# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""GAN-based text-to-speech task."""
import argparse
import logging
from typing import Callable
from typing import Collection
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from espnet2.gan_tts.abs_gan_tts import AbsGANTTS
from espnet2.gan_tts.espnet_model import ESPnetGANTTSModel
from espnet2.gan_tts.joint import JointText2Wav
from espnet2.gan_tts.vits import VITS
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.layers.global_mvn import GlobalMVN
from espnet2.layers.utterance_mvn import UtteranceMVN
from espnet2.tasks.abs_task import AbsTask
from espnet2.tasks.abs_task import optim_classes
from espnet2.text.phoneme_tokenizer import g2p_choices
from espnet2.train.class_choices import ClassChoices
from espnet2.train.collate_fn import CommonCollateFn
from espnet2.train.gan_trainer import GANTrainer
from espnet2.train.preprocessor import CommonPreprocessor
from espnet2.tts.feats_extract.abs_feats_extract import AbsFeatsExtract
from espnet2.tts.feats_extract.dio import Dio
from espnet2.tts.feats_extract.energy import Energy
from espnet2.tts.feats_extract.linear_spectrogram import LinearSpectrogram
from espnet2.tts.feats_extract.log_mel_fbank import LogMelFbank
from espnet2.tts.feats_extract.log_spectrogram import LogSpectrogram
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet2.utils.nested_dict_action import NestedDictAction
from espnet2.utils.types import int_or_none
from espnet2.utils.types import str2bool
from espnet2.utils.types import str_or_none
feats_extractor_choices = ClassChoices(
"feats_extract",
classes=dict(
fbank=LogMelFbank,
log_spectrogram=LogSpectrogram,
linear_spectrogram=LinearSpectrogram,
),
type_check=AbsFeatsExtract,
default="linear_spectrogram",
)
normalize_choices = ClassChoices(
"normalize",
classes=dict(
global_mvn=GlobalMVN,
utterance_mvn=UtteranceMVN,
),
type_check=AbsNormalize,
default=None,
optional=True,
)
tts_choices = ClassChoices(
"tts",
classes=dict(
vits=VITS,
joint_text2wav=JointText2Wav,
),
type_check=AbsGANTTS,
default="vits",
)
pitch_extractor_choices = ClassChoices(
"pitch_extract",
classes=dict(dio=Dio),
type_check=AbsFeatsExtract,
default=None,
optional=True,
)
energy_extractor_choices = ClassChoices(
"energy_extract",
classes=dict(energy=Energy),
type_check=AbsFeatsExtract,
default=None,
optional=True,
)
pitch_normalize_choices = ClassChoices(
"pitch_normalize",
classes=dict(
global_mvn=GlobalMVN,
utterance_mvn=UtteranceMVN,
),
type_check=AbsNormalize,
default=None,
optional=True,
)
energy_normalize_choices = ClassChoices(
"energy_normalize",
classes=dict(
global_mvn=GlobalMVN,
utterance_mvn=UtteranceMVN,
),
type_check=AbsNormalize,
default=None,
optional=True,
)
[docs]class GANTTSTask(AbsTask):
"""GAN-based text-to-speech task."""
# GAN requires two optimizers
num_optimizers: int = 2
# Add variable objects configurations
class_choices_list = [
# --feats_extractor and --feats_extractor_conf
feats_extractor_choices,
# --normalize and --normalize_conf
normalize_choices,
# --tts and --tts_conf
tts_choices,
# --pitch_extract and --pitch_extract_conf
pitch_extractor_choices,
# --pitch_normalize and --pitch_normalize_conf
pitch_normalize_choices,
# --energy_extract and --energy_extract_conf
energy_extractor_choices,
# --energy_normalize and --energy_normalize_conf
energy_normalize_choices,
]
# Use GANTrainer instead of Trainer
trainer = GANTrainer
[docs] @classmethod
def add_task_arguments(cls, parser: argparse.ArgumentParser):
# NOTE(kamo): Use '_' instead of '-' to avoid confusion
assert check_argument_types()
group = parser.add_argument_group(description="Task related")
# NOTE(kamo): add_arguments(..., required=True) can't be used
# to provide --print_config mode. Instead of it, do as
required = parser.get_default("required")
required += ["token_list"]
group.add_argument(
"--token_list",
type=str_or_none,
default=None,
help="A text mapping int-id to token",
)
group.add_argument(
"--odim",
type=int_or_none,
default=None,
help="The number of dimension of output feature",
)
group.add_argument(
"--model_conf",
action=NestedDictAction,
default=get_default_kwargs(ESPnetGANTTSModel),
help="The keyword arguments for model class.",
)
group = parser.add_argument_group(description="Preprocess related")
group.add_argument(
"--use_preprocessor",
type=str2bool,
default=True,
help="Apply preprocessing to data or not",
)
group.add_argument(
"--token_type",
type=str,
default="phn",
choices=["bpe", "char", "word", "phn"],
help="The text will be tokenized in the specified level token",
)
group.add_argument(
"--bpemodel",
type=str_or_none,
default=None,
help="The model file of sentencepiece",
)
parser.add_argument(
"--non_linguistic_symbols",
type=str_or_none,
help="non_linguistic_symbols file path",
)
parser.add_argument(
"--cleaner",
type=str_or_none,
choices=[None, "tacotron", "jaconv", "vietnamese", "korean_cleaner"],
default=None,
help="Apply text cleaning",
)
parser.add_argument(
"--g2p",
type=str_or_none,
choices=g2p_choices,
default=None,
help="Specify g2p method if --token_type=phn",
)
for class_choices in cls.class_choices_list:
# Append --<name> and --<name>_conf.
# e.g. --encoder and --encoder_conf
class_choices.add_arguments(group)
[docs] @classmethod
def build_collate_fn(
cls, args: argparse.Namespace, train: bool
) -> Callable[
[Collection[Tuple[str, Dict[str, np.ndarray]]]],
Tuple[List[str], Dict[str, torch.Tensor]],
]:
assert check_argument_types()
return CommonCollateFn(
float_pad_value=0.0, int_pad_value=0, not_sequence=["spembs", "sids"]
)
[docs] @classmethod
def build_preprocess_fn(
cls, args: argparse.Namespace, train: bool
) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
assert check_argument_types()
if args.use_preprocessor:
retval = CommonPreprocessor(
train=train,
token_type=args.token_type,
token_list=args.token_list,
bpemodel=args.bpemodel,
non_linguistic_symbols=args.non_linguistic_symbols,
text_cleaner=args.cleaner,
g2p_type=args.g2p,
)
else:
retval = None
assert check_return_type(retval)
return retval
[docs] @classmethod
def required_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
if not inference:
retval = ("text", "speech")
else:
# Inference mode
retval = ("text",)
return retval
[docs] @classmethod
def optional_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
if not inference:
retval = ("spembs", "sids", "durations", "pitch", "energy")
else:
# Inference mode
retval = ("spembs", "sids", "speech", "durations", "pitch", "energy")
return retval
[docs] @classmethod
def build_model(cls, args: argparse.Namespace) -> ESPnetGANTTSModel:
assert check_argument_types()
if isinstance(args.token_list, str):
with open(args.token_list, encoding="utf-8") as f:
token_list = [line.rstrip() for line in f]
# "args" is saved as it is in a yaml file by BaseTask.main().
# Overwriting token_list to keep it as "portable".
args.token_list = token_list.copy()
elif isinstance(args.token_list, (tuple, list)):
token_list = args.token_list.copy()
else:
raise RuntimeError("token_list must be str or dict")
vocab_size = len(token_list)
logging.info(f"Vocabulary size: {vocab_size }")
# 1. feats_extract
if args.odim is None:
# Extract features in the model
feats_extract_class = feats_extractor_choices.get_class(args.feats_extract)
feats_extract = feats_extract_class(**args.feats_extract_conf)
odim = feats_extract.output_size()
else:
# Give features from data-loader
args.feats_extract = None
args.feats_extract_conf = None
feats_extract = None
odim = args.odim
# 2. Normalization layer
if args.normalize is not None:
normalize_class = normalize_choices.get_class(args.normalize)
normalize = normalize_class(**args.normalize_conf)
else:
normalize = None
# 3. TTS
tts_class = tts_choices.get_class(args.tts)
tts = tts_class(idim=vocab_size, odim=odim, **args.tts_conf)
# 4. Extra components
pitch_extract = None
energy_extract = None
pitch_normalize = None
energy_normalize = None
if getattr(args, "pitch_extract", None) is not None:
pitch_extract_class = pitch_extractor_choices.get_class(
args.pitch_extract,
)
pitch_extract = pitch_extract_class(
**args.pitch_extract_conf,
)
if getattr(args, "energy_extract", None) is not None:
energy_extract_class = energy_extractor_choices.get_class(
args.energy_extract,
)
energy_extract = energy_extract_class(
**args.energy_extract_conf,
)
if getattr(args, "pitch_normalize", None) is not None:
pitch_normalize_class = pitch_normalize_choices.get_class(
args.pitch_normalize,
)
pitch_normalize = pitch_normalize_class(
**args.pitch_normalize_conf,
)
if getattr(args, "energy_normalize", None) is not None:
energy_normalize_class = energy_normalize_choices.get_class(
args.energy_normalize,
)
energy_normalize = energy_normalize_class(
**args.energy_normalize_conf,
)
# 5. Build model
model = ESPnetGANTTSModel(
feats_extract=feats_extract,
normalize=normalize,
pitch_extract=pitch_extract,
pitch_normalize=pitch_normalize,
energy_extract=energy_extract,
energy_normalize=energy_normalize,
tts=tts,
**args.model_conf,
)
assert check_return_type(model)
return model
[docs] @classmethod
def build_optimizers(
cls,
args: argparse.Namespace,
model: ESPnetGANTTSModel,
) -> List[torch.optim.Optimizer]:
# check
assert hasattr(model.tts, "generator")
assert hasattr(model.tts, "discriminator")
# define generator optimizer
optim_g_class = optim_classes.get(args.optim)
if optim_g_class is None:
raise ValueError(f"must be one of {list(optim_classes)}: {args.optim}")
if args.sharded_ddp:
try:
import fairscale
except ImportError:
raise RuntimeError("Requiring fairscale. Do 'pip install fairscale'")
optim_g = fairscale.optim.oss.OSS(
params=model.tts.generator.parameters(),
optim=optim_g_class,
**args.optim_conf,
)
else:
optim_g = optim_g_class(
model.tts.generator.parameters(),
**args.optim_conf,
)
optimizers = [optim_g]
# define discriminator optimizer
optim_d_class = optim_classes.get(args.optim2)
if optim_d_class is None:
raise ValueError(f"must be one of {list(optim_classes)}: {args.optim2}")
if args.sharded_ddp:
try:
import fairscale
except ImportError:
raise RuntimeError("Requiring fairscale. Do 'pip install fairscale'")
optim_d = fairscale.optim.oss.OSS(
params=model.tts.discriminator.parameters(),
optim=optim_d_class,
**args.optim2_conf,
)
else:
optim_d = optim_d_class(
model.tts.discriminator.parameters(),
**args.optim2_conf,
)
optimizers += [optim_d]
return optimizers