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DeepSpeech

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提交 b361a738 编写于 作者: L lym0302
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improve server code, test=doc

上级 2c2e561b
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Showing with 812 addition and 706 deletion
demos/streaming_tts_server/conf/tts_online_application.yaml
浏览文件 @ b361a738
......@@ -43,12 +43,12 @@ tts_online:
device: 'cpu' # set 'gpu:id' or 'cpu'
# am_block and am_pad only for fastspeech2_cnndecoder_onnx model to streaming am infer,
# when am_pad set 12, streaming synthetic audio is the same as non-streaming synthetic audio
am_block: 42
am_block: 72
am_pad: 12
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block: 14
voc_block: 36
voc_pad: 14
......@@ -91,12 +91,12 @@ tts_online-onnx:
lang: 'zh'
# am_block and am_pad only for fastspeech2_cnndecoder_onnx model to streaming am infer,
# when am_pad set 12, streaming synthetic audio is the same as non-streaming synthetic audio
am_block: 42
am_block: 72
am_pad: 12
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc_onnx, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc_onnx, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block: 14
voc_block: 36
voc_pad: 14
# voc_upsample should be same as n_shift on voc config.
voc_upsample: 300
......
paddlespeech/server/bin/paddlespeech_client.py
浏览文件 @ b361a738
......@@ -31,6 +31,7 @@ from ..util import stats_wrapper
from paddlespeech.cli.log import logger
from paddlespeech.server.utils.audio_handler import ASRWsAudioHandler
from paddlespeech.server.utils.audio_process import wav2pcm
from paddlespeech.server.utils.util import compute_delay
from paddlespeech.server.utils.util import wav2base64
__all__ = [
......@@ -221,7 +222,7 @@ class TTSOnlineClientExecutor(BaseExecutor):
play = args.play
try:
res = self(
self(
input=input_,
server_ip=server_ip,
port=port,
......@@ -257,17 +258,42 @@ class TTSOnlineClientExecutor(BaseExecutor):
logger.info("tts http client start")
from paddlespeech.server.utils.audio_handler import TTSHttpHandler
handler = TTSHttpHandler(server_ip, port, play)
handler.run(input, spk_id, speed, volume, sample_rate, output)
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = handler.run(
input, spk_id, speed, volume, sample_rate, output)
delay_time_list = compute_delay(receive_time_list,
chunk_duration_list)
elif protocol == "websocket":
from paddlespeech.server.utils.audio_handler import TTSWsHandler
logger.info("tts websocket client start")
handler = TTSWsHandler(server_ip, port, play)
loop = asyncio.get_event_loop()
loop.run_until_complete(handler.run(input, output))
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = loop.run_until_complete(
handler.run(input, output))
delay_time_list = compute_delay(receive_time_list,
chunk_duration_list)
else:
logger.error("Please set correct protocol, http or websocket")
return False
logger.info(f"sentence: {input}")
logger.info(f"duration: {duration} s")
logger.info(f"first response: {first_response} s")
logger.info(f"final response: {final_response} s")
logger.info(f"RTF: {final_response/duration}")
if output is not None:
if save_audio_success:
logger.info(f"Audio successfully saved in {output}")
else:
logger.error("Audio save failed.")
if delay_time_list != []:
logger.info(
f"Delay situation: total number of packages: {len(receive_time_list)}, the number of delayed packets: {len(delay_time_list)}, minimum delay time: {min(delay_time_list)} s, maximum delay time: {max(delay_time_list)} s, average delay time: {sum(delay_time_list)/len(delay_time_list)} s, delay rate:{len(delay_time_list)/len(receive_time_list)}"
)
else:
logger.info("The sentence has no delay in streaming synthesis.")
@cli_client_register(
......
paddlespeech/server/conf/application.yaml
浏览文件 @ b361a738
# This is the parameter configuration file for PaddleSpeech Serving.
# This is the parameter configuration file for PaddleSpeech Offline Serving..
#################################################################################
# SERVER SETTING #
......@@ -7,9 +7,7 @@ host: 127.0.0.1
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
# protocol = ['websocket', 'http'] (only one can be selected).
# http only support offline engine type.
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference', 'cls_python', 'cls_inference']
protocol: 'http'
engine_list: ['asr_python', 'tts_python', 'cls_python', 'text_python', 'vector_python']
......@@ -50,24 +48,6 @@ asr_inference:
summary: True # False -> do not show predictor config
################### speech task: asr; engine_type: online #######################
asr_online:
model_type: 'deepspeech2online_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: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
################################### TTS #########################################
################### speech task: tts; engine_type: python #######################
tts_python:
......
paddlespeech/server/conf/tts_online_application.yaml
浏览文件 @ b361a738
......@@ -43,12 +43,12 @@ tts_online:
device: 'cpu' # set 'gpu:id' or 'cpu'
# am_block and am_pad only for fastspeech2_cnndecoder_onnx model to streaming am infer,
# when am_pad set 12, streaming synthetic audio is the same as non-streaming synthetic audio
am_block: 42
am_block: 72
am_pad: 12
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block: 14
voc_block: 36
voc_pad: 14
......@@ -91,12 +91,12 @@ tts_online-onnx:
lang: 'zh'
# am_block and am_pad only for fastspeech2_cnndecoder_onnx model to streaming am infer,
# when am_pad set 12, streaming synthetic audio is the same as non-streaming synthetic audio
am_block: 42
am_block: 72
am_pad: 12
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc_onnx, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc_onnx, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block: 14
voc_block: 36
voc_pad: 14
# voc_upsample should be same as n_shift on voc config.
voc_upsample: 300
......
paddlespeech/server/engine/asr/online/asr_engine.py
浏览文件 @ b361a738
......@@ -20,10 +20,9 @@ import paddle
from numpy import float32
from yacs.config import CfgNode
from .pretrained_models import pretrained_models
from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.asr.infer import model_alias
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import download_and_decompress
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.frontend.speech import SpeechSegment
......@@ -40,45 +39,6 @@ from paddlespeech.server.utils.paddle_predictor import init_predictor
__all__ = ['ASREngine']
pretrained_models = {
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'model':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"conformer_online_multicn-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
'md5':
'0ac93d390552336f2a906aec9e33c5fa',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
'model':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'params':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}
# ASR server connection process class
class PaddleASRConnectionHanddler:
......@@ -625,24 +585,7 @@ class PaddleASRConnectionHanddler:
class ASRServerExecutor(ASRExecutor):
def __init__(self):
super().__init__()
pass
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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
self.pretrained_models = pretrained_models
def _init_from_path(self,
model_type: str='deepspeech2online_aishell',
......@@ -658,20 +601,20 @@ class ASRServerExecutor(ASRExecutor):
"""
self.model_type = model_type
self.sample_rate = sample_rate
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
if cfg_path is None or am_model is None or am_params is None:
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
logger.info(f"Load the pretrained model, tag = {tag}")
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.am_model = os.path.join(res_path,
pretrained_models[tag]['model'])
self.pretrained_models[tag]['model'])
self.am_params = os.path.join(res_path,
pretrained_models[tag]['params'])
self.pretrained_models[tag]['params'])
logger.info(res_path)
else:
self.cfg_path = os.path.abspath(cfg_path)
......@@ -699,8 +642,8 @@ class ASRServerExecutor(ASRExecutor):
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = pretrained_models[tag]['lm_url']
lm_md5 = pretrained_models[tag]['lm_md5']
lm_url = self.pretrained_models[tag]['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5']
logger.info(f"Start to load language model {lm_url}")
self.download_lm(
lm_url,
......@@ -773,7 +716,7 @@ class ASRServerExecutor(ASRExecutor):
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
logger.info(f"model name: {model_name}")
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config
model = model_class.from_config(model_conf)
self.model = model
......
paddlespeech/server/engine/asr/online/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
pretrained_models = {
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_fbank161_ckpt_0.2.1.model.tar.gz',
'md5':
'98b87b171b7240b7cae6e07d8d0bc9be',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'model':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2_online/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"conformer_online_multicn-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz',
'md5':
'0ac93d390552336f2a906aec9e33c5fa',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/chunk_conformer/checkpoints/multi_cn',
'model':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'params':
'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}
paddlespeech/server/engine/asr/paddleinference/asr_engine.py
浏览文件 @ b361a738
......@@ -19,6 +19,7 @@ from typing import Optional
import paddle
from yacs.config import CfgNode
from .pretrained_models import pretrained_models
from paddlespeech.cli.asr.infer import ASRExecutor
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import MODEL_HOME
......@@ -31,32 +32,11 @@ from paddlespeech.server.utils.paddle_predictor import run_model
__all__ = ['ASREngine']
pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'model':
'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}
class ASRServerExecutor(ASRExecutor):
def __init__(self):
super().__init__()
pass
self.pretrained_models = pretrained_models
def _init_from_path(self,
model_type: str='wenetspeech',
......@@ -71,18 +51,18 @@ class ASRServerExecutor(ASRExecutor):
Init model and other resources from a specific path.
"""
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
if cfg_path is None or am_model is None or am_params is None:
sample_rate_str = '16k' if sample_rate == 16000 else '8k'
tag = model_type + '-' + lang + '-' + sample_rate_str
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.am_model = os.path.join(res_path,
pretrained_models[tag]['model'])
self.pretrained_models[tag]['model'])
self.am_params = os.path.join(res_path,
pretrained_models[tag]['params'])
self.pretrained_models[tag]['params'])
logger.info(res_path)
logger.info(self.cfg_path)
logger.info(self.am_model)
......@@ -109,8 +89,8 @@ class ASRServerExecutor(ASRExecutor):
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = pretrained_models[tag]['lm_url']
lm_md5 = pretrained_models[tag]['lm_md5']
lm_url = self.pretrained_models[tag]['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5']
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
......
paddlespeech/server/engine/asr/paddleinference/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
pretrained_models = {
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'model':
'exp/deepspeech2/checkpoints/avg_1.jit.pdmodel',
'params':
'exp/deepspeech2/checkpoints/avg_1.jit.pdiparams',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
}
paddlespeech/server/engine/cls/paddleinference/cls_engine.py
浏览文件 @ b361a738
......@@ -20,83 +20,20 @@ import numpy as np
import paddle
import yaml
from .pretrained_models import pretrained_models
from paddlespeech.cli.cls.infer import CLSExecutor
from paddlespeech.cli.log import logger
from paddlespeech.cli.utils import download_and_decompress
from paddlespeech.cli.utils import MODEL_HOME
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.paddle_predictor import init_predictor
from paddlespeech.server.utils.paddle_predictor import run_model
__all__ = ['CLSEngine']
pretrained_models = {
"panns_cnn6-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
'md5':
'da087c31046d23281d8ec5188c1967da',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
'md5':
'5460cc6eafbfaf0f261cc75b90284ae1',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
'md5':
'ccc80b194821274da79466862b2ab00f',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
}
class CLSServerExecutor(CLSExecutor):
def __init__(self):
super().__init__()
pass
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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
self.pretrained_models = pretrained_models
def _init_from_path(
self,
......@@ -113,14 +50,14 @@ class CLSServerExecutor(CLSExecutor):
if cfg_path is None or model_path is None or params_path is None or label_file is None:
tag = model_type + '-' + '32k'
self.res_path = self._get_pretrained_path(tag)
self.cfg_path = os.path.join(self.res_path,
pretrained_models[tag]['cfg_path'])
self.model_path = os.path.join(self.res_path,
pretrained_models[tag]['model_path'])
self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path'])
self.model_path = os.path.join(
self.res_path, self.pretrained_models[tag]['model_path'])
self.params_path = os.path.join(
self.res_path, pretrained_models[tag]['params_path'])
self.label_file = os.path.join(self.res_path,
pretrained_models[tag]['label_file'])
self.res_path, self.pretrained_models[tag]['params_path'])
self.label_file = os.path.join(
self.res_path, self.pretrained_models[tag]['label_file'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.model_path = os.path.abspath(model_path)
......
paddlespeech/server/engine/cls/paddleinference/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
pretrained_models = {
"panns_cnn6-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
'md5':
'da087c31046d23281d8ec5188c1967da',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
'md5':
'5460cc6eafbfaf0f261cc75b90284ae1',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url':
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
'md5':
'ccc80b194821274da79466862b2ab00f',
'cfg_path':
'panns.yaml',
'model_path':
'inference.pdmodel',
'params_path':
'inference.pdiparams',
'label_file':
'audioset_labels.txt',
},
}
paddlespeech/server/engine/tts/online/onnx/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
# support online model
pretrained_models = {
# fastspeech2
"fastspeech2_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip',
'md5':
'fd3ad38d83273ad51f0ea4f4abf3ab4e',
'ckpt': ['fastspeech2_csmsc.onnx'],
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
"fastspeech2_cnndecoder_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip',
'md5':
'5f70e1a6bcd29d72d54e7931aa86f266',
'ckpt': [
'fastspeech2_csmsc_am_encoder_infer.onnx',
'fastspeech2_csmsc_am_decoder.onnx',
'fastspeech2_csmsc_am_postnet.onnx',
],
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
# mb_melgan
"mb_melgan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip',
'md5':
'5b83ec746e8414bc29032d954ffd07ec',
'ckpt':
'mb_melgan_csmsc.onnx',
'sample_rate':
24000,
},
# hifigan
"hifigan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip',
'md5':
'1a7dc0385875889e46952e50c0994a6b',
'ckpt':
'hifigan_csmsc.onnx',
'sample_rate':
24000,
},
}
paddlespeech/server/engine/tts/online/onnx/tts_engine.py
浏览文件 @ b361a738
......@@ -20,10 +20,9 @@ from typing import Optional
import numpy as np
import paddle
from .pretrained_models import pretrained_models
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.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.onnx_infer import get_sess
......@@ -34,83 +33,6 @@ from paddlespeech.t2s.frontend.zh_frontend import Frontend
__all__ = ['TTSEngine']
# support online model
pretrained_models = {
# fastspeech2
"fastspeech2_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_csmsc_onnx_0.2.0.zip',
'md5':
'fd3ad38d83273ad51f0ea4f4abf3ab4e',
'ckpt': ['fastspeech2_csmsc.onnx'],
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
"fastspeech2_cnndecoder_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip',
'md5':
'5f70e1a6bcd29d72d54e7931aa86f266',
'ckpt': [
'fastspeech2_csmsc_am_encoder_infer.onnx',
'fastspeech2_csmsc_am_decoder.onnx',
'fastspeech2_csmsc_am_postnet.onnx',
],
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'sample_rate':
24000,
},
# mb_melgan
"mb_melgan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip',
'md5':
'5b83ec746e8414bc29032d954ffd07ec',
'ckpt':
'mb_melgan_csmsc.onnx',
'sample_rate':
24000,
},
# hifigan
"hifigan_csmsc_onnx-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_onnx_0.2.0.zip',
'md5':
'1a7dc0385875889e46952e50c0994a6b',
'ckpt':
'hifigan_csmsc.onnx',
'sample_rate':
24000,
},
}
model_alias = {
# acoustic model
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
# voc
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
}
__all__ = ['TTSEngine']
class TTSServerExecutor(TTSExecutor):
def __init__(self, am_block, am_pad, voc_block, voc_pad, voc_upsample):
......@@ -122,23 +44,6 @@ class TTSServerExecutor(TTSExecutor):
self.voc_upsample = voc_upsample
self.pretrained_models = pretrained_models
self.model_alias = model_alias
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
#Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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,
......@@ -173,10 +78,10 @@ class TTSServerExecutor(TTSExecutor):
am_res_path = self._get_pretrained_path(am_tag)
self.am_res_path = am_res_path
self.am_ckpt = os.path.join(
am_res_path, pretrained_models[am_tag]['ckpt'][0])
am_res_path, self.pretrained_models[am_tag]['ckpt'][0])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['phones_dict'])
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
else:
self.am_ckpt = os.path.abspath(am_ckpt[0])
......@@ -192,16 +97,16 @@ class TTSServerExecutor(TTSExecutor):
am_res_path = self._get_pretrained_path(am_tag)
self.am_res_path = am_res_path
self.am_encoder_infer = os.path.join(
am_res_path, pretrained_models[am_tag]['ckpt'][0])
am_res_path, self.pretrained_models[am_tag]['ckpt'][0])
self.am_decoder = os.path.join(
am_res_path, pretrained_models[am_tag]['ckpt'][1])
am_res_path, self.pretrained_models[am_tag]['ckpt'][1])
self.am_postnet = os.path.join(
am_res_path, pretrained_models[am_tag]['ckpt'][2])
am_res_path, self.pretrained_models[am_tag]['ckpt'][2])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['phones_dict'])
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
self.am_stat = os.path.join(
am_res_path, pretrained_models[am_tag]['speech_stats'])
am_res_path, self.pretrained_models[am_tag]['speech_stats'])
else:
self.am_encoder_infer = os.path.abspath(am_ckpt[0])
......@@ -229,8 +134,8 @@ class TTSServerExecutor(TTSExecutor):
if voc_ckpt is None:
voc_res_path = self._get_pretrained_path(voc_tag)
self.voc_res_path = voc_res_path
self.voc_ckpt = os.path.join(voc_res_path,
pretrained_models[voc_tag]['ckpt'])
self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt'])
else:
self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
......@@ -283,7 +188,6 @@ class TTSServerExecutor(TTSExecutor):
"""
Model inference and result stored in self.output.
"""
#import pdb;pdb.set_trace()
am_block = self.am_block
am_pad = self.am_pad
......@@ -453,10 +357,21 @@ class TTSEngine(BaseEngine):
self.config.am_block, self.config.am_pad, self.config.voc_block,
self.config.voc_pad, self.config.voc_upsample)
if "cpu" in self.config.am_sess_conf.device or "cpu" in self.config.voc_sess_conf.device:
paddle.set_device("cpu")
else:
paddle.set_device(self.config.am_sess_conf.device)
try:
if self.config.am_sess_conf.device is not None:
self.device = self.config.am_sess_conf.device
elif self.config.voc_sess_conf.device is not None:
self.device = self.config.voc_sess_conf.device
else:
self.device = paddle.get_device()
paddle.set_device(self.device)
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
)
logger.error("Initialize TTS server engine Failed on device: %s." %
(self.device))
return False
try:
self.executor._init_from_path(
......@@ -480,16 +395,17 @@ class TTSEngine(BaseEngine):
(self.config.voc_sess_conf.device))
return False
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.config.voc_sess_conf.device))
# warm up
try:
self.warm_up()
logger.info("Warm up successfully.")
except Exception as e:
logger.error("Failed to warm up on tts engine.")
return False
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.config.voc_sess_conf.device))
return True
def warm_up(self):
......@@ -499,9 +415,7 @@ class TTSEngine(BaseEngine):
sentence = "您好，欢迎使用语音合成服务。"
if self.config.lang == 'en':
sentence = "Hello and welcome to the speech synthesis service."
logger.info(
"*******************************warm up ********************************"
)
logger.info("Start to warm up.")
for i in range(3):
for wav in self.executor.infer(
text=sentence,
......@@ -512,9 +426,6 @@ class TTSEngine(BaseEngine):
f"The first response time of the {i} warm up: {self.executor.first_response_time} s"
)
break
logger.info(
"**********************************************************************"
)
def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
# Convert byte to text
......
paddlespeech/server/engine/tts/online/python/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
# support online model
pretrained_models = {
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_cnndecoder_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip',
'md5':
'6eb28e22ace73e0ebe7845f86478f89f',
'config':
'cnndecoder.yaml',
'ckpt':
'snapshot_iter_153000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
}
paddlespeech/server/engine/tts/online/python/tts_engine.py
浏览文件 @ b361a738
......@@ -22,10 +22,9 @@ import paddle
import yaml
from yacs.config import CfgNode
from .pretrained_models import pretrained_models
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.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import float2pcm
......@@ -37,87 +36,6 @@ from paddlespeech.t2s.modules.normalizer import ZScore
__all__ = ['TTSEngine']
# support online model
pretrained_models = {
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_cnndecoder_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_ckpt_1.0.0.zip',
'md5':
'6eb28e22ace73e0ebe7845f86478f89f',
'config':
'cnndecoder.yaml',
'ckpt':
'snapshot_iter_153000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
}
model_alias = {
# acoustic model
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
# voc
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
}
__all__ = ['TTSEngine']
class TTSServerExecutor(TTSExecutor):
def __init__(self, am_block, am_pad, voc_block, voc_pad):
......@@ -126,6 +44,7 @@ class TTSServerExecutor(TTSExecutor):
self.am_pad = am_pad
self.voc_block = voc_block
self.voc_pad = voc_pad
self.pretrained_models = pretrained_models
def get_model_info(self,
field: str,
......@@ -146,7 +65,7 @@ class TTSServerExecutor(TTSExecutor):
[Tensor]: standard deviation
"""
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
if field == "am":
odim = self.am_config.n_mels
......@@ -169,22 +88,6 @@ class TTSServerExecutor(TTSExecutor):
return model, model_mu, model_std
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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',
......@@ -210,15 +113,15 @@ class TTSServerExecutor(TTSExecutor):
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,
pretrained_models[am_tag]['config'])
self.am_config = os.path.join(
am_res_path, self.pretrained_models[am_tag]['config'])
self.am_ckpt = os.path.join(am_res_path,
pretrained_models[am_tag]['ckpt'])
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join(
am_res_path, pretrained_models[am_tag]['speech_stats'])
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, pretrained_models[am_tag]['phones_dict'])
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
print("self.phones_dict:", self.phones_dict)
logger.info(am_res_path)
logger.info(self.am_config)
......@@ -239,12 +142,12 @@ class TTSServerExecutor(TTSExecutor):
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,
pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(voc_res_path,
pretrained_models[voc_tag]['ckpt'])
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, pretrained_models[voc_tag]['speech_stats'])
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)
......@@ -286,7 +189,7 @@ class TTSServerExecutor(TTSExecutor):
self.am_ckpt, self.am_stat)
am_normalizer = ZScore(am_mu, am_std)
am_inference_class = dynamic_import(self.am_name + '_inference',
model_alias)
self.model_alias)
self.am_inference = am_inference_class(am_normalizer, am)
self.am_inference.eval()
print("acoustic model done!")
......@@ -297,7 +200,7 @@ class TTSServerExecutor(TTSExecutor):
self.voc_ckpt, self.voc_stat)
voc_normalizer = ZScore(voc_mu, voc_std)
voc_inference_class = dynamic_import(self.voc_name + '_inference',
model_alias)
self.model_alias)
self.voc_inference = voc_inference_class(voc_normalizer, voc)
self.voc_inference.eval()
print("voc done!")
......@@ -477,7 +380,7 @@ class TTSEngine(BaseEngine):
), "Please set correct voc_block and voc_pad, they should be more than 0."
try:
if self.config.device:
if self.config.device is not None:
self.device = self.config.device
else:
self.device = paddle.get_device()
......@@ -513,16 +416,16 @@ class TTSEngine(BaseEngine):
(self.device))
return False
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.device))
# warm up
try:
self.warm_up()
logger.info("Warm up successfully.")
except Exception as e:
logger.error("Failed to warm up on tts engine.")
return False
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.device))
return True
def warm_up(self):
......@@ -532,9 +435,7 @@ class TTSEngine(BaseEngine):
sentence = "您好，欢迎使用语音合成服务。"
if self.config.lang == 'en':
sentence = "Hello and welcome to the speech synthesis service."
logger.info(
"*******************************warm up ********************************"
)
logger.info("Start to warm up.")
for i in range(3):
for wav in self.executor.infer(
text=sentence,
......@@ -545,9 +446,6 @@ class TTSEngine(BaseEngine):
f"The first response time of the {i} warm up: {self.executor.first_response_time} s"
)
break
logger.info(
"**********************************************************************"
)
def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
# Convert byte to text
......
paddlespeech/server/engine/tts/paddleinference/pretrained_models.py 0 → 100644
浏览文件 @ b361a738
# Copyright (c) 2022 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.
# 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,
},
}
paddlespeech/server/engine/tts/paddleinference/tts_engine.py
浏览文件 @ b361a738
......@@ -23,10 +23,9 @@ import paddle
import soundfile as sf
from scipy.io import wavfile
from .pretrained_models import pretrained_models
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.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import change_speed
from paddlespeech.server.utils.errors import ErrorCode
......@@ -38,101 +37,11 @@ from paddlespeech.t2s.frontend.zh_frontend import Frontend
__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
self.pretrained_models = pretrained_models
def _init_from_path(
self,
......@@ -161,14 +70,14 @@ class TTSServerExecutor(TTSExecutor):
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'])
self.am_model = os.path.join(
am_res_path, self.pretrained_models[am_tag]['model'])
self.am_params = os.path.join(
am_res_path, self.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']
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
self.am_sample_rate = self.pretrained_models[am_tag]['sample_rate']
logger.info(am_res_path)
logger.info(self.am_model)
......@@ -183,17 +92,17 @@ class TTSServerExecutor(TTSExecutor):
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in pretrained_models[am_tag]:
if 'tones_dict' in self.pretrained_models[am_tag]:
self.tones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['tones_dict'])
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 pretrained_models[am_tag]:
if 'speaker_dict' in self.pretrained_models[am_tag]:
self.speaker_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['speaker_dict'])
am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
......@@ -202,11 +111,12 @@ class TTSServerExecutor(TTSExecutor):
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']
self.voc_model = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['model'])
self.voc_params = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['params'])
self.voc_sample_rate = self.pretrained_models[voc_tag][
'sample_rate']
logger.info(voc_res_path)
logger.info(self.voc_model)
logger.info(self.voc_params)
......@@ -352,8 +262,24 @@ class TTSEngine(BaseEngine):
def init(self, config: dict) -> bool:
self.executor = TTSServerExecutor()
self.config = config
try:
if self.config.am_predictor_conf.device is not None:
self.device = self.config.am_predictor_conf.device
elif self.config.voc_predictor_conf.device is not None:
self.device = self.config.voc_predictor_conf.device
else:
self.device = paddle.get_device()
paddle.set_device(self.device)
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
)
logger.error("Initialize TTS server engine Failed on device: %s." %
(self.device))
return False
self.executor._init_from_path(
am=self.config.am,
am_model=self.config.am_model,
......@@ -370,9 +296,35 @@ class TTSEngine(BaseEngine):
am_predictor_conf=self.config.am_predictor_conf,
voc_predictor_conf=self.config.voc_predictor_conf, )
# warm up
try:
self.warm_up()
logger.info("Warm up successfully.")
except Exception as e:
logger.error("Failed to warm up on tts engine.")
return False
logger.info("Initialize TTS server engine successfully.")
return True
def warm_up(self):
"""warm up
"""
if self.config.lang == 'zh':
sentence = "您好，欢迎使用语音合成服务。"
if self.config.lang == 'en':
sentence = "Hello and welcome to the speech synthesis service."
logger.info("Start to warm up.")
for i in range(3):
st = time.time()
self.executor.infer(
text=sentence,
lang=self.config.lang,
am=self.config.am,
spk_id=0, )
logger.info(
f"The response time of the {i} warm up: {time.time() - st} s")
def postprocess(self,
wav,
original_fs: int,
......
paddlespeech/server/engine/tts/python/tts_engine.py
浏览文件 @ b361a738
......@@ -51,15 +51,15 @@ class TTSEngine(BaseEngine):
def init(self, config: dict) -> bool:
self.executor = TTSServerExecutor()
self.config = config
try:
self.config = config
if self.config.device:
if self.config.device is not None:
self.device = self.config.device
else:
self.device = paddle.get_device()
paddle.set_device(self.device)
except BaseException:
except BaseException as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
)
......@@ -87,10 +87,36 @@ class TTSEngine(BaseEngine):
(self.device))
return False
# warm up
try:
self.warm_up()
logger.info("Warm up successfully.")
except Exception as e:
logger.error("Failed to warm up on tts engine.")
return False
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.device))
return True
def warm_up(self):
"""warm up
"""
if self.config.lang == 'zh':
sentence = "您好，欢迎使用语音合成服务。"
if self.config.lang == 'en':
sentence = "Hello and welcome to the speech synthesis service."
logger.info("Start to warm up.")
for i in range(3):
st = time.time()
self.executor.infer(
text=sentence,
lang=self.config.lang,
am=self.config.am,
spk_id=0, )
logger.info(
f"The response time of the {i} warm up: {time.time() - st} s")
def postprocess(self,
wav,
original_fs: int,
......
paddlespeech/server/restful/tts_api.py
浏览文件 @ b361a738
......@@ -128,7 +128,7 @@ def tts(request_body: TTSRequest):
return response
@router.post("/paddlespeech/streaming/tts")
@router.post("/paddlespeech/tts/streaming")
async def stream_tts(request_body: TTSRequest):
text = request_body.text
......
paddlespeech/server/tests/tts/online/http_client.py
浏览文件 @ b361a738
......@@ -14,6 +14,7 @@
import argparse
from paddlespeech.server.utils.audio_handler import TTSHttpHandler
from paddlespeech.server.utils.util import compute_delay
if __name__ == "__main__":
parser = argparse.ArgumentParser()
......@@ -43,5 +44,25 @@ if __name__ == "__main__":
print("tts http client start")
handler = TTSHttpHandler(args.server, args.port, args.play)
handler.run(args.text, args.spk_id, args.speed, args.volume,
args.sample_rate, args.output)
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = handler.run(
args.text, args.spk_id, args.speed, args.volume, args.sample_rate,
args.output)
delay_time_list = compute_delay(receive_time_list, chunk_duration_list)
print(f"sentence: {args.text}")
print(f"duration: {duration} s")
print(f"first response: {first_response} s")
print(f"final response: {final_response} s")
print(f"RTF: {final_response/duration}")
if args.output is not None:
if save_audio_success:
print(f"Audio successfully saved in {args.output}")
else:
print("Audio save failed.")
if delay_time_list != []:
print(
f"Delay situation: total number of packages: {len(receive_time_list)}, the number of delayed packets: {len(delay_time_list)}, minimum delay time: {min(delay_time_list)} s, maximum delay time: {max(delay_time_list)} s, average delay time: {sum(delay_time_list)/len(delay_time_list)} s, delay rate:{len(delay_time_list)/len(receive_time_list)}"
)
else:
print("The sentence has no delay in streaming synthesis.")
paddlespeech/server/tests/tts/online/ws_client.py
浏览文件 @ b361a738
......@@ -15,6 +15,7 @@ import argparse
import asyncio
from paddlespeech.server.utils.audio_handler import TTSWsHandler
from paddlespeech.server.utils.util import compute_delay
if __name__ == "__main__":
parser = argparse.ArgumentParser()
......@@ -35,4 +36,24 @@ if __name__ == "__main__":
print("tts websocket client start")
handler = TTSWsHandler(args.server, args.port, args.play)
loop = asyncio.get_event_loop()
loop.run_until_complete(handler.run(args.text, args.output))
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = loop.run_until_complete(
handler.run(args.text, args.output))
delay_time_list = compute_delay(receive_time_list, chunk_duration_list)
print(f"sentence: {args.text}")
print(f"duration: {duration} s")
print(f"first response: {first_response} s")
print(f"final response: {final_response} s")
print(f"RTF: {final_response/duration}")
if args.output is not None:
if save_audio_success:
print(f"Audio successfully saved in {args.output}")
else:
print("Audio save failed.")
if delay_time_list != []:
print(
f"Delay situation: total number of packages: {len(receive_time_list)}, the number of delayed packets: {len(delay_time_list)}, minimum delay time: {min(delay_time_list)} s, maximum delay time: {max(delay_time_list)} s, average delay time: {sum(delay_time_list)/len(delay_time_list)} s, delay rate:{len(delay_time_list)/len(receive_time_list)}"
)
else:
print("The sentence has no delay in streaming synthesis.")
paddlespeech/server/utils/audio_handler.py
浏览文件 @ b361a738
......@@ -259,7 +259,8 @@ class TTSWsHandler:
"""
self.server = server
self.port = port
self.url = "ws://" + self.server + ":" + str(self.port) + "/ws/tts"
self.url = "ws://" + self.server + ":" + str(
self.port) + "/paddlespeech/tts/streaming"
self.play = play
if self.play:
import pyaudio
......@@ -295,6 +296,8 @@ class TTSWsHandler:
output (str): save audio path
"""
all_bytes = b''
receive_time_list = []
chunk_duration_list = []
# 1. Send websocket handshake protocal
async with websockets.connect(self.url) as ws:
......@@ -309,14 +312,15 @@ class TTSWsHandler:
# 3. Process the received response
message = await ws.recv()
logger.info(f"句子：{text}")
logger.info(f"首包响应：{time.time() - st} s")
first_response = time.time() - st
message = json.loads(message)
status = message["status"]
while (status == 1):
receive_time_list.append(time.time())
audio = message["audio"]
audio = base64.b64decode(audio) # bytes
chunk_duration_list.append(len(audio) / 2.0 / 24000)
all_bytes += audio
if self.play:
self.mutex.acquire()
......@@ -334,15 +338,11 @@ class TTSWsHandler:
if status == 2:
final_response = time.time() - st
duration = len(all_bytes) / 2.0 / 24000
logger.info(f"尾包响应：{final_response} s")
logger.info(f"音频时长：{duration} s")
logger.info(f"RTF: {final_response / duration}")
if output is not None:
if save_audio(all_bytes, output):
logger.info(f"音频保存至：{output}")
else:
logger.error("save audio error")
save_audio_success = save_audio(all_bytes, output)
else:
save_audio_success = False
else:
logger.error("infer error")
......@@ -352,6 +352,8 @@ class TTSWsHandler:
self.stream.close()
self.p.terminate()
return first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list
class TTSHttpHandler:
def __init__(self, server="127.0.0.1", port=8092, play: bool=False):
......@@ -365,7 +367,7 @@ class TTSHttpHandler:
self.server = server
self.port = port
self.url = "http://" + str(self.server) + ":" + str(
self.port) + "/paddlespeech/streaming/tts"
self.port) + "/paddlespeech/tts/streaming"
self.play = play
if self.play:
......@@ -423,13 +425,16 @@ class TTSHttpHandler:
all_bytes = b''
first_flag = 1
receive_time_list = []
chunk_duration_list = []
# 2. Send request
st = time.time()
html = requests.post(self.url, json.dumps(params), stream=True)
# 3. Process the received response
for chunk in html.iter_content(chunk_size=1024):
for chunk in html.iter_content(chunk_size=None):
receive_time_list.append(time.time())
audio = base64.b64decode(chunk) # bytes
if first_flag:
first_response = time.time() - st
......@@ -443,21 +448,15 @@ class TTSHttpHandler:
self.t.start()
self.start_play = False
all_bytes += audio
chunk_duration_list.append(len(audio) / 2.0 / 24000)
final_response = time.time() - st
duration = len(all_bytes) / 2.0 / 24000
logger.info(f"句子：{text}")
logger.info(f"首包响应：{first_response} s")
logger.info(f"尾包响应：{final_response} s")
logger.info(f"音频时长：{duration} s")
logger.info(f"RTF: {final_response / duration}")
if output is not None:
if save_audio(all_bytes, output):
logger.info(f"音频保存至：{output}")
else:
logger.error("save audio error")
save_audio_success = save_audio(all_bytes, output)
else:
save_audio_success = False
if self.play:
self.t.join()
......@@ -465,6 +464,8 @@ class TTSHttpHandler:
self.stream.close()
self.p.terminate()
return first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list
class VectorHttpHandler:
def __init__(self, server_ip=None, port=None):
......
paddlespeech/server/utils/util.py
浏览文件 @ b361a738
......@@ -75,3 +75,74 @@ def get_chunks(data, block_size, pad_size, step):
else:
print("Please set correct type to get chunks, am or voc")
return chunks
def compute_delay(receive_time_list, chunk_duration_list):
"""compute delay
Args:
receive_time_list (list): Time to receive each packet
chunk_duration_list (list): The audio duration corresponding to each packet
Returns:
[list]: Delay time list
"""
assert (len(receive_time_list) == len(chunk_duration_list))
delay_time_list = []
play_time = receive_time_list[0] + chunk_duration_list[0]
for i in range(1, len(receive_time_list)):
receive_time = receive_time_list[i]
delay_time = receive_time - play_time
# 有延迟
if delay_time > 0:
play_time = play_time + delay_time + chunk_duration_list[i]
delay_time_list.append(delay_time)
# 没有延迟
else:
play_time = play_time + chunk_duration_list[i]
return delay_time_list
def count_engine(logfile: str="./nohup.out"):
"""For inference on the statistical engine side
Args:
logfile (str, optional): server log. Defaults to "./nohup.out".
"""
first_response_list = []
final_response_list = []
duration_list = []
with open(logfile, "r") as f:
for line in f.readlines():
if "- first response time:" in line:
first_response = float(line.splie(" ")[-2])
first_response_list.append(first_response)
elif "- final response time:" in line:
final_response = float(line.splie(" ")[-2])
final_response_list.append(final_response)
elif "- The durations of audio is:" in line:
duration = float(line.splie(" ")[-2])
duration_list.append(duration)
assert (len(first_response_list) == len(final_response_list) and
len(final_response_list) == len(duration_list))
avg_first_response = sum(first_response_list) / len(first_response_list)
avg_final_response = sum(final_response_list) / len(final_response_list)
avg_duration = sum(duration_list) / len(duration_list)
RTF = sum(final_response_list) / sum(duration_list)
print(
"************************* engine result ***************************************"
)
print(
f"test num: {len(duration_list)}, avg first response: {avg_first_response} s, avg final response: {avg_final_response} s, avg duration: {avg_duration}, RTF: {RTF}"
)
print(
f"min duration: {min(duration_list)} s, max duration: {max(duration_list)} s"
)
print(
f"max first response: {max(first_response_list)} s, min first response: {min(first_response_list)} s"
)
print(
f"max final response: {max(final_response_list)} s, min final response: {min(final_response_list)} s"
)
paddlespeech/server/ws/tts_socket.py
浏览文件 @ b361a738
......@@ -24,7 +24,7 @@ from paddlespeech.server.engine.engine_pool import get_engine_pool
router = APIRouter()
@router.websocket('/ws/tts')
@router.websocket('/paddlespeech/tts/streaming')
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
......
tests/unit/server/offline/change_yaml.py
浏览文件 @ b361a738
......@@ -19,7 +19,7 @@ def change_device(yamlfile: str, engine: str, device: str):
if device == 'cpu':
set_device = 'cpu'
elif device == 'gpu':
set_device = 'gpu:0'
set_device = 'gpu:3'
else:
print("Please set correct device: cpu or gpu.")
......
tests/unit/server/offline/conf/application.yaml
浏览文件 @ b361a738
# This is the parameter configuration file for PaddleSpeech Serving.
# This is the parameter configuration file for PaddleSpeech Offline Serving.
#################################################################################
# SERVER SETTING #
......@@ -7,8 +7,8 @@ host: 127.0.0.1
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference', 'cls_python', 'cls_inference']
protocol: 'http'
engine_list: ['asr_python', 'tts_python', 'cls_python']
......
tests/unit/server/offline/test_server_client.sh
浏览文件 @ b361a738
......@@ -21,6 +21,8 @@ StartService(){
}
ClientTest(){
echo "aaaaaaaaaaaaaaaaaaaaaaaaaa $server_ip"
echo "aaaaaaaaaaaaaaaaaaaaaaaaaa $port"
# Client test
# test asr client
paddlespeech_client asr --server_ip $server_ip --port $port --input ./zh.wav
......@@ -39,6 +41,7 @@ ClientTest(){
((test_times+=1))
paddlespeech_client cls --server_ip $server_ip --port $port --input ./zh.wav
((test_times+=1))
}
GetTestResult() {
......@@ -58,6 +61,7 @@ rm -rf log/server.log.wf
rm -rf log/server.log
rm -rf log/test_result.log
cp ../../../../demos/speech_server/conf/application.yaml ./conf/
config_file=./conf/application.yaml
server_ip=$(cat $config_file | grep "host" | awk -F " " '{print $2}')
port=$(cat $config_file | grep "port" | awk '/port:/ {print $2}')
......@@ -191,5 +195,4 @@ echo "***************** Here are all the test results ********************"
cat ./log/test_result.log
# Restoring conf is the same as demos/speech_server
rm -rf ./conf
cp ../../../demos/speech_server/conf/ ./ -rf
\ No newline at end of file
cp ../../../../demos/speech_server/conf/application.yaml ./conf/
tests/unit/server/online/tts/check_server/conf/application.yaml
浏览文件 @ b361a738
......@@ -39,9 +39,9 @@ tts_online:
# others
lang: 'zh'
device: 'cpu' # set 'gpu:id' or 'cpu'
am_block: 42
am_block: 72
am_pad: 12
voc_block: 14
voc_block: 36
voc_pad: 14
......@@ -80,9 +80,9 @@ tts_online-onnx:
# others
lang: 'zh'
am_block: 42
am_block: 72
am_pad: 12
voc_block: 14
voc_block: 36
voc_pad: 14
voc_upsample: 300
tests/unit/server/online/tts/check_server/test_all.sh
浏览文件 @ b361a738
......@@ -10,7 +10,6 @@ bash test.sh tts_online $log_all_dir/log_tts_online_cpu
python change_yaml.py --change_type engine_type --target_key engine_list --target_value tts_online-onnx
bash test.sh tts_online-onnx $log_all_dir/log_tts_online-onnx_cpu
python change_yaml.py --change_type device --target_key device --target_value gpu:3
bash test.sh tts_online $log_all_dir/log_tts_online_gpu
......
tests/unit/server/online/tts/check_server/tts_online_application.yaml
浏览文件 @ b361a738
......@@ -39,9 +39,9 @@ tts_online:
# others
lang: 'zh'
device: 'cpu' # set 'gpu:id' or 'cpu'
am_block: 42
am_block: 72
am_pad: 12
voc_block: 14
voc_block: 36
voc_pad: 14
......@@ -80,9 +80,9 @@ tts_online-onnx:
# others
lang: 'zh'
am_block: 42
am_block: 72
am_pad: 12
voc_block: 14
voc_block: 36
voc_pad: 14
voc_upsample: 300
tests/unit/server/online/tts/test_server/test_http_client.py
浏览文件 @ b361a738
......@@ -12,117 +12,35 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import base64
import json
import asyncio
import os
import time
import requests
from paddlespeech.server.utils.audio_process import pcm2wav
from paddlespeech.server.utils.util import compute_delay
from paddlespeech.t2s.exps.syn_utils import get_sentences
def save_audio(buffer, audio_path) -> bool:
if audio_path.endswith("pcm"):
with open(audio_path, "wb") as f:
f.write(buffer)
elif audio_path.endswith("wav"):
with open("./tmp.pcm", "wb") as f:
f.write(buffer)
pcm2wav("./tmp.pcm", audio_path, channels=1, bits=16, sample_rate=24000)
os.system("rm ./tmp.pcm")
else:
print("Only supports saved audio format is pcm or wav")
return False
return True
def test(args, text, utt_id):
params = {
"text": text,
"spk_id": args.spk_id,
"speed": args.speed,
"volume": args.volume,
"sample_rate": args.sample_rate,
"save_path": ''
}
buffer = b''
flag = 1
url = "http://" + str(args.server) + ":" + str(
args.port) + "/paddlespeech/streaming/tts"
st = time.time()
html = requests.post(url, json.dumps(params), stream=True)
for chunk in html.iter_content(chunk_size=1024):
chunk = base64.b64decode(chunk) # bytes
if flag:
first_response = time.time() - st
print(f"首包响应：{first_response} s")
flag = 0
buffer += chunk
final_response = time.time() - st
duration = len(buffer) / 2.0 / 24000
print(f"sentence: {text}")
print(f"尾包响应：{final_response} s")
print(f"音频时长：{duration} s")
print(f"RTF: {final_response / duration}")
save_path = str(args.output_dir + "/" + utt_id + ".wav")
save_audio(buffer, save_path)
print("音频保存至：", save_path)
return first_response, final_response, duration
def count_engine(logfile: str="./nohup.out"):
"""For inference on the statistical engine side
Args:
logfile (str, optional): server log. Defaults to "./nohup.out".
"""
first_response_list = []
final_response_list = []
duration_list = []
output = str(args.output_dir + "/" + utt_id + ".wav")
if args.protocol == "http":
print("tts http client start")
from paddlespeech.server.utils.audio_handler import TTSHttpHandler
handler = TTSHttpHandler(args.server_ip, args.port, args.play)
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = handler.run(
text, args.spk_id, args.speed, args.volume, args.sample_rate,
output)
elif args.protocol == "websocket":
from paddlespeech.server.utils.audio_handler import TTSWsHandler
print("tts websocket client start")
handler = TTSWsHandler(args.server_ip, args.port, args.play)
loop = asyncio.get_event_loop()
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = loop.run_until_complete(
handler.run(text, output))
with open(logfile, "r") as f:
for line in f.readlines():
if "- first response time:" in line:
first_response = float(line.splie(" ")[-2])
first_response_list.append(first_response)
elif "- final response time:" in line:
final_response = float(line.splie(" ")[-2])
final_response_list.append(final_response)
elif "- The durations of audio is:" in line:
duration = float(line.splie(" ")[-2])
duration_list.append(duration)
else:
print("Please set correct protocol, http or websocket")
assert (len(first_response_list) == len(final_response_list) and
len(final_response_list) == len(duration_list))
avg_first_response = sum(first_response_list) / len(first_response_list)
avg_final_response = sum(final_response_list) / len(final_response_list)
avg_duration = sum(duration_list) / len(duration_list)
RTF = sum(final_response_list) / sum(duration_list)
print(
"************************* engine result ***************************************"
)
print(
f"test num: {len(duration_list)}, avg first response: {avg_first_response} s, avg final response: {avg_final_response} s, avg duration: {avg_duration}, RTF: {RTF}"
)
print(
f"min duration: {min(duration_list)} s, max duration: {max(duration_list)} s"
)
print(
f"max first response: {max(first_response_list)} s, min first response: {min(first_response_list)} s"
)
print(
f"max final response: {max(final_response_list)} s, min final response: {min(final_response_list)} s"
)
return first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list
if __name__ == "__main__":
......@@ -142,10 +60,18 @@ if __name__ == "__main__":
default=0,
help='Sampling rate, the default is the same as the model')
parser.add_argument(
"--server", type=str, help="server ip", default="127.0.0.1")
"--server_ip", type=str, help="server ip", default="127.0.0.1")
parser.add_argument("--port", type=int, help="server port", default=8092)
parser.add_argument(
"--protocol",
type=str,
choices=['http', 'websocket'],
help="server protocol",
default="http")
parser.add_argument(
"--output_dir", type=str, default="./output", help="output dir")
parser.add_argument(
"--play", type=bool, help="whether to play audio", default=False)
args = parser.parse_args()
......@@ -155,13 +81,35 @@ if __name__ == "__main__":
first_response_list = []
final_response_list = []
duration_list = []
all_delay_list = []
packet_count = 0.0
sentences = get_sentences(text_file=args.text, lang="zh")
for utt_id, sentence in sentences:
first_response, final_response, duration = test(args, sentence, utt_id)
first_response, final_response, duration, save_audio_success, receive_time_list, chunk_duration_list = test(
args, sentence, utt_id)
delay_time_list = compute_delay(receive_time_list, chunk_duration_list)
first_response_list.append(first_response)
final_response_list.append(final_response)
duration_list.append(duration)
packet_count += len(receive_time_list)
print(f"句子：{sentence}")
print(f"首包响应时间：{first_response} s")
print(f"尾包响应时间：{final_response} s")
print(f"音频时长：{duration} s")
print(f"该句RTF：{final_response/duration}")
if delay_time_list != []:
for t in delay_time_list:
all_delay_list.append(t)
print(
f"该句流式合成的延迟情况：总包个数：{len(receive_time_list)}，延迟包个数：{len(delay_time_list)}, 最小延迟时间：{min(delay_time_list)} s, 最大延迟时间：{max(delay_time_list)} s, 平均延迟时间：{sum(delay_time_list)/len(delay_time_list)} s, 延迟率：{len(delay_time_list)/len(receive_time_list)}"
)
else:
print("该句流式合成无延迟情况")
packet_count += len(receive_time_list)
assert (len(first_response_list) == len(final_response_list) and
len(final_response_list) == len(duration_list))
......@@ -170,19 +118,35 @@ if __name__ == "__main__":
avg_final_response = sum(final_response_list) / len(final_response_list)
avg_duration = sum(duration_list) / len(duration_list)
RTF = sum(final_response_list) / sum(duration_list)
if all_delay_list != []:
delay_count = len(all_delay_list)
avg_delay = sum(all_delay_list) / len(all_delay_list)
delay_ratio = len(all_delay_list) / packet_count
min_delay = min(all_delay_list)
max_delay = max(all_delay_list)
else:
delay_count = 0.0
avg_delay = 0.0
delay_ratio = 0.0
min_delay = 0.0
max_delay = 0.0
print(
"************************* server/client result ***************************************"
)
print(
f"test num: {len(duration_list)}, avg first response: {avg_first_response} s, avg final response: {avg_final_response} s, avg duration: {avg_duration}, RTF: {RTF}"
f"test num: {len(duration_list)}, avg first response: {avg_first_response} s, avg final response: {avg_final_response} s, avg duration: {avg_duration}, RTF: {RTF}."
)
print(
f"test num: {len(duration_list)}, packet count: {packet_count}, delay count: {delay_count}, avg delay time: {avg_delay} s, delay ratio: {delay_ratio} "
)
print(
f"min duration: {min(duration_list)} s, max duration: {max(duration_list)} s"
)
print(
f"max first response: {max(first_response_list)} s, min first response: {min(first_response_list)} s"
f"min first response: {min(first_response_list)} s, max first response: {max(first_response_list)} s."
)
print(
f"max final response: {max(final_response_list)} s, min final response: {min(final_response_list)} s"
f"min final response: {min(final_response_list)} s, max final response: {max(final_response_list)} s."
)
print(f"min delay: {min_delay} s, max delay: {max_delay}")
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