"""Text-to-speech task."""
import argparse
import logging
from pathlib import Path
from typing import Callable, Collection, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import yaml
from typeguard import typechecked
from espnet2.gan_tts.jets import JETS
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.tasks.abs_task import AbsTask
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.preprocessor import CommonPreprocessor
from espnet2.train.trainer import Trainer
from espnet2.tts.abs_tts import AbsTTS
from espnet2.tts.espnet_model import ESPnetTTSModel
from espnet2.tts.fastspeech import FastSpeech
from espnet2.tts.fastspeech2 import FastSpeech2
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.tts.prodiff import ProDiff
from espnet2.tts.tacotron2 import Tacotron2
from espnet2.tts.transformer import Transformer
from espnet2.tts.utils import ParallelWaveGANPretrainedVocoder
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet2.utils.griffin_lim import Spectrogram2Waveform
from espnet2.utils.nested_dict_action import NestedDictAction
from espnet2.utils.types import int_or_none, str2bool, str_or_none
feats_extractor_choices = ClassChoices(
"feats_extract",
classes=dict(
fbank=LogMelFbank,
spectrogram=LogSpectrogram,
linear_spectrogram=LinearSpectrogram,
),
type_check=AbsFeatsExtract,
default="fbank",
)
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,
)
normalize_choices = ClassChoices(
"normalize",
classes=dict(global_mvn=GlobalMVN),
type_check=AbsNormalize,
default="global_mvn",
optional=True,
)
pitch_normalize_choices = ClassChoices(
"pitch_normalize",
classes=dict(global_mvn=GlobalMVN),
type_check=AbsNormalize,
default=None,
optional=True,
)
energy_normalize_choices = ClassChoices(
"energy_normalize",
classes=dict(global_mvn=GlobalMVN),
type_check=AbsNormalize,
default=None,
optional=True,
)
tts_choices = ClassChoices(
"tts",
classes=dict(
tacotron2=Tacotron2,
transformer=Transformer,
fastspeech=FastSpeech,
fastspeech2=FastSpeech2,
prodiff=ProDiff,
# NOTE(kan-bayashi): available only for inference
vits=VITS,
joint_text2wav=JointText2Wav,
jets=JETS,
),
type_check=AbsTTS,
default="tacotron2",
)
[docs]class TTSTask(AbsTask):
# If you need more than one optimizers, change this value
num_optimizers: int = 1
# 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,
]
# If you need to modify train() or eval() procedures, change Trainer class here
trainer = Trainer
[docs] @classmethod
@typechecked
def add_task_arguments(cls, parser: argparse.ArgumentParser):
# NOTE(kamo): Use '_' instead of '-' to avoid confusion
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(ESPnetTTSModel),
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
@typechecked
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]],
]:
return CommonCollateFn(
float_pad_value=0.0,
int_pad_value=0,
not_sequence=["spembs", "sids", "lids"],
)
[docs] @classmethod
@typechecked
def build_preprocess_fn(
cls, args: argparse.Namespace, train: bool
) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
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
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",
"durations",
"pitch",
"energy",
"sids",
"lids",
)
else:
# Inference mode
retval = (
"spembs",
"speech",
"durations",
"pitch",
"energy",
"sids",
"lids",
)
return retval
[docs] @classmethod
@typechecked
def build_model(cls, args: argparse.Namespace) -> ESPnetTTSModel:
if isinstance(args.token_list, str):
with open(args.token_list, encoding="utf-8") as f:
token_list = [line[0] + line[1:].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)
if args.pitch_extract_conf.get("reduction_factor", None) is not None:
assert args.pitch_extract_conf.get(
"reduction_factor", None
) == args.tts_conf.get("reduction_factor", 1)
else:
args.pitch_extract_conf["reduction_factor"] = args.tts_conf.get(
"reduction_factor", 1
)
pitch_extract = pitch_extract_class(**args.pitch_extract_conf)
if getattr(args, "energy_extract", None) is not None:
if args.energy_extract_conf.get("reduction_factor", None) is not None:
assert args.energy_extract_conf.get(
"reduction_factor", None
) == args.tts_conf.get("reduction_factor", 1)
else:
args.energy_extract_conf["reduction_factor"] = args.tts_conf.get(
"reduction_factor", 1
)
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 = ESPnetTTSModel(
feats_extract=feats_extract,
pitch_extract=pitch_extract,
energy_extract=energy_extract,
normalize=normalize,
pitch_normalize=pitch_normalize,
energy_normalize=energy_normalize,
tts=tts,
**args.model_conf,
)
return model
[docs] @classmethod
def build_vocoder_from_file(
cls,
vocoder_config_file: Union[Path, str] = None,
vocoder_file: Union[Path, str] = None,
model: Optional[ESPnetTTSModel] = None,
device: str = "cpu",
):
# Build vocoder
if vocoder_file is None:
# If vocoder file is not provided, use griffin-lim as a vocoder
vocoder_conf = {}
if vocoder_config_file is not None:
vocoder_config_file = Path(vocoder_config_file)
with vocoder_config_file.open("r", encoding="utf-8") as f:
vocoder_conf = yaml.safe_load(f)
if model.feats_extract is not None:
vocoder_conf.update(model.feats_extract.get_parameters())
if (
"n_fft" in vocoder_conf
and "n_shift" in vocoder_conf
and "fs" in vocoder_conf
):
return Spectrogram2Waveform(**vocoder_conf)
else:
logging.warning("Vocoder is not available. Skipped its building.")
return None
elif str(vocoder_file).endswith(".pkl"):
# If the extension is ".pkl", the model is trained with parallel_wavegan
vocoder = ParallelWaveGANPretrainedVocoder(
vocoder_file, vocoder_config_file
)
return vocoder.to(device)
else:
raise ValueError(f"{vocoder_file} is not supported format.")