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247 lines
8.2 KiB
247 lines
8.2 KiB
([简体中文](./README_cn.md)|English)
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# Speech Server
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## Introduction
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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.
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## Usage
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### 1. Installation
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see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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It is recommended to use **paddlepaddle 2.2.1** or above.
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You can choose one way from meduim and hard to install paddlespeech.
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### 2. Prepare config File
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The configuration file can be found in `conf/application.yaml` .
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Among them, `engine_list` indicates the speech engine that will be included in the service to be started, in the format of `<speech task>_<engine type>`.
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At present, the speech tasks integrated by the service include: asr (speech recognition), tts (text to sppech) and cls (audio classification).
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Currently the engine type supports two forms: python and inference (Paddle Inference)
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The input of ASR client demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
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Here are sample files for thisASR client demo that can be downloaded:
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
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```
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### 3. Server Usage
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- Command Line (Recommended)
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```bash
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# start the service
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paddlespeech_server start --config_file ./conf/application.yaml
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```
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Usage:
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```bash
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paddlespeech_server start --help
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```
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Arguments:
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- `config_file`: yaml file of the app, defalut: ./conf/application.yaml
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- `log_file`: log file. Default: ./log/paddlespeech.log
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Output:
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```bash
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[2022-02-23 11:17:32] [INFO] [server.py:64] Started server process [6384]
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INFO: Waiting for application startup.
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[2022-02-23 11:17:32] [INFO] [on.py:26] Waiting for application startup.
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INFO: Application startup complete.
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[2022-02-23 11:17:32] [INFO] [on.py:38] Application startup complete.
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INFO: Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
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[2022-02-23 11:17:32] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
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```
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- Python API
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```python
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from paddlespeech.server.bin.paddlespeech_server import ServerExecutor
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server_executor = ServerExecutor()
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server_executor(
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config_file="./conf/application.yaml",
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log_file="./log/paddlespeech.log")
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```
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Output:
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```bash
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INFO: Started server process [529]
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[2022-02-23 14:57:56] [INFO] [server.py:64] Started server process [529]
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INFO: Waiting for application startup.
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[2022-02-23 14:57:56] [INFO] [on.py:26] Waiting for application startup.
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INFO: Application startup complete.
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[2022-02-23 14:57:56] [INFO] [on.py:38] Application startup complete.
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INFO: Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
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[2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
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```
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### 4. ASR Client Usage
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**Note:** The response time will be slightly longer when using the client for the first time
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- Command Line (Recommended)
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```
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paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
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```
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Usage:
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```bash
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paddlespeech_client asr --help
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```
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Arguments:
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- `server_ip`: server ip. Default: 127.0.0.1
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- `port`: server port. Default: 8090
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- `input`(required): Audio file to be recognized.
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- `sample_rate`: Audio ampling rate, default: 16000.
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- `lang`: Language. Default: "zh_cn".
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- `audio_format`: Audio format. Default: "wav".
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Output:
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```bash
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[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'}}
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[2022-02-23 18:11:22,820] [ INFO] - time cost 0.689145 s.
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```
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- Python API
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```python
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from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor
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import json
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asrclient_executor = ASRClientExecutor()
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res = asrclient_executor(
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input="./zh.wav",
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server_ip="127.0.0.1",
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port=8090,
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sample_rate=16000,
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lang="zh_cn",
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audio_format="wav")
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print(res.json())
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```
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Output:
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```bash
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{'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'}}
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```
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### 5. TTS Client Usage
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**Note:** The response time will be slightly longer when using the client for the first time
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- Command Line (Recommended)
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```bash
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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
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```
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Usage:
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```bash
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paddlespeech_client tts --help
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```
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Arguments:
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- `server_ip`: server ip. Default: 127.0.0.1
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- `port`: server port. Default: 8090
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- `input`(required): Input text to generate.
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- `spk_id`: Speaker id for multi-speaker text to speech. Default: 0
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- `speed`: Audio speed, the value should be set between 0 and 3. Default: 1.0
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- `volume`: Audio volume, the value should be set between 0 and 3. Default: 1.0
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- `sample_rate`: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0
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- `output`: Output wave filepath. Default: None, which means not to save the audio to the local.
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Output:
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```bash
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[2022-02-23 15:20:37,875] [ INFO] - {'description': 'success.'}
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[2022-02-23 15:20:37,875] [ INFO] - Save synthesized audio successfully on output.wav.
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[2022-02-23 15:20:37,875] [ INFO] - Audio duration: 3.612500 s.
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[2022-02-23 15:20:37,875] [ INFO] - Response time: 0.348050 s.
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```
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- Python API
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```python
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from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor
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import json
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ttsclient_executor = TTSClientExecutor()
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res = ttsclient_executor(
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input="您好，欢迎使用百度飞桨语音合成服务。",
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server_ip="127.0.0.1",
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port=8090,
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spk_id=0,
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speed=1.0,
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volume=1.0,
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sample_rate=0,
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output="./output.wav")
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response_dict = res.json()
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print(response_dict["message"])
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print("Save synthesized audio successfully on %s." % (response_dict['result']['save_path']))
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print("Audio duration: %f s." %(response_dict['result']['duration']))
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```
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Output:
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```bash
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{'description': 'success.'}
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Save synthesized audio successfully on ./output.wav.
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Audio duration: 3.612500 s.
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```
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### 6. CLS Client Usage
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**Note:** The response time will be slightly longer when using the client for the first time
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- Command Line (Recommended)
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```
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paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
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```
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Usage:
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```bash
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paddlespeech_client cls --help
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```
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Arguments:
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- `server_ip`: server ip. Default: 127.0.0.1
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- `port`: server port. Default: 8090
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- `input`(required): Audio file to be classified.
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- `topk`: topk scores of classification result.
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Output:
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```bash
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[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}]}}
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[2022-03-09 20:44:39,975] [ INFO] - Response time 0.104360 s.
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```
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- Python API
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```python
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from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
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import json
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clsclient_executor = CLSClientExecutor()
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res = clsclient_executor(
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input="./zh.wav",
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server_ip="127.0.0.1",
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port=8090,
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topk=1)
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print(res.json())
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```
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Output:
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```bash
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{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
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```
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## Models supported by the service
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### ASR model
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Get all models supported by the ASR service via `paddlespeech_server stats --task asr`, where static models can be used for paddle inference inference.
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### TTS model
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Get all models supported by the TTS service via `paddlespeech_server stats --task tts`, where static models can be used for paddle inference inference.
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### CLS model
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Get all models supported by the CLS service via `paddlespeech_server stats --task cls`, where static models can be used for paddle inference inference.
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