([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 2.2.2** 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 `<speech task>_<engine type>` .
At present, the speech tasks integrated by the service include: asr (speech recognition), tts (text to sppech) and cls (audio classification).
Currently the engine type supports two forms: python and inference (Paddle Inference)
**Note:** If the service can be started normally in the container, but the client access IP is unreachable, you can try to replace the `host` address in the configuration file with the local IP address.
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://127.0.0.1:8090 (Press CTRL+C to quit)
[2022-02-23 11:17:32] [INFO] [server.py:204] Uvicorn running on http://127.0.0.1:8090 (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://127.0.0.1:8090 (Press CTRL+C to quit)
[2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://127.0.0.1:8090 (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.
