# 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 stats_wrapper
from paddlespeech.resource import CommonTaskResource
from paddlespeech.t2s.exps.syn_utils import get_am_inference
from paddlespeech.t2s.exps.syn_utils import get_frontend
from paddlespeech.t2s.exps.syn_utils import get_sess
from paddlespeech.t2s.exps.syn_utils import get_voc_inference
from paddlespeech.t2s.exps.syn_utils import run_frontend
from paddlespeech.t2s.utils import str2bool
__all__ = ['TTSExecutor']
ONNX_SUPPORT_SET = {
'speedyspeech_csmsc',
'fastspeech2_csmsc',
'fastspeech2_ljspeech',
'fastspeech2_aishell3',
'fastspeech2_vctk',
'fastspeech2_male',
'fastspeech2_mix',
'fastspeech2_canton',
'pwgan_csmsc',
'pwgan_ljspeech',
'pwgan_aishell3',
'pwgan_vctk',
'pwgan_male',
'mb_melgan_csmsc',
'hifigan_csmsc',
'hifigan_ljspeech',
'hifigan_aishell3',
'hifigan_vctk',
'hifigan_male',
}
[docs]class TTSExecutor(BaseExecutor):
def __init__(self):
super().__init__('tts')
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',
'fastspeech2_mix',
'tacotron2_csmsc',
'tacotron2_ljspeech',
'fastspeech2_male',
'fastspeech2_canton',
],
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='hifigan_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',
'pwgan_male',
'hifigan_male',
],
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 or mix')
self.parser.add_argument(
'--device',
type=str,
default=paddle.get_device(),
help='Choose device to execute model inference.')
self.parser.add_argument('--cpu_threads', type=int, default=2)
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.')
self.parser.add_argument(
"--use_onnx",
type=str2bool,
default=False,
help="whether to usen onnxruntime inference.")
self.parser.add_argument(
'--fs',
type=int,
default=24000,
help='sample rate for onnx models when use specified model files.')
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='hifigan_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.debug('Models had been initialized.')
return
# am
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
use_pretrained_am = True
else:
use_pretrained_am = False
am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
skip_download=not use_pretrained_am,
version=None, # default version
)
if use_pretrained_am:
self.am_res_path = self.task_resource.res_dir
self.am_config = os.path.join(self.am_res_path,
self.task_resource.res_dict['config'])
self.am_ckpt = os.path.join(self.am_res_path,
self.task_resource.res_dict['ckpt'])
self.am_stat = os.path.join(
self.am_res_path, self.task_resource.res_dict['speech_stats'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['phones_dict'])
logger.debug(self.am_res_path)
logger.debug(self.am_config)
logger.debug(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(self.am_config)
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in self.task_resource.res_dict:
self.tones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['tones_dict'])
if tones_dict:
self.tones_dict = tones_dict
# for multi speaker fastspeech2
self.speaker_dict = None
if 'speaker_dict' in self.task_resource.res_dict:
self.speaker_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
# voc
if voc_ckpt is None or voc_config is None or voc_stat is None:
use_pretrained_voc = True
else:
use_pretrained_voc = False
voc_lang = lang
# When speaker is 174 (csmsc), use csmsc's vocoder is better than aishell3's
if lang == 'mix' or lang == 'canton':
voc_dataset = voc[voc.rindex('_') + 1:]
if voc_dataset in {"ljspeech", "vctk"}:
voc_lang = 'en'
else:
voc_lang = 'zh'
voc_tag = voc + '-' + voc_lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
skip_download=not use_pretrained_voc,
version=None, # default version
)
if use_pretrained_voc:
self.voc_res_path = self.task_resource.voc_res_dir
self.voc_config = os.path.join(
self.voc_res_path, self.task_resource.voc_res_dict['config'])
self.voc_ckpt = os.path.join(
self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
self.voc_stat = os.path.join(
self.voc_res_path,
self.task_resource.voc_res_dict['speech_stats'])
logger.debug(self.voc_res_path)
logger.debug(self.voc_config)
logger.debug(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, 'rt', encoding='utf-8') as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
tone_size = None
if self.tones_dict:
with open(self.tones_dict, 'rt', encoding='utf-8') as f:
tone_id = [line.strip().split() for line in f.readlines()]
tone_size = len(tone_id)
spk_num = None
if self.speaker_dict:
with open(self.speaker_dict, 'rt', encoding='utf-8') as f:
spk_id = [line.strip().split() for line in f.readlines()]
spk_num = len(spk_id)
# frontend
self.frontend = get_frontend(
lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
# acoustic model
self.am_inference = get_am_inference(
am=am,
am_config=self.am_config,
am_ckpt=self.am_ckpt,
am_stat=self.am_stat,
phones_dict=self.phones_dict,
tones_dict=self.tones_dict,
speaker_dict=self.speaker_dict)
# vocoder
self.voc_inference = get_voc_inference(
voc=voc,
voc_config=self.voc_config,
voc_ckpt=self.voc_ckpt,
voc_stat=self.voc_stat)
def _init_from_path_onnx(self,
am: str='fastspeech2_csmsc',
am_ckpt: Optional[os.PathLike]=None,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
voc: str='hifigan_csmsc',
voc_ckpt: Optional[os.PathLike]=None,
lang: str='zh',
device: str='cpu',
cpu_threads: int=2,
fs: int=24000):
if hasattr(self, 'am_sess') and hasattr(self, 'voc_sess'):
logger.debug('Models had been initialized.')
return
# am
if am_ckpt is None or phones_dict is None:
use_pretrained_am = True
else:
use_pretrained_am = False
am_tag = am + '_onnx' + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
skip_download=not use_pretrained_am,
version=None, # default version
)
if use_pretrained_am:
self.am_res_path = self.task_resource.res_dir
self.am_ckpt = os.path.join(self.am_res_path,
self.task_resource.res_dict['ckpt'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['phones_dict'])
self.am_fs = self.task_resource.res_dict['sample_rate']
logger.debug(self.am_res_path)
logger.debug(self.am_ckpt)
else:
self.am_ckpt = os.path.abspath(am_ckpt)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(self.am_ckpt)
self.am_fs = fs
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in self.task_resource.res_dict:
self.tones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['tones_dict'])
if tones_dict:
self.tones_dict = tones_dict
# voc
if voc_ckpt is None:
use_pretrained_voc = True
else:
use_pretrained_voc = False
voc_lang = lang
if lang == 'mix' or lang == 'canton':
voc_dataset = voc[voc.rindex('_') + 1:]
if voc_dataset in {"ljspeech", "vctk"}:
voc_lang = 'en'
else:
voc_lang = 'zh'
voc_tag = voc + '_onnx' + '-' + voc_lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
skip_download=not use_pretrained_voc,
version=None, # default version
)
if use_pretrained_voc:
self.voc_res_path = self.task_resource.voc_res_dir
self.voc_ckpt = os.path.join(
self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
logger.debug(self.voc_res_path)
logger.debug(self.voc_ckpt)
else:
self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
# frontend
self.frontend = get_frontend(
lang=lang, phones_dict=self.phones_dict, tones_dict=self.tones_dict)
self.am_sess = get_sess(
model_path=self.am_ckpt, device=device, cpu_threads=cpu_threads)
# vocoder
self.voc_sess = get_sess(
model_path=self.voc_ckpt, device=device, cpu_threads=cpu_threads)
[docs] 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
[docs] @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:]
merge_sentences = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
frontend_st = time.time()
frontend_dict = run_frontend(
frontend=self.frontend,
text=text,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang)
self.frontend_time = time.time() - frontend_st
self.am_time = 0
self.voc_time = 0
flags = 0
phone_ids = frontend_dict['phone_ids']
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 = frontend_dict['tone_ids'][i]
mel = self.am_inference(part_phone_ids, part_tone_ids)
# fastspeech2
else:
# multi speaker
if am_dataset in {'aishell3', 'vctk', 'mix', 'canton'}:
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
[docs] def infer_onnx(self,
text: str,
lang: str='zh',
am: str='fastspeech2_csmsc',
spk_id: int=0):
am_name = am[:am.rindex('_')]
am_dataset = am[am.rindex('_') + 1:]
merge_sentences = False
get_tone_ids = False
if am_name == 'speedyspeech':
get_tone_ids = True
am_input_feed = {}
frontend_st = time.time()
frontend_dict = run_frontend(
frontend=self.frontend,
text=text,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids,
lang=lang,
to_tensor=False, )
self.frontend_time = time.time() - frontend_st
phone_ids = frontend_dict['phone_ids']
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]
if am_name == 'fastspeech2':
am_input_feed.update({'text': part_phone_ids})
if am_dataset in {"aishell3", "vctk", "mix", "canton"}:
# NOTE: 'spk_id' should be List[int] rather than int here!!
am_input_feed.update({'spk_id': [spk_id]})
elif am_name == 'speedyspeech':
part_tone_ids = frontend_dict['tone_ids'][i]
am_input_feed.update({
'phones': part_phone_ids,
'tones': part_tone_ids
})
mel = self.am_sess.run(output_names=None, input_feed=am_input_feed)
mel = mel[0]
self.am_time += (time.time() - am_st)
# voc
voc_st = time.time()
wav = self.voc_sess.run(
output_names=None, input_feed={'logmel': mel})
wav = wav[0]
if flags == 0:
wav_all = wav
flags = 1
else:
wav_all = np.concatenate([wav_all, wav])
self.voc_time += (time.time() - voc_st)
self._outputs['wav'] = wav_all
[docs] 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
[docs] def postprocess_onnx(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'], samplerate=self.am_fs)
return output
# 命令行的入口是这里
[docs] 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
use_onnx = args.use_onnx
cpu_threads = args.cpu_threads
fs = args.fs
if not args.verbose:
self.disable_task_loggers()
task_source = self.get_input_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,
use_onnx=use_onnx,
cpu_threads=cpu_threads,
fs=fs)
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
# pyton api 的入口是这里
@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='hifigan_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',
use_onnx: bool=False,
cpu_threads: int=2,
fs: int=24000):
"""
Python API to call an executor.
"""
if not use_onnx:
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
else:
# use onnx
# we use `cpu` for onnxruntime by default
# please see description in https://github.com/PaddlePaddle/PaddleSpeech/pull/2220
self.task_resource = CommonTaskResource(
task='tts', model_format='onnx')
assert (
am in ONNX_SUPPORT_SET and voc in ONNX_SUPPORT_SET
), f'the am and voc you choose, they should be in {ONNX_SUPPORT_SET}'
self._init_from_path_onnx(
am=am,
am_ckpt=am_ckpt,
phones_dict=phones_dict,
tones_dict=tones_dict,
speaker_dict=speaker_dict,
voc=voc,
voc_ckpt=voc_ckpt,
lang=lang,
device=device,
cpu_threads=cpu_threads,
fs=fs)
self.infer_onnx(text=text, lang=lang, am=am, spk_id=spk_id)
res = self.postprocess_onnx(output=output)
return res