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PaddleSpeech/demos/speech_server/README.md

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([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).
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:
```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
- 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
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:
```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'}}
time cost 0.604353 s.
```
### 5. TTS Client Usage
- 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: `output.wav`.
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.
[2022-02-23 15:20:37,875] [ INFO] - RTF: 0.096346
```
- Python API
```python
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:
```bash
{'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](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/demos/speech_recognition/README.md#4pretrained-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](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/demos/text_to_speech/README.md#4-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