Merge branch 'develop' of https://github.com/PaddlePaddle/DeepSpeech into unit_test

pull/1488/head
huangyuxin 3 years ago
commit 48e610755d

@ -9,9 +9,17 @@ port: 8090
##################################################################
# CONFIG FILE #
##################################################################
# add engine type (Options: asr, tts) and config file here.
# The engine_type of speech task needs to keep the same type as the config file of speech task.
# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
#
# add engine type (Options: python, inference)
engine_type:
asr: 'inference'
tts: 'inference'
# add engine backend type (Options: asr, tts) and config file here.
# Adding a speech task to engine_backend means starting the service.
engine_backend:
asr: 'conf/asr/asr.yaml'
tts: 'conf/tts/tts.yaml'
asr: 'conf/asr/asr_pd.yaml'
tts: 'conf/tts/tts_pd.yaml'

@ -1,7 +1,8 @@
model: 'conformer_wenetspeech'
lang: 'zh'
sample_rate: 16000
cfg_path:
ckpt_path:
cfg_path: # [optional]
ckpt_path: # [optional]
decode_method: 'attention_rescoring'
force_yes: False
force_yes: True
device: 'cpu' # set 'gpu:id' or 'cpu'

@ -0,0 +1,25 @@
# This is the parameter configuration file for ASR server.
# These are the static models that support paddle inference.
##################################################################
# ACOUSTIC MODEL SETTING #
# am choices=['deepspeech2offline_aishell'] TODO
##################################################################
model_type: 'deepspeech2offline_aishell'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method:
force_yes: True
am_predictor_conf:
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: True
switch_ir_optim: True
##################################################################
# OTHERS #
##################################################################

@ -29,4 +29,4 @@ voc_stat:
# OTHERS #
##################################################################
lang: 'zh'
device: 'gpu:2'
device: 'cpu' # set 'gpu:id' or 'cpu'

@ -6,8 +6,8 @@
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
##################################################################
am: 'fastspeech2_csmsc'
am_model: # the pdmodel file of am static model
am_params: # the pdiparams file of am static model
am_model: # the pdmodel file of your am static model (XX.pdmodel)
am_params: # the pdiparams file of your am static model (XX.pdipparams)
am_sample_rate: 24000
phones_dict:
tones_dict:
@ -15,9 +15,9 @@ speaker_dict:
spk_id: 0
am_predictor_conf:
use_gpu: True
enable_mkldnn: True
switch_ir_optim: True
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: False
switch_ir_optim: False
##################################################################
@ -25,17 +25,16 @@ am_predictor_conf:
# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
##################################################################
voc: 'pwgan_csmsc'
voc_model: # the pdmodel file of vocoder static model
voc_params: # the pdiparams file of vocoder static model
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
voc_sample_rate: 24000
voc_predictor_conf:
use_gpu: True
enable_mkldnn: True
switch_ir_optim: True
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: False
switch_ir_optim: False
##################################################################
# OTHERS #
##################################################################
lang: 'zh'
device: paddle.get_device()

@ -12,8 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import uvicorn
import yaml
from fastapi import FastAPI
from paddlespeech.server.engine.engine_pool import init_engine_pool

@ -48,8 +48,9 @@ class TTSClientExecutor(BaseExecutor):
self.parser.add_argument(
'--input',
type=str,
default="你好,欢迎使用语音合成服务",
help='A sentence to be synthesized.')
default=None,
help='Text to be synthesized.',
required=True)
self.parser.add_argument(
'--spk_id', type=int, default=0, help='Speaker id')
self.parser.add_argument(
@ -123,7 +124,7 @@ class TTSClientExecutor(BaseExecutor):
logger.info("RTF: %f " % (time_consume / duration))
return True
except:
except BaseException:
logger.error("Failed to synthesized audio.")
return False
@ -163,7 +164,7 @@ class TTSClientExecutor(BaseExecutor):
print("Audio duration: %f s." % (duration))
print("Response time: %f s." % (time_consume))
print("RTF: %f " % (time_consume / duration))
except:
except BaseException:
print("Failed to synthesized audio.")
@ -181,8 +182,9 @@ class ASRClientExecutor(BaseExecutor):
self.parser.add_argument(
'--input',
type=str,
default="./paddlespeech/server/tests/16_audio.wav",
help='Audio file to be recognized')
default=None,
help='Audio file to be recognized',
required=True)
self.parser.add_argument(
'--sample_rate', type=int, default=16000, help='audio sample rate')
self.parser.add_argument(
@ -209,7 +211,7 @@ class ASRClientExecutor(BaseExecutor):
logger.info(r.json())
logger.info("time cost %f s." % (time_end - time_start))
return True
except:
except BaseException:
logger.error("Failed to speech recognition.")
return False
@ -240,5 +242,5 @@ class ASRClientExecutor(BaseExecutor):
time_end = time.time()
print(r.json())
print("time cost %f s." % (time_end - time_start))
except:
print("Failed to speech recognition.")
except BaseException:
print("Failed to speech recognition.")

@ -20,7 +20,7 @@ from fastapi import FastAPI
from ..executor import BaseExecutor
from ..util import cli_server_register
from ..util import stats_wrapper
from paddlespeech.server.engine.engine_factory import EngineFactory
from paddlespeech.server.engine.engine_pool import init_engine_pool
from paddlespeech.server.restful.api import setup_router
from paddlespeech.server.utils.config import get_config
@ -41,7 +41,8 @@ class ServerExecutor(BaseExecutor):
"--config_file",
action="store",
help="yaml file of the app",
default="./conf/application.yaml")
default=None,
required=True)
self.parser.add_argument(
"--log_file",
@ -51,8 +52,10 @@ class ServerExecutor(BaseExecutor):
def init(self, config) -> bool:
"""system initialization
Args:
config (CfgNode): config object
Returns:
bool:
"""
@ -61,13 +64,8 @@ class ServerExecutor(BaseExecutor):
api_router = setup_router(api_list)
app.include_router(api_router)
# init engine
engine_pool = []
for engine in config.engine_backend:
engine_pool.append(EngineFactory.get_engine(engine_name=engine))
if not engine_pool[-1].init(
config_file=config.engine_backend[engine]):
return False
if not init_engine_pool(config):
return False
return True

@ -9,12 +9,17 @@ port: 8090
##################################################################
# CONFIG FILE #
##################################################################
# The engine_type of speech task needs to keep the same type as the config file of speech task.
# E.g: The engine_type of asr is 'python', the engine_backend of asr is 'XX/asr.yaml'
# E.g: The engine_type of asr is 'inference', the engine_backend of asr is 'XX/asr_pd.yaml'
#
# add engine type (Options: python, inference)
engine_type:
asr: 'inference'
# tts: 'inference'
asr: 'python'
tts: 'python'
# add engine backend type (Options: asr, tts) and config file here.
# Adding a speech task to engine_backend means starting the service.
engine_backend:
asr: 'conf/asr/asr_pd.yaml'
#tts: 'conf/tts/tts_pd.yaml'
asr: 'conf/asr/asr.yaml'
tts: 'conf/tts/tts.yaml'

@ -5,3 +5,4 @@ cfg_path: # [optional]
ckpt_path: # [optional]
decode_method: 'attention_rescoring'
force_yes: True
device: 'cpu' # set 'gpu:id' or 'cpu'

@ -15,7 +15,7 @@ decode_method:
force_yes: True
am_predictor_conf:
use_gpu: True
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: True
switch_ir_optim: True

@ -29,4 +29,4 @@ voc_stat:
# OTHERS #
##################################################################
lang: 'zh'
device: paddle.get_device()
device: 'cpu' # set 'gpu:id' or 'cpu'

@ -6,18 +6,18 @@
# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
##################################################################
am: 'fastspeech2_csmsc'
am_model: # the pdmodel file of am static model
am_params: # the pdiparams file of am static model
am_sample_rate: 24000
am_model: # the pdmodel file of your am static model (XX.pdmodel)
am_params: # the pdiparams file of your am static model (XX.pdipparams)
am_sample_rate: 24000 # must match the model
phones_dict:
tones_dict:
speaker_dict:
spk_id: 0
am_predictor_conf:
use_gpu: True
enable_mkldnn: True
switch_ir_optim: True
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: False
switch_ir_optim: False
##################################################################
@ -25,17 +25,16 @@ am_predictor_conf:
# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
##################################################################
voc: 'pwgan_csmsc'
voc_model: # the pdmodel file of vocoder static model
voc_params: # the pdiparams file of vocoder static model
voc_sample_rate: 24000
voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel)
voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams)
voc_sample_rate: 24000 #must match the model
voc_predictor_conf:
use_gpu: True
enable_mkldnn: True
switch_ir_optim: True
device: 'cpu' # set 'gpu:id' or 'cpu'
enable_mkldnn: False
switch_ir_optim: False
##################################################################
# OTHERS #
##################################################################
lang: 'zh'
device: paddle.get_device()

@ -13,31 +13,24 @@
# limitations under the License.
import io
import os
from typing import List
from typing import Optional
from typing import Union
import librosa
import paddle
import soundfile
from yacs.config import CfgNode
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.modules.ctc import CTCDecoder
from paddlespeech.s2t.utils.utility import UpdateConfig
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.config import get_config
from paddlespeech.server.utils.paddle_predictor import init_predictor
from paddlespeech.server.utils.paddle_predictor import run_model
from paddlespeech.server.engine.base_engine import BaseEngine
__all__ = ['ASREngine']
pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'url':
@ -143,7 +136,6 @@ class ASRServerExecutor(ASRExecutor):
batch_average=True, # sum / batch_size
grad_norm_type=self.config.get('ctc_grad_norm_type', None))
@paddle.no_grad()
def infer(self, model_type: str):
"""
@ -161,9 +153,8 @@ class ASRServerExecutor(ASRExecutor):
cfg.beam_size, cfg.cutoff_prob, cfg.cutoff_top_n,
cfg.num_proc_bsearch)
output_data = run_model(
self.am_predictor,
[audio.numpy(), audio_len.numpy()])
output_data = run_model(self.am_predictor,
[audio.numpy(), audio_len.numpy()])
probs = output_data[0]
eouts_len = output_data[1]
@ -208,14 +199,14 @@ class ASREngine(BaseEngine):
paddle.set_device(paddle.get_device())
self.executor._init_from_path(
model_type=self.config.model_type,
am_model=self.config.am_model,
am_params=self.config.am_params,
lang=self.config.lang,
sample_rate=self.config.sample_rate,
cfg_path=self.config.cfg_path,
decode_method=self.config.decode_method,
am_predictor_conf=self.config.am_predictor_conf)
model_type=self.config.model_type,
am_model=self.config.am_model,
am_params=self.config.am_params,
lang=self.config.lang,
sample_rate=self.config.sample_rate,
cfg_path=self.config.cfg_path,
decode_method=self.config.decode_method,
am_predictor_conf=self.config.am_predictor_conf)
logger.info("Initialize ASR server engine successfully.")
return True
@ -230,7 +221,8 @@ class ASREngine(BaseEngine):
io.BytesIO(audio_data), self.config.sample_rate,
self.config.force_yes):
logger.info("start running asr engine")
self.executor.preprocess(self.config.model_type, io.BytesIO(audio_data))
self.executor.preprocess(self.config.model_type,
io.BytesIO(audio_data))
self.executor.infer(self.config.model_type)
self.output = self.executor.postprocess() # Retrieve result of asr.
logger.info("end inferring asr engine")

@ -12,21 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import os
from typing import List
from typing import Optional
from typing import Union
import librosa
import paddle
import soundfile
from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.log import logger
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.s2t.utils.utility import UpdateConfig
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.config import get_config
@ -63,7 +53,10 @@ class ASREngine(BaseEngine):
self.executor = ASRServerExecutor()
self.config = get_config(config_file)
paddle.set_device(paddle.get_device())
if self.config.device is None:
paddle.set_device(paddle.get_device())
else:
paddle.set_device(self.config.device)
self.executor._init_from_path(
self.config.model, self.config.lang, self.config.sample_rate,
self.config.cfg_path, self.config.decode_method,

@ -12,8 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Any
from typing import List
from typing import Union
from pattern_singleton import Singleton

@ -13,7 +13,6 @@
# limitations under the License.
from typing import Text
__all__ = ['EngineFactory']

@ -29,8 +29,10 @@ def init_engine_pool(config) -> bool:
"""
global ENGINE_POOL
for engine in config.engine_backend:
ENGINE_POOL[engine] = EngineFactory.get_engine(engine_name=engine, engine_type=config.engine_type[engine])
if not ENGINE_POOL[engine].init(config_file=config.engine_backend[engine]):
ENGINE_POOL[engine] = EngineFactory.get_engine(
engine_name=engine, engine_type=config.engine_type[engine])
if not ENGINE_POOL[engine].init(
config_file=config.engine_backend[engine]):
return False
return True

@ -344,7 +344,6 @@ class TTSEngine(BaseEngine):
try:
self.config = get_config(config_file)
self.executor._init_from_path(
am=self.config.am,
am_model=self.config.am_model,
@ -361,8 +360,8 @@ class TTSEngine(BaseEngine):
am_predictor_conf=self.config.am_predictor_conf,
voc_predictor_conf=self.config.voc_predictor_conf, )
except:
logger.info("Initialize TTS server engine Failed.")
except BaseException:
logger.error("Initialize TTS server engine Failed.")
return False
logger.info("Initialize TTS server engine successfully.")
@ -406,11 +405,13 @@ class TTSEngine(BaseEngine):
# transform speed
try: # windows not support soxbindings
wav_speed = change_speed(wav_vol, speed, target_fs)
except:
except ServerBaseException:
raise ServerBaseException(
ErrorCode.SERVER_INTERNAL_ERR,
"Transform speed failed. Can not install soxbindings on your system. \
You need to set speed value 1.0.")
except BaseException:
logger.error("Transform speed failed.")
# wav to base64
buf = io.BytesIO()
@ -463,9 +464,11 @@ class TTSEngine(BaseEngine):
try:
self.executor.infer(
text=sentence, lang=lang, am=self.config.am, spk_id=spk_id)
except:
except ServerBaseException:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts infer failed.")
except BaseException:
logger.error("tts infer failed.")
try:
target_sample_rate, wav_base64 = self.postprocess(
@ -475,8 +478,10 @@ class TTSEngine(BaseEngine):
volume=volume,
speed=speed,
audio_path=save_path)
except:
except ServerBaseException:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts postprocess failed.")
except BaseException:
logger.error("tts postprocess failed.")
return lang, target_sample_rate, wav_base64

@ -54,7 +54,10 @@ class TTSEngine(BaseEngine):
try:
self.config = get_config(config_file)
paddle.set_device(self.config.device)
if self.config.device is None:
paddle.set_device(paddle.get_device())
else:
paddle.set_device(self.config.device)
self.executor._init_from_path(
am=self.config.am,
@ -69,8 +72,8 @@ class TTSEngine(BaseEngine):
voc_ckpt=self.config.voc_ckpt,
voc_stat=self.config.voc_stat,
lang=self.config.lang)
except:
logger.info("Initialize TTS server engine Failed.")
except BaseException:
logger.error("Initialize TTS server engine Failed.")
return False
logger.info("Initialize TTS server engine successfully.")
@ -114,10 +117,13 @@ class TTSEngine(BaseEngine):
# transform speed
try: # windows not support soxbindings
wav_speed = change_speed(wav_vol, speed, target_fs)
except:
except ServerBaseException:
raise ServerBaseException(
ErrorCode.SERVER_INTERNAL_ERR,
"Can not install soxbindings on your system.")
"Transform speed failed. Can not install soxbindings on your system. \
You need to set speed value 1.0.")
except BaseException:
logger.error("Transform speed failed.")
# wav to base64
buf = io.BytesIO()
@ -170,9 +176,11 @@ class TTSEngine(BaseEngine):
try:
self.executor.infer(
text=sentence, lang=lang, am=self.config.am, spk_id=spk_id)
except:
except ServerBaseException:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts infer failed.")
except BaseException:
logger.error("tts infer failed.")
try:
target_sample_rate, wav_base64 = self.postprocess(
@ -182,8 +190,10 @@ class TTSEngine(BaseEngine):
volume=volume,
speed=speed,
audio_path=save_path)
except:
except ServerBaseException:
raise ServerBaseException(ErrorCode.SERVER_INTERNAL_ERR,
"tts postprocess failed.")
except BaseException:
logger.error("tts postprocess failed.")
return lang, target_sample_rate, wav_base64

@ -14,6 +14,7 @@
import base64
import traceback
from typing import Union
from fastapi import APIRouter
from paddlespeech.server.engine.engine_pool import get_engine_pool
@ -83,7 +84,7 @@ def asr(request_body: ASRRequest):
except ServerBaseException as e:
response = failed_response(e.error_code, e.msg)
except:
except BaseException:
response = failed_response(ErrorCode.SERVER_UNKOWN_ERR)
traceback.print_exc()

@ -11,7 +11,6 @@
# 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.
from typing import List
from typing import Optional
from pydantic import BaseModel

@ -11,9 +11,6 @@
# 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.
from typing import List
from typing import Optional
from pydantic import BaseModel
__all__ = ['ASRResponse', 'TTSResponse']

@ -16,7 +16,7 @@ from typing import Union
from fastapi import APIRouter
from paddlespeech.server.engine.tts.paddleinference.tts_engine import TTSEngine
from paddlespeech.server.engine.engine_pool import get_engine_pool
from paddlespeech.server.restful.request import TTSRequest
from paddlespeech.server.restful.response import ErrorResponse
from paddlespeech.server.restful.response import TTSResponse
@ -60,28 +60,41 @@ def tts(request_body: TTSRequest):
Returns:
json: [description]
"""
# json to dict
item_dict = request_body.dict()
sentence = item_dict['text']
spk_id = item_dict['spk_id']
speed = item_dict['speed']
volume = item_dict['volume']
sample_rate = item_dict['sample_rate']
save_path = item_dict['save_path']
# get params
text = request_body.text
spk_id = request_body.spk_id
speed = request_body.speed
volume = request_body.volume
sample_rate = request_body.sample_rate
save_path = request_body.save_path
# Check parameters
if speed <=0 or speed > 3 or volume <=0 or volume > 3 or \
sample_rate not in [0, 16000, 8000] or \
(save_path is not None and not save_path.endswith("pcm") and not save_path.endswith("wav")):
return failed_response(ErrorCode.SERVER_PARAM_ERR)
# single
tts_engine = TTSEngine()
if speed <= 0 or speed > 3:
return failed_response(
ErrorCode.SERVER_PARAM_ERR,
"invalid speed value, the value should be between 0 and 3.")
if volume <= 0 or volume > 3:
return failed_response(
ErrorCode.SERVER_PARAM_ERR,
"invalid volume value, the value should be between 0 and 3.")
if sample_rate not in [0, 16000, 8000]:
return failed_response(
ErrorCode.SERVER_PARAM_ERR,
"invalid sample_rate value, the choice of value is 0, 8000, 16000.")
if save_path is not None and not save_path.endswith(
"pcm") and not save_path.endswith("wav"):
return failed_response(
ErrorCode.SERVER_PARAM_ERR,
"invalid save_path, saved audio formats support pcm and wav")
# run
try:
# get single engine from engine pool
engine_pool = get_engine_pool()
tts_engine = engine_pool['tts']
lang, target_sample_rate, wav_base64 = tts_engine.run(
sentence, spk_id, speed, volume, sample_rate, save_path)
text, spk_id, speed, volume, sample_rate, save_path)
response = {
"success": True,
@ -101,7 +114,7 @@ def tts(request_body: TTSRequest):
}
except ServerBaseException as e:
response = failed_response(e.error_code, e.msg)
except:
except BaseException:
response = failed_response(ErrorCode.SERVER_UNKOWN_ERR)
traceback.print_exc()

@ -10,11 +10,11 @@
# 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
import requests
import base64
import json
import time
import base64
import io
import requests
def readwav2base64(wav_file):
@ -34,23 +34,23 @@ def main():
url = "http://127.0.0.1:8090/paddlespeech/asr"
# start Timestamp
time_start=time.time()
time_start = time.time()
test_audio_dir = "./16_audio.wav"
audio = readwav2base64(test_audio_dir)
data = {
"audio": audio,
"audio_format": "wav",
"sample_rate": 16000,
"lang": "zh_cn",
}
"audio": audio,
"audio_format": "wav",
"sample_rate": 16000,
"lang": "zh_cn",
}
r = requests.post(url=url, data=json.dumps(data))
# ending Timestamp
time_end=time.time()
print('time cost',time_end - time_start, 's')
time_end = time.time()
print('time cost', time_end - time_start, 's')
print(r.json())

@ -25,6 +25,7 @@ import soundfile
from paddlespeech.server.utils.audio_process import wav2pcm
# Request and response
def tts_client(args):
""" Request and response
@ -99,5 +100,5 @@ if __name__ == "__main__":
print("Inference time: %f" % (time_consume))
print("The duration of synthesized audio: %f" % (duration))
print("The RTF is: %f" % (rtf))
except:
except BaseException:
print("Failed to synthesized audio.")

@ -219,7 +219,7 @@ class ConfigCache:
try:
cfg = yaml.load(file, Loader=yaml.FullLoader)
self._data.update(cfg)
except:
except BaseException:
self.flush()
@property

@ -41,8 +41,9 @@ def init_predictor(model_dir: Optional[os.PathLike]=None,
config = Config(model_file, params_file)
config.enable_memory_optim()
if predictor_conf["use_gpu"]:
config.enable_use_gpu(1000, 0)
if "gpu" in predictor_conf["device"]:
gpu_id = predictor_conf["device"].split(":")[-1]
config.enable_use_gpu(1000, int(gpu_id))
if predictor_conf["enable_mkldnn"]:
config.enable_mkldnn()
if predictor_conf["switch_ir_optim"]:

@ -1,48 +0,0 @@
ConfigArgParse
coverage
editdistance
g2p_en
g2pM
gpustat
h5py
inflect
jieba
jsonlines
kaldiio
librosa
loguru
matplotlib
nara_wpe
nltk
paddleaudio
paddlenlp
paddlespeech_ctcdecoders
paddlespeech_feat
pandas
phkit
Pillow
praatio==5.0.0
pre-commit
pybind11
pypi-kenlm
pypinyin
python-dateutil
pyworld
resampy==0.2.2
sacrebleu
scipy
sentencepiece~=0.1.96
snakeviz
soundfile~=0.10
sox
soxbindings
textgrid
timer
tqdm
typeguard
unidecode
visualdl
webrtcvad
yacs~=0.1.8
yq
zhon

@ -27,46 +27,52 @@ from setuptools.command.install import install
HERE = Path(os.path.abspath(os.path.dirname(__file__)))
VERSION = '0.1.1'
VERSION = '0.1.2'
base = [
"editdistance",
"g2p_en",
"g2pM",
"h5py",
"inflect",
"jieba",
"jsonlines",
"kaldiio",
"librosa==0.8.1",
"loguru",
"matplotlib",
"nara_wpe",
"pandas",
"paddleaudio",
"paddlenlp",
"paddlespeech_feat",
"praatio==5.0.0",
"pypinyin",
"python-dateutil",
"pyworld",
"resampy==0.2.2",
"sacrebleu",
"scipy",
"sentencepiece~=0.1.96",
"soundfile~=0.10",
"textgrid",
"timer",
"tqdm",
"typeguard",
"visualdl",
"webrtcvad",
"yacs~=0.1.8",
]
server = [
"fastapi",
"uvicorn",
"pattern_singleton",
]
requirements = {
"install": [
"editdistance",
"g2p_en",
"g2pM",
"h5py",
"inflect",
"jieba",
"jsonlines",
"kaldiio",
"librosa",
"loguru",
"matplotlib",
"nara_wpe",
"pandas",
"paddleaudio",
"paddlenlp",
"paddlespeech_feat",
"praatio==5.0.0",
"pypinyin",
"python-dateutil",
"pyworld",
"resampy==0.2.2",
"sacrebleu",
"scipy",
"sentencepiece~=0.1.96",
"soundfile~=0.10",
"textgrid",
"timer",
"tqdm",
"typeguard",
"visualdl",
"webrtcvad",
"yacs~=0.1.8",
# fastapi server
"fastapi",
"uvicorn",
],
"install":
base + server,
"develop": [
"ConfigArgParse",
"coverage",

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