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server.sh | 3 years ago | |
tts_client.sh | 3 years ago |
README.md
(简体中文|English)
Speech Server
Introduction
This demo is an implementation of starting the voice service and accessing the service. It can be achieved with a single command using paddlespeech_server
and paddlespeech_client
or a few lines of code in python.
Usage
1. Installation
see installation.
You can choose one way from easy, meduim and hard to install paddlespeech.
2. Prepare config File
The configuration file contains the service-related configuration files and the model configuration related to the voice tasks contained in the service. They are all under the conf
folder.
The input of ASR client demo should be a WAV file(.wav
), and the sample rate must be the same as the model.
Here are sample files for thisASR client demo that can be downloaded:
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
3. Server Usage
-
Command Line (Recommended)
# start the service paddlespeech_server start --config_file ./conf/application.yaml
Usage:
paddlespeech_server start --help
Arguments:
config_file
: yaml file of the app, defalut: ./conf/application.yamllog_file
: log file. Default: ./log/paddlespeech.log
Output:
[2022-02-23 11:17:32] [INFO] [server.py:64] Started server process [6384] INFO: Waiting for application startup. [2022-02-23 11:17:32] [INFO] [on.py:26] Waiting for application startup. INFO: Application startup complete. [2022-02-23 11:17:32] [INFO] [on.py:38] Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit) [2022-02-23 11:17:32] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
-
Python API
from paddlespeech.server.bin.paddlespeech_server import ServerExecutor server_executor = ServerExecutor() server_executor( config_file="./conf/application.yaml", log_file="./log/paddlespeech.log")
Output:
INFO: Started server process [529] [2022-02-23 14:57:56] [INFO] [server.py:64] Started server process [529] INFO: Waiting for application startup. [2022-02-23 14:57:56] [INFO] [on.py:26] Waiting for application startup. INFO: Application startup complete. [2022-02-23 14:57:56] [INFO] [on.py:38] Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit) [2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
4. ASR Client Usage
-
Command Line (Recommended)
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
Usage:
paddlespeech_client asr --help
Arguments:
server_ip
: server ip. Default: 127.0.0.1port
: server port. Default: 8090input
(required): Audio file to be recognized.sample_rate
: Audio ampling rate, default: 16000.lang
: Language. Default: "zh_cn".audio_format
: Audio format. Default: "wav".
Output:
[2022-02-23 18:11:22,819] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}} [2022-02-23 18:11:22,820] [ INFO] - time cost 0.689145 s.
-
Python API
from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor asrclient_executor = ASRClientExecutor() asrclient_executor( input="./zh.wav", server_ip="127.0.0.1", port=8090, sample_rate=16000, lang="zh_cn", audio_format="wav")
Output:
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'transcription': '我认为跑步最重要的就是给我带来了身体健康'}} time cost 0.604353 s.
5. TTS Client Usage
-
Command Line (Recommended)
paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好,欢迎使用百度飞桨语音合成服务。" --output output.wav
Usage:
paddlespeech_client tts --help
Arguments:
server_ip
: server ip. Default: 127.0.0.1port
: server port. Default: 8090input
(required): Input text to generate.spk_id
: Speaker id for multi-speaker text to speech. Default: 0speed
: Audio speed, the value should be set between 0 and 3. Default: 1.0volume
: Audio volume, the value should be set between 0 and 3. Default: 1.0sample_rate
: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0output
: Output wave filepath. Default:output.wav
.
Output:
[2022-02-23 15:20:37,875] [ INFO] - {'description': 'success.'} [2022-02-23 15:20:37,875] [ INFO] - Save synthesized audio successfully on output.wav. [2022-02-23 15:20:37,875] [ INFO] - Audio duration: 3.612500 s. [2022-02-23 15:20:37,875] [ INFO] - Response time: 0.348050 s. [2022-02-23 15:20:37,875] [ INFO] - RTF: 0.096346
-
Python API
from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor ttsclient_executor = TTSClientExecutor() ttsclient_executor( input="您好,欢迎使用百度飞桨语音合成服务。", server_ip="127.0.0.1", port=8090, spk_id=0, speed=1.0, volume=1.0, sample_rate=0, output="./output.wav")
Output:
{'description': 'success.'} Save synthesized audio successfully on ./output.wav. Audio duration: 3.612500 s. Response time: 0.388317 s. RTF: 0.107493
Pretrained Models
ASR model
Here is a list of ASR pretrained models released by PaddleSpeech, both command line and python interfaces are available:
Model | Language | Sample Rate |
---|---|---|
conformer_wenetspeech | zh | 16000 |
transformer_librispeech | en | 16000 |
TTS model
Here is a list of TTS pretrained models released by PaddleSpeech, both command line and python interfaces are available:
-
Acoustic model
Model Language speedyspeech_csmsc zh fastspeech2_csmsc zh fastspeech2_aishell3 zh fastspeech2_ljspeech en fastspeech2_vctk en -
Vocoder
Model Language pwgan_csmsc zh pwgan_aishell3 zh pwgan_ljspeech en pwgan_vctk en mb_melgan_csmsc zh
Here is a list of TTS pretrained static models released by PaddleSpeech, both command line and python interfaces are available:
-
Acoustic model
Model Language speedyspeech_csmsc zh fastspeech2_csmsc zh -
Vocoder
Model Language pwgan_csmsc zh mb_melgan_csmsc zh hifigan_csmsc zh