([简体中文](./README_cn.md)|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](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md). It is recommended to use **paddlepaddle 2.2.1** or above. You can choose one way from meduim and hard to install paddlespeech. ### 2. Prepare config File The configuration file can be found in `conf/application.yaml` . Among them, `engine_list` indicates the speech engine that will be included in the service to be started, in the format of _. At present, the speech tasks integrated by the service include: asr (speech recognition) and tts (speech synthesis). Currently the engine type supports two forms: python and inference (Paddle Inference) 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: ```bash wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav ``` ### 3. Server Usage - Command Line (Recommended) ```bash # start the service paddlespeech_server start --config_file ./conf/application.yaml ``` Usage: ```bash paddlespeech_server start --help ``` Arguments: - `config_file`: yaml file of the app, defalut: ./conf/application.yaml - `log_file`: log file. Default: ./log/paddlespeech.log Output: ```bash [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 ```python from paddlespeech.server.bin.paddlespeech_server import ServerExecutor server_executor = ServerExecutor() server_executor( config_file="./conf/application.yaml", log_file="./log/paddlespeech.log") ``` Output: ```bash 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 **Note:** The response time will be slightly longer when using the client for the first time - Command Line (Recommended) ``` paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav ``` Usage: ```bash paddlespeech_client asr --help ``` Arguments: - `server_ip`: server ip. Default: 127.0.0.1 - `port`: server port. Default: 8090 - `input`(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: ```bash [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 ```python from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor import json asrclient_executor = ASRClientExecutor() res = asrclient_executor( input="./zh.wav", server_ip="127.0.0.1", port=8090, sample_rate=16000, lang="zh_cn", audio_format="wav") print(res.json()) ``` Output: ```bash {'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'}} ``` ### 5. TTS Client Usage **Note:** The response time will be slightly longer when using the client for the first time - Command Line (Recommended) ```bash 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: ```bash paddlespeech_client tts --help ``` Arguments: - `server_ip`: server ip. Default: 127.0.0.1 - `port`: server port. Default: 8090 - `input`(required): Input text to generate. - `spk_id`: Speaker id for multi-speaker text to speech. Default: 0 - `speed`: Audio speed, the value should be set between 0 and 3. Default: 1.0 - `volume`: Audio volume, the value should be set between 0 and 3. Default: 1.0 - `sample_rate`: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0 - `output`: Output wave filepath. Default: None, which means not to save the audio to the local. Output: ```bash [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. ``` - Python API ```python from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor import json ttsclient_executor = TTSClientExecutor() res = 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") response_dict = res.json() print(response_dict["message"]) print("Save synthesized audio successfully on %s." % (response_dict['result']['save_path'])) print("Audio duration: %f s." %(response_dict['result']['duration'])) ``` Output: ```bash {'description': 'success.'} Save synthesized audio successfully on ./output.wav. Audio duration: 3.612500 s. ``` ### 6. CLS Client Usage **Note:** The response time will be slightly longer when using the client for the first time - Command Line (Recommended) ``` paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav ``` Usage: ```bash paddlespeech_client cls --help ``` Arguments: - `server_ip`: server ip. Default: 127.0.0.1 - `port`: server port. Default: 8090 - `input`(required): Audio file to be classified. - `topk`: topk scores of classification result. Output: ```bash [2022-03-09 20:44:39,974] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}} [2022-03-09 20:44:39,975] [ INFO] - Response time 0.104360 s. ``` - Python API ```python from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor import json clsclient_executor = CLSClientExecutor() res = clsclient_executor( input="./zh.wav", server_ip="127.0.0.1", port=8090, topk=1) print(res.json()) ``` Output: ```bash {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}} ``` ## Models supported by the service ### ASR model Get all models supported by the ASR service via `paddlespeech_server stats --task asr`, where static models can be used for paddle inference inference. ### TTS model Get all models supported by the TTS service via `paddlespeech_server stats --task tts`, where static models can be used for paddle inference inference. ### CLS model Get all models supported by the CLS service via `paddlespeech_server stats --task cls`, where static models can be used for paddle inference inference.