# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import time
from collections import OrderedDict
from typing import Any
from typing import List
from typing import Optional
from typing import Union
import numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.modules.normalizer import ZScore
__all__ = ['TTSExecutor']
@cli_register(
name='paddlespeech.tts', description='Text to Speech infer command.')
class TTSExecutor(BaseExecutor):
def __init__(self):
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.tts', add_help=True)
self.parser.add_argument(
'--input', type=str, default=None, help='Input text to generate.')
# acoustic model
self.parser.add_argument(
'--am',
type=str,
default='fastspeech2_csmsc',
choices=[
'speedyspeech_csmsc',
'fastspeech2_csmsc',
'fastspeech2_ljspeech',
'fastspeech2_aishell3',
'fastspeech2_vctk',
'tacotron2_csmsc',
'tacotron2_ljspeech',
],
help='Choose acoustic model type of tts task.')
self.parser.add_argument(
'--am_config',
type=str,
default=None,
help='Config of acoustic model. Use deault config when it is None.')
self.parser.add_argument(
'--am_ckpt',
type=str,
default=None,
help='Checkpoint file of acoustic model.')
self.parser.add_argument(
"--am_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
)
self.parser.add_argument(
"--phones_dict",
type=str,
default=None,
help="phone vocabulary file.")
self.parser.add_argument(
"--tones_dict",
type=str,
default=None,
help="tone vocabulary file.")
self.parser.add_argument(
"--speaker_dict",
type=str,
default=None,
help="speaker id map file.")
self.parser.add_argument(
'--spk_id',
type=int,
default=0,
help='spk id for multi speaker acoustic model')
# vocoder
self.parser.add_argument(
'--voc',
type=str,
default='pwgan_csmsc',
choices=[
'pwgan_csmsc',
'pwgan_ljspeech',
'pwgan_aishell3',
'pwgan_vctk',
'mb_melgan_csmsc',
'style_melgan_csmsc',
'hifigan_csmsc',
'hifigan_ljspeech',
'hifigan_aishell3',
'hifigan_vctk',
'wavernn_csmsc',
],
help='Choose vocoder type of tts task.')
self.parser.add_argument(
'--voc_config',
type=str,
default=None,
help='Config of voc. Use deault config when it is None.')
self.parser.add_argument(
'--voc_ckpt',
type=str,
default=None,
help='Checkpoint file of voc.')
self.parser.add_argument(
"--voc_stat",
type=str,
default=None,
help="mean and standard deviation used to normalize spectrogram when training voc."
)
# other
self.parser.add_argument(
'--lang',
type=str,
default='zh',
help='Choose model language. zh or en')
self.parser.add_argument(
'--device',
type=str,
default=paddle.get_device(),
help='Choose device to execute model inference.')
self.parser.add_argument(
'--output', type=str, default='output.wav', help='output file name')
self.parser.add_argument(
'-d',
'--job_dump_result',
action='store_true',
help='Save job result into file.')
self.parser.add_argument(
'-v',
'--verbose',
action='store_true',
help='Increase logger verbosity of current task.')
def _init_from_path(
self,
am: str='fastspeech2_csmsc',
am_config: Optional[os.PathLike]=None,
am_ckpt: Optional[os.PathLike]=None,
am_stat: Optional[os.PathLike]=None,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
voc: str='pwgan_csmsc',
voc_config: Optional[os.PathLike]=None,
voc_ckpt: Optional[os.PathLike]=None,
voc_stat: Optional[os.PathLike]=None,
lang: str='zh', ):
"""
Init model and other resources from a specific path.
"""
if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
logger.info('Models had been initialized.')
return
# am
am_tag = am + '-' + lang
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
am_res_path = self._get_pretrained_path(am_tag)
self.am_res_path = am_res_path
self.am_config = os.path.join(
am_res_path, self.pretrained_models[am_tag]['config'])
self.am_ckpt = os.path.join(am_res_path,
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speech_stats'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
logger.info(am_res_path)
logger.info(self.am_config)
logger.info(self.am_ckpt)
else:
self.am_config = os.path.abspath(am_config)
self.am_ckpt = os.path.abspath(am_ckpt)
self.am_stat = os.path.abspath(am_stat)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in self.pretrained_models[am_tag]:
self.tones_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['tones_dict'])
if tones_dict:
self.tones_dict = tones_dict
# for multi speaker fastspeech2
self.speaker_dict = None
if 'speaker_dict' in self.pretrained_models[am_tag]:
self.speaker_dict = os.path.join(
am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
# voc
voc_tag = voc + '-' + lang
if voc_ckpt is None or voc_config is None or voc_stat is None:
voc_res_path = self._get_pretrained_path(voc_tag)
self.voc_res_path = voc_res_path
self.voc_config = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt'])
self.voc_stat = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
logger.info(voc_res_path)
logger.info(self.voc_config)
logger.info(self.voc_ckpt)
else:
self.voc_config = os.path.abspath(voc_config)
self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_stat = os.path.abspath(voc_stat)
self.voc_res_path = os.path.dirname(
os.path.abspath(self.voc_config))
# Init body.
with open(self.am_config) as f:
self.am_config = CfgNode(yaml.safe_load(f))
with open(self.voc_config) as f:
self.voc_config = CfgNode(yaml.safe_load(f))
with open(self.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
print("vocab_size:", vocab_size)
tone_size = None
if self.tones_dict:
with open(self.tones_dict, "r") as f:
tone_id = [line.strip().split() for line in f.readlines()]
tone_size = len(tone_id)
print("tone_size:", tone_size)
spk_num = None
if self.speaker_dict:
with open(self.speaker_dict, 'rt') as f:
spk_id = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id)
print("spk_num:", spk_num)
# frontend
if lang == 'zh':
self.frontend = Frontend(
phone_vocab_path=self.phones_dict,
tone_vocab_path=self.tones_dict)
elif lang == 'en':
self.frontend = English(phone_vocab_path=self.phones_dict)
print("frontend done!")
# acoustic model
odim = self.am_config.n_mels
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_class = dynamic_import(am_name, self.model_alias)
am_inference_class = dynamic_import(am_name + '_inference',
self.model_alias)
if am_name == 'fastspeech2':
am = am_class(
idim=vocab_size,
odim=odim,
spk_num=spk_num,
**self.am_config["model"])
elif am_name == 'speedyspeech':
am = am_class(
vocab_size=vocab_size,
tone_size=tone_size,
**self.am_config["model"])
elif am_name == 'tacotron2':
am = am_class(idim=vocab_size, odim=odim, **self.am_config["model"])
am.set_state_dict(paddle.load(self.am_ckpt)["main_params"])
am.eval()
am_mu, am_std = np.load(self.am_stat)
am_mu = paddle.to_tensor(am_mu)
am_std = paddle.to_tensor(am_std)
am_normalizer = ZScore(am_mu, am_std)
self.am_inference = am_inference_class(am_normalizer, am)
self.am_inference.eval()
print("acoustic model done!")
# vocoder
# model: {model_name}_{dataset}
voc_name = voc[:voc.rindex('_')]
voc_class = dynamic_import(voc_name, self.model_alias)
voc_inference_class = dynamic_import(voc_name + '_inference',
self.model_alias)
if voc_name != 'wavernn':
voc = voc_class(**self.voc_config["generator_params"])
voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
voc.remove_weight_norm()
voc.eval()
else:
voc = voc_class(**self.voc_config["model"])
voc.set_state_dict(paddle.load(self.voc_ckpt)["main_params"])
voc.eval()
voc_mu, voc_std = np.load(self.voc_stat)
voc_mu = paddle.to_tensor(voc_mu)
voc_std = paddle.to_tensor(voc_std)
voc_normalizer = ZScore(voc_mu, voc_std)
self.voc_inference = voc_inference_class(voc_normalizer, voc)
self.voc_inference.eval()
print("voc done!")
def preprocess(self, input: Any, *args, **kwargs):
"""
Input preprocess and return paddle.Tensor stored in self._inputs.
Input content can be a text(tts), a file(asr, cls), a stream(not supported yet) or anything needed.
Args:
input (Any): Input text/file/stream or other content.
"""
pass
@paddle.no_grad()
def infer(self,
text: str,
lang: str='zh',
am: str='fastspeech2_csmsc',
spk_id: int=0):
"""
Model inference and result stored in self.output.
"""
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
get_tone_ids = False
merge_sentences = False
frontend_st = time.time()
if am_name == 'speedyspeech':
get_tone_ids = True
if lang == 'zh':
input_ids = self.frontend.get_input_ids(
text,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids)
phone_ids = input_ids["phone_ids"]
if get_tone_ids:
tone_ids = input_ids["tone_ids"]
elif lang == 'en':
input_ids = self.frontend.get_input_ids(
text, merge_sentences=merge_sentences)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en'}!")
self.frontend_time = time.time() - frontend_st
self.am_time = 0
self.voc_time = 0
flags = 0
for i in range(len(phone_ids)):
am_st = time.time()
part_phone_ids = phone_ids[i]
# am
if am_name == 'speedyspeech':
part_tone_ids = tone_ids[i]
mel = self.am_inference(part_phone_ids, part_tone_ids)
# fastspeech2
else:
# multi speaker
if am_dataset in {"aishell3", "vctk"}:
mel = self.am_inference(
part_phone_ids, spk_id=paddle.to_tensor(spk_id))
else:
mel = self.am_inference(part_phone_ids)
self.am_time += (time.time() - am_st)
# voc
voc_st = time.time()
wav = self.voc_inference(mel)
if flags == 0:
wav_all = wav
flags = 1
else:
wav_all = paddle.concat([wav_all, wav])
self.voc_time += (time.time() - voc_st)
self._outputs['wav'] = wav_all
def postprocess(self, output: str='output.wav') -> Union[str, os.PathLike]:
"""
Output postprocess and return results.
This method get model output from self._outputs and convert it into human-readable results.
Returns:
Union[str, os.PathLike]: Human-readable results such as texts and audio files.
"""
output = os.path.abspath(os.path.expanduser(output))
sf.write(
output, self._outputs['wav'].numpy(), samplerate=self.am_config.fs)
return output
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
args = self.parser.parse_args(argv)
am = args.am
am_config = args.am_config
am_ckpt = args.am_ckpt
am_stat = args.am_stat
phones_dict = args.phones_dict
tones_dict = args.tones_dict
speaker_dict = args.speaker_dict
voc = args.voc
voc_config = args.voc_config
voc_ckpt = args.voc_ckpt
voc_stat = args.voc_stat
lang = args.lang
device = args.device
spk_id = args.spk_id
if not args.verbose:
self.disable_task_loggers()
task_source = self.get_task_source(args.input)
task_results = OrderedDict()
has_exceptions = False
for id_, input_ in task_source.items():
if len(task_source) > 1:
assert isinstance(args.output,
str) and args.output.endswith('.wav')
output = args.output.replace('.wav', f'_{id_}.wav')
else:
output = args.output
try:
res = self(
text=input_,
# acoustic model related
am=am,
am_config=am_config,
am_ckpt=am_ckpt,
am_stat=am_stat,
phones_dict=phones_dict,
tones_dict=tones_dict,
speaker_dict=speaker_dict,
spk_id=spk_id,
# vocoder related
voc=voc,
voc_config=voc_config,
voc_ckpt=voc_ckpt,
voc_stat=voc_stat,
# other
lang=lang,
device=device,
output=output)
task_results[id_] = res
except Exception as e:
has_exceptions = True
task_results[id_] = f'{e.__class__.__name__}: {e}'
self.process_task_results(args.input, task_results,
args.job_dump_result)
if has_exceptions:
return False
else:
return True
@stats_wrapper
def __call__(self,
text: str,
am: str='fastspeech2_csmsc',
am_config: Optional[os.PathLike]=None,
am_ckpt: Optional[os.PathLike]=None,
am_stat: Optional[os.PathLike]=None,
spk_id: int=0,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
voc: str='pwgan_csmsc',
voc_config: Optional[os.PathLike]=None,
voc_ckpt: Optional[os.PathLike]=None,
voc_stat: Optional[os.PathLike]=None,
lang: str='zh',
device: str=paddle.get_device(),
output: str='output.wav'):
"""
Python API to call an executor.
"""
paddle.set_device(device)
self._init_from_path(
am=am,
am_config=am_config,
am_ckpt=am_ckpt,
am_stat=am_stat,
phones_dict=phones_dict,
tones_dict=tones_dict,
speaker_dict=speaker_dict,
voc=voc,
voc_config=voc_config,
voc_ckpt=voc_ckpt,
voc_stat=voc_stat,
lang=lang)
self.infer(text=text, lang=lang, am=am, spk_id=spk_id)
res = self.postprocess(output=output)
return res