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PaddleSpeech/paddlespeech/server/speechserving/engine/tts/paddleinference/tts_engine.py

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# 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 base64
import io
import os
from typing import Optional
import librosa
import numpy as np
import paddle
import soundfile as sf
from engine.base_engine import BaseEngine
from scipy.io import wavfile
from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.cli.utils import download_and_decompress
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from utils.audio_process import change_speed
from utils.config import get_config
from utils.errors import ErrorCode
from utils.exception import ServerBaseException
from utils.paddle_predictor import init_predictor
from utils.paddle_predictor import run_model
__all__ = ['TTSEngine']
# Static model applied on paddle inference
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip',
'md5':
'f10cbdedf47dc7a9668d2264494e1823',
'model':
'speedyspeech_csmsc.pdmodel',
'params':
'speedyspeech_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
'sample_rate':
24000,
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip',
'md5':
'9788cd9745e14c7a5d12d32670b2a5a7',
'model':
'fastspeech2_csmsc.pdmodel',
'params':
'fastspeech2_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip',
'md5':
'e3504aed9c5a290be12d1347836d2742',
'model':
'pwgan_csmsc.pdmodel',
'params':
'pwgan_csmsc.pdiparams',
'sample_rate':
24000,
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_static_0.1.1.zip',
'md5':
'ac6eee94ba483421d750433f4c3b8d36',
'model':
'mb_melgan_csmsc.pdmodel',
'params':
'mb_melgan_csmsc.pdiparams',
'sample_rate':
24000,
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_static_0.1.1.zip',
'md5':
'7edd8c436b3a5546b3a7cb8cff9d5a0c',
'model':
'hifigan_csmsc.pdmodel',
'params':
'hifigan_csmsc.pdiparams',
'sample_rate':
24000,
},
}
class TTSServerExecutor(TTSExecutor):
def __init__(self):
super().__init__()
pass
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format(
tag)
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(pretrained_models[tag],
res_path)
decompressed_path = os.path.abspath(decompressed_path)
logger.info(
'Use pretrained model stored in: {}'.format(decompressed_path))
return decompressed_path
def _init_from_path(
self,
am: str='fastspeech2_csmsc',
am_model: Optional[os.PathLike]=None,
am_params: Optional[os.PathLike]=None,
am_sample_rate: int=24000,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
voc: str='pwgan_csmsc',
voc_model: Optional[os.PathLike]=None,
voc_params: Optional[os.PathLike]=None,
voc_sample_rate: int=24000,
lang: str='zh',
am_predictor_conf: dict=None,
voc_predictor_conf: dict=None, ):
"""
Init model and other resources from a specific path.
"""
if hasattr(self, 'am') and hasattr(self, 'voc'):
logger.info('Models had been initialized.')
return
# am
am_tag = am + '-' + lang
if am_model is None or am_params 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_model = os.path.join(am_res_path,
pretrained_models[am_tag]['model'])
self.am_params = os.path.join(am_res_path,
pretrained_models[am_tag]['params'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['phones_dict'])
self.am_sample_rate = pretrained_models[am_tag]['sample_rate']
logger.info(am_res_path)
logger.info(self.am_model)
logger.info(self.am_params)
else:
self.am_model = os.path.abspath(am_model)
self.am_params = os.path.abspath(am_params)
self.phones_dict = os.path.abspath(phones_dict)
self.am_sample_rate = am_sample_rate
self.am_res_path = os.path.dirname(os.path.abspath(self.am_model))
print("self.phones_dict:", self.phones_dict)
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in pretrained_models[am_tag]:
self.tones_dict = os.path.join(
am_res_path, 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 pretrained_models[am_tag]:
self.speaker_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
# voc
voc_tag = voc + '-' + lang
if voc_model is None or voc_params is None:
voc_res_path = self._get_pretrained_path(voc_tag)
self.voc_res_path = voc_res_path
self.voc_model = os.path.join(voc_res_path,
pretrained_models[voc_tag]['model'])
self.voc_params = os.path.join(voc_res_path,
pretrained_models[voc_tag]['params'])
self.voc_sample_rate = pretrained_models[voc_tag]['sample_rate']
logger.info(voc_res_path)
logger.info(self.voc_model)
logger.info(self.voc_params)
else:
self.voc_model = os.path.abspath(voc_model)
self.voc_params = os.path.abspath(voc_params)
self.voc_sample_rate = voc_sample_rate
self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_model))
assert (
self.voc_sample_rate == self.am_sample_rate
), "The sample rate of AM and Vocoder model are different, please check model."
# Init body.
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!")
# am predictor
self.am_predictor_conf = am_predictor_conf
self.am_predictor = init_predictor(
model_file=self.am_model,
params_file=self.am_params,
predictor_conf=self.am_predictor_conf)
# voc predictor
self.voc_predictor_conf = voc_predictor_conf
self.voc_predictor = init_predictor(
model_file=self.voc_model,
params_file=self.voc_params,
predictor_conf=self.voc_predictor_conf)
@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
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'}!")
flags = 0
for i in range(len(phone_ids)):
part_phone_ids = phone_ids[i]
# am
if am_name == 'speedyspeech':
part_tone_ids = tone_ids[i]
am_result = run_model(
self.am_predictor,
[part_phone_ids.numpy(), part_tone_ids.numpy()])
mel = am_result[0]
# fastspeech2
else:
# multi speaker do not have static model
if am_dataset in {"aishell3", "vctk"}:
pass
else:
am_result = run_model(self.am_predictor,
[part_phone_ids.numpy()])
mel = am_result[0]
# voc
voc_result = run_model(self.voc_predictor, [mel])
wav = voc_result[0]
wav = paddle.to_tensor(wav)
if flags == 0:
wav_all = wav
flags = 1
else:
wav_all = paddle.concat([wav_all, wav])
self._outputs['wav'] = wav_all
class TTSEngine(BaseEngine):
"""TTS server engine
Args:
metaclass: Defaults to Singleton.
"""
def __init__(self):
"""Initialize TTS server engine
"""
super(TTSEngine, self).__init__()
def init(self, config_file: str) -> bool:
self.executor = TTSServerExecutor()
self.config_file = config_file
self.config = get_config(config_file)
self.executor._init_from_path(
am=self.config.am,
am_model=self.config.am_model,
am_params=self.config.am_params,
am_sample_rate=self.config.am_sample_rate,
phones_dict=self.config.phones_dict,
tones_dict=self.config.tones_dict,
speaker_dict=self.config.speaker_dict,
voc=self.config.voc,
voc_model=self.config.voc_model,
voc_params=self.config.voc_params,
voc_sample_rate=self.config.voc_sample_rate,
lang=self.config.lang,
am_predictor_conf=self.config.am_predictor_conf,
voc_predictor_conf=self.config.voc_predictor_conf, )
logger.info("Initialize TTS server engine successfully.")
return True
def postprocess(self,
wav,
original_fs: int,
target_fs: int=16000,
volume: float=1.0,
speed: float=1.0,
audio_path: str=None):
"""Post-processing operations, including speech, volume, sample rate, save audio file
Args:
wav (numpy(float)): Synthesized audio sample points
original_fs (int): original audio sample rate
target_fs (int): target audio sample rate
volume (float): target volume
speed (float): target speed
Raises:
ServerBaseException: Throws an exception if the change speed unsuccessfully.
Returns:
target_fs: target sample rate for synthesized audio.
wav_base64: The base64 format of the synthesized audio.
"""
# transform sample_rate
if target_fs == 0 or target_fs > original_fs:
target_fs = original_fs
wav_tar_fs = wav
else:
wav_tar_fs = librosa.resample(
np.squeeze(wav), original_fs, target_fs)
# transform volume
wav_vol = wav_tar_fs * volume
# transform speed
try: # windows not support soxbindings
wav_speed = change_speed(wav_vol, speed, target_fs)
except:
raise ServerBaseException(
ErrorCode.SERVER_INTERNAL_ERR,
"Can not install soxbindings on your system.")
# wav to base64
buf = io.BytesIO()
wavfile.write(buf, target_fs, wav_speed)
base64_bytes = base64.b64encode(buf.read())
wav_base64 = base64_bytes.decode('utf-8')
# save audio
if audio_path is not None and audio_path.endswith(".wav"):
sf.write(audio_path, wav_speed, target_fs)
elif audio_path is not None and audio_path.endswith(".pcm"):
wav_norm = wav_speed * (32767 / max(0.001,
np.max(np.abs(wav_speed))))
with open(audio_path, "wb") as f:
f.write(wav_norm.astype(np.int16))
return target_fs, wav_base64
def run(self,
sentence: str,
spk_id: int=0,
speed: float=1.0,
volume: float=1.0,
sample_rate: int=0,
save_path: str=None):
"""get the result of the server response
Args:
sentence (str): sentence to be synthesized
spk_id (int, optional): speaker id. Defaults to 0.
speed (float, optional): audio speed, 0 < speed <=3.0. Defaults to 1.0.
volume (float, optional): The volume relative to the audio synthesized by the model,
0 < volume <=3.0. Defaults to 1.0.
sample_rate (int, optional): Set the sample rate of the synthesized audio.
0 represents the sample rate for model synthesis. Defaults to 0.
save_path (str, optional): The save path of the synthesized audio. Defaults to None.
Raises:
ServerBaseException: Throws an exception if tts inference unsuccessfully.
ServerBaseException: Throws an exception if postprocess unsuccessfully.
Returns:
lang: model language
target_sample_rate: target sample rate for synthesized audio.
wav_base64: The base64 format of the synthesized audio.
"""
lang = self.config.lang
try:
self.executor.infer(
text=sentence, lang=lang, am=self.config.am, spk_id=spk_id)
except:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts infer failed.")
try:
target_sample_rate, wav_base64 = self.postprocess(
wav=self.executor._outputs['wav'].numpy(),
original_fs=self.executor.am_sample_rate,
target_fs=sample_rate,
volume=volume,
speed=speed,
audio_path=save_path)
except:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts postprocess failed.")
return lang, target_sample_rate, wav_base64
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