**kwargs,
) -> Dict[str, Any]:
"""Perform generator forward.
Args:
text (Tensor): Text index tensor (B, T_text).
text_lengths (Tensor): Text length tensor (B,).
feats (Tensor): Feature tensor (B, T_feats, aux_channels).
feats_lengths (Tensor): Feature length tensor (B,).
speech (Tensor): Speech waveform tensor (B, T_wav).
speech_lengths (Tensor): Speech length tensor (B,).
Returns:
Dict[str, Any]:
* loss (Tensor): Loss scalar tensor.
* stats (Dict[str, float]): Statistics to be monitored.
* weight (Tensor): Weight tensor to summarize losses.
* optim_idx (int): Optimizer index (0 for G and 1 for D).
"""
# setup
batch_size = text.size(0)
speech = speech.unsqueeze(1)
# calculate generator outputs
reuse_cache = True
if not self.cache_generator_outputs or self._cache is None:
reuse_cache = False
# calculate text2mel outputs
text2mel_loss, stats, feats_gen = self.generator["text2mel"](
text=text,
text_lengths=text_lengths,
feats=feats,
feats_lengths=feats_lengths,
joint_training=True,
**kwargs,
)
# get random segments
feats_gen_, start_idxs = get_random_segments(
x=feats_gen.transpose(1, 2),
x_lengths=feats_lengths,
segment_size=self.segment_size,
)
# calculate vocoder outputs
speech_hat_ = self.generator["vocoder"](feats_gen_)
if self.use_pqmf:
speech_hat_ = self.pqmf.synthesis(speech_hat_)
else:
text2mel_loss, stats, speech_hat_, start_idxs = self._cache
# store cache
if self.training and self.cache_generator_outputs and not reuse_cache:
self._cache = (text2mel_loss, stats, speech_hat_, start_idxs)
speech_ = get_segments(
x=speech,
start_idxs=start_idxs * self.generator["vocoder"].upsample_factor,
segment_size=self.segment_size * self.generator["vocoder"].upsample_factor,
)
# calculate discriminator outputs
p_hat = self.discriminator(speech_hat_)
with torch.no_grad():
# do not store discriminator gradient in generator turn
p = self.discriminator(speech_)
# calculate losses
adv_loss = self.generator_adv_loss(p_hat)
adv_loss = adv_loss * self.lambda_adv
text2mel_loss = text2mel_loss * self.lambda_text2mel
loss = adv_loss + text2mel_loss
if self.use_feat_match_loss:
feat_match_loss = self.feat_match_loss(p_hat, p)
feat_match_loss = feat_match_loss * self.lambda_feat_match
loss = loss + feat_match_loss
stats.update(feat_match_loss=feat_match_loss.item())
if self.use_mel_loss:
mel_loss = self.mel_loss(speech_hat_, speech_)
mel_loss = self.lambda_mel * mel_loss
loss = loss + mel_loss
stats.update(mel_loss=mel_loss.item())
stats.update(
adv_loss=adv_loss.item(),
text2mel_loss=text2mel_loss.item(),
loss=loss.item(),
)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
# reset cache
if reuse_cache or not self.training:
self._cache = None
return {
"loss": loss,
"stats": stats,
"weight": weight,
"optim_idx": 0, # needed for trainer
}
def _forward_discrminator(
self,
text: torch.Tensor,
text_lengths: torch.Tensor,
feats: torch.Tensor,
feats_lengths: torch.Tensor,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
**kwargs,
) -> Dict[str, Any]:
"""Perform discriminator forward.
Args:
text (Tensor): Text index tensor (B, T_text).
text_lengths (Tensor): Text length tensor (B,).
feats (Tensor): Feature tensor (B, T_feats, aux_channels).
feats_lengths (Tensor): Feature length tensor (B,).
speech (Tensor): Speech waveform tensor (B, T_wav).
speech_lengths (Tensor): Speech length tensor (B,).
Returns:
Dict[str, Any]:
* loss (Tensor): Loss scalar tensor.
* stats (Dict[str, float]): Statistics to be monitored.
* weight (Tensor): Weight tensor to summarize losses.
* optim_idx (int): Optimizer index (0 for G and 1 for D).
"""
# setup
batch_size = text.size(0)
speech = speech.unsqueeze(1)
# calculate generator outputs
reuse_cache = True
if not self.cache_generator_outputs or self._cache is None:
reuse_cache = False
# calculate text2mel outputs
text2mel_loss, stats, feats_gen = self.generator["text2mel"](
text=text,
text_lengths=text_lengths,
feats=feats,
feats_lengths=feats_lengths,
joint_training=True,
**kwargs,
)
# get random segments
feats_gen_, start_idxs = get_random_segments(
x=feats_gen.transpose(1, 2),
x_lengths=feats_lengths,
segment_size=self.segment_size,
)
# calculate vocoder outputs
speech_hat_ = self.generator["vocoder"](feats_gen_)
if self.use_pqmf:
speech_hat_ = self.pqmf.synthesis(speech_hat_)
else:
_, _, speech_hat_, start_idxs = self._cache
# store cache
if self.cache_generator_outputs and not reuse_cache:
self._cache = (text2mel_loss, stats, speech_hat_, start_idxs)
# parse outputs
speech_ = get_segments(
x=speech,
start_idxs=start_idxs * self.generator["vocoder"].upsample_factor,
segment_size=self.segment_size * self.generator["vocoder"].upsample_factor,
)
# calculate discriminator outputs
p_hat = self.discriminator(speech_hat_.detach())
p = self.discriminator(speech_)
# calculate losses
real_loss, fake_loss = self.discriminator_adv_loss(p_hat, p)
loss = real_loss + fake_loss
stats = dict(
discriminator_loss=loss.item(),
real_loss=real_loss.item(),
fake_loss=fake_loss.item(),
)
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
# reset cache
if reuse_cache or not self.training:
self._cache = None
return {
"loss": loss,
"stats": stats,
"weight": weight,
"optim_idx": 1, # needed for trainer
}
[docs] def inference(
self,
text: torch.Tensor,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""Run inference.
Args:
text (Tensor): Input text index tensor (T_text,).
Returns:
Dict[str, Tensor]:
* wav (Tensor): Generated waveform tensor (T_wav,).
* feat_gan (Tensor): Generated feature tensor (T_text, C).
"""
output_dict = self.generator["text2mel"].inference(
text=text,
**kwargs,
)
wav = self.generator["vocoder"].inference(output_dict["feat_gen"])
if self.use_pqmf:
wav = self.pqmf.synthesis(wav.unsqueeze(0).transpose(1, 2))
wav = wav.squeeze(0).transpose(0, 1)
output_dict.update(wav=wav)
return output_dict