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add paddle inference code, test=doc

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lym0302 3 years ago
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commit 8ee7fd4f31
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speechserving/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 argparse
import base64
import io
import os
from typing import Optional
import librosa
import numpy as np
import paddle
import soundfile as sf
import yaml
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.errors import ErrorCode
from utils.exception import ServerBaseException
from utils.paddle_predictor import init_predictor
from utils.paddle_predictor import run_model
#from paddle.inference import Config
#from paddle.inference import create_predictor
__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':
'9a849a74d1be0c758dd5a1b9c8f77f3d',
'model':
'speedyspeech_csmsc.pdmodel',
'params':
'speedyspeech_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip',
'md5':
'8eb01c2e4bc7e8b59beaa9fa046069cf',
'model':
'fastspeech2_csmsc.pdmodel',
'params':
'fastspeech2_csmsc.pdiparams',
'phones_dict':
'phone_id_map.txt',
},
# 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',
},
# 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',
},
# 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',
},
}
class TTSServerExecutor(TTSExecutor):
def __init__(self):
super().__init__()
self.parser = argparse.ArgumentParser(
prog='paddlespeech.tts', add_help=True)
self.parser.add_argument(
'--conf',
type=str,
default='./conf/tts/tts_pd.yaml',
help='Configuration parameters.')
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,
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,
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 phones_dict is None:
print("please input phones_dict!")
### 后续下载的模型里加上 phone 和 tone的 dict 就不用这个了
#if am_model is None or am_params is None or phones_dict is None:
if am_model is None or am_params 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.phones_dict = os.path.abspath(phones_dict)
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_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'])
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_res_path = os.path.dirname(os.path.abspath(self.voc_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, name=None):
"""Initialize TTS server engine
"""
super(TTSEngine, self).__init__()
self.executor = TTSServerExecutor()
config_path = self.executor.parser.parse_args().conf
with open(config_path, 'rt') as f:
self.conf_dict = yaml.safe_load(f)
self.executor._init_from_path(
am=self.conf_dict["am"],
am_model=self.conf_dict["am_model"],
am_params=self.conf_dict["am_params"],
phones_dict=self.conf_dict["phones_dict"],
tones_dict=self.conf_dict["tones_dict"],
speaker_dict=self.conf_dict["speaker_dict"],
voc=self.conf_dict["voc"],
voc_model=self.conf_dict["voc_model"],
voc_params=self.conf_dict["voc_params"],
lang=self.conf_dict["lang"],
am_predictor_conf=self.conf_dict["am_predictor_conf"],
voc_predictor_conf=self.conf_dict["voc_predictor_conf"], )
logger.info("Initialize TTS server engine successfully.")
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
"""
# 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: Exception
ServerBaseException: Exception
Returns:
lang, target_sample_rate, wav_base64
"""
lang = self.conf_dict["lang"]
try:
self.executor.infer(
text=sentence,
lang=lang,
am=self.conf_dict["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_config.fs,
original_fs=24000, # TODO get sample rate from model
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

12
speechserving/speechserving/utils/audio_process.py
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@ -17,11 +17,11 @@ import numpy as np
def wav2pcm(wavfile, pcmfile, data_type=np.int16):
f = open(wavfile, "rb")
f.seek(0)
f.read(44)
data = np.fromfile(f, dtype=data_type)
data.tofile(pcmfile)
with open(wavfile, "rb") as f:
f.seek(0)
f.read(44)
data = np.fromfile(f, dtype=data_type)
data.tofile(pcmfile)
def pcm2wav(pcm_file, wav_file, channels=1, bits=16, sample_rate=16000):
@ -52,7 +52,7 @@ def change_speed(sample_raw, speed_rate, sample_rate):
:raises ValueError: If speed_rate <= 0.0.
"""
if speed_rate == 1.0:
return
return sample_raw
if speed_rate <= 0:
raise ValueError("speed_rate should be greater than zero.")

82
speechserving/speechserving/utils/paddle_predictor.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 os
from typing import Optional
from paddle.inference import Config
from paddle.inference import create_predictor
def init_predictor(model_dir: Optional[os.PathLike]=None,
model_file: Optional[os.PathLike]=None,
params_file: Optional[os.PathLike]=None,
predictor_conf: dict=None):
"""Create predictor with Paddle inference
Args:
model_dir (Optional[os.PathLike], optional): The path of the static model saved in the model layer. Defaults to None.
model_file (Optional[os.PathLike], optional): *.pdmodel file path. Defaults to None.
params_file (Optional[os.PathLike], optional): *.pdiparams file path.. Defaults to None.
predictor_conf (dict, optional): The configuration parameters of predictor. Defaults to None.
Returns:
[type]: [description]
"""
if model_dir is not None:
config = Config(args.model_dir)
else:
config = Config(model_file, params_file)
config.enable_memory_optim()
if "use_gpu" in predictor_conf and predictor_conf["use_gpu"] == "true":
config.enable_use_gpu(1000, 0)
if "enable_mkldnn" in predictor_conf and predictor_conf[
"enable_mkldnn"] == "true":
config.enable_mkldnn()
if "switch_ir_optim" in predictor_conf and predictor_conf[
"switch_ir_optim"] == "true":
config.switch_ir_optim()
predictor = create_predictor(config)
return predictor
def run_model(predictor, input: list):
""" run predictor
Args:
predictor: paddle inference predictor
input (list): The input of predictor
Returns:
list: result list
"""
input_names = predictor.get_input_names()
for i, name in enumerate(input_names):
input_handle = predictor.get_input_handle(name)
input_handle.copy_from_cpu(input[i])
# do the inference
predictor.run()
results = []
# get out data from output tensor
output_names = predictor.get_output_names()
for i, name in enumerate(output_names):
output_handle = predictor.get_output_handle(name)
output_data = output_handle.copy_to_cpu()
results.append(output_data)
return results
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