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PaddleSpeech
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demos/streaming_asr_server/README.md
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@ -3,7 +3,7 @@
# 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.
This demo is an implementation of starting the streaming speech 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
@ -14,17 +14,16 @@ 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 `<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)
The configuration file can be found in `conf/ws_application.yaml` 和 `conf/ws_conformer_application.yaml`.
At present, the speech tasks integrated by the model include: DeepSpeech2 and conformer.
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
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
```
### 3. Server Usage
@ -32,7 +31,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
```bash
# start the service
paddlespeech_server start --config_file ./conf/application.yaml
paddlespeech_server start --config_file ./conf/ws_conformer_application.yaml
```
Usage:
@ -41,19 +40,72 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
paddlespeech_server start --help
```
Arguments:
- `config_file`: yaml file of the app, defalut: ./conf/application.yaml
- `config_file`: yaml file of the app, defalut: ./conf/ws_conformer_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]
[2022-04-21 15:52:18,126] [ INFO] - create the online asr engine instance
[2022-04-21 15:52:18,127] [ INFO] - paddlespeech_server set the device: cpu
[2022-04-21 15:52:18,128] [ INFO] - Load the pretrained model, tag = conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,128] [ INFO] - File /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz md5 checking...
[2022-04-21 15:52:18,727] [ INFO] - Use pretrained model stored in: /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/model.yaml
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/exp/chunk_conformer/checkpoints/multi_cn.pdparams
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/exp/chunk_conformer/checkpoints/multi_cn.pdparams
[2022-04-21 15:52:19,446] [ INFO] - start to create the stream conformer asr engine
[2022-04-21 15:52:19,473] [ INFO] - model name: conformer_online
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
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set kaiming_uniform
[2022-04-21 15:52:21,731] [ INFO] - create the transformer like model success
[2022-04-21 15:52:21,733] [ INFO] - Initialize ASR server engine successfully.
INFO: Started server process [11173]
[2022-04-21 15:52:21] [INFO] [server.py:75] Started server process [11173]
INFO: Waiting for application startup.
[2022-02-23 11:17:32] [INFO] [on.py:26] Waiting for application startup.
[2022-04-21 15:52:21] [INFO] [on.py:45] Waiting for application startup.
INFO: Application startup complete.
[2022-02-23 11:17:32] [INFO] [on.py:38] Application startup complete.
[2022-04-21 15:52:21] [INFO] [on.py:59] Application startup complete.
/home/users/xiongxinlei/.conda/envs/paddlespeech/lib/python3.9/asyncio/base_events.py:1460: DeprecationWarning: The loop argument is deprecated since Python 3.8, and scheduled for removal in Python 3.10.
infos = await tasks.gather(*fs, loop=self)
/home/users/xiongxinlei/.conda/envs/paddlespeech/lib/python3.9/asyncio/base_events.py:1518: DeprecationWarning: The loop argument is deprecated since Python 3.8, and scheduled for removal in Python 3.10.
await tasks.sleep(0, loop=self)
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)
[2022-04-21 15:52:21] [INFO] [server.py:206] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
```
- Python API
@ -62,21 +114,73 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
server_executor = ServerExecutor()
server_executor(
config_file="./conf/application.yaml",
config_file="./conf/ws_conformer_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]
[2022-04-21 15:52:18,126] [ INFO] - create the online asr engine instance
[2022-04-21 15:52:18,127] [ INFO] - paddlespeech_server set the device: cpu
[2022-04-21 15:52:18,128] [ INFO] - Load the pretrained model, tag = conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,128] [ INFO] - File /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/asr1_chunk_conformer_multi_cn_ckpt_0.2.3.model.tar.gz md5 checking...
[2022-04-21 15:52:18,727] [ INFO] - Use pretrained model stored in: /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/model.yaml
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/exp/chunk_conformer/checkpoints/multi_cn.pdparams
[2022-04-21 15:52:18,727] [ INFO] - /home/users/xiongxinlei/.paddlespeech/models/conformer_online_multicn-zh-16k/exp/chunk_conformer/checkpoints/multi_cn.pdparams
[2022-04-21 15:52:19,446] [ INFO] - start to create the stream conformer asr engine
[2022-04-21 15:52:19,473] [ INFO] - model name: conformer_online
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
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set kaiming_uniform
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set kaiming_uniform
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set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
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set kaiming_uniform
set kaiming_uniform
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set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
set kaiming_uniform
[2022-04-21 15:52:21,731] [ INFO] - create the transformer like model success
[2022-04-21 15:52:21,733] [ INFO] - Initialize ASR server engine successfully.
INFO: Started server process [11173]
[2022-04-21 15:52:21] [INFO] [server.py:75] Started server process [11173]
INFO: Waiting for application startup.
[2022-02-23 14:57:56] [INFO] [on.py:26] Waiting for application startup.
[2022-04-21 15:52:21] [INFO] [on.py:45] Waiting for application startup.
INFO: Application startup complete.
[2022-02-23 14:57:56] [INFO] [on.py:38] Application startup complete.
[2022-04-21 15:52:21] [INFO] [on.py:59] Application startup complete.
/home/users/xiongxinlei/.conda/envs/paddlespeech/lib/python3.9/asyncio/base_events.py:1460: DeprecationWarning: The loop argument is deprecated since Python 3.8, and scheduled for removal in Python 3.10.
infos = await tasks.gather(*fs, loop=self)
/home/users/xiongxinlei/.conda/envs/paddlespeech/lib/python3.9/asyncio/base_events.py:1518: DeprecationWarning: The loop argument is deprecated since Python 3.8, and scheduled for removal in Python 3.10.
await tasks.sleep(0, loop=self)
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)
[2022-04-21 15:52:21] [INFO] [server.py:206] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
```
@ -84,13 +188,13 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
**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
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
Usage:
```bash
paddlespeech_client asr --help
paddlespeech_client asr_online --help
```
Arguments:
- `server_ip`: server ip. Default: 127.0.0.1
@ -102,8 +206,69 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
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.
[2022-04-21 15:59:03,904] [ INFO] - receive msg={"status": "ok", "signal": "server_ready"}
[2022-04-21 15:59:03,960] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:03,973] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:03,987] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,000] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,012] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,024] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,036] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,047] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,607] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,620] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,633] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,645] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,657] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,669] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,680] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:05,176] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,185] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,192] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,200] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,208] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,216] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,224] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,232] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,724] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,732] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,740] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,747] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,755] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,763] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,770] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:06,271] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,279] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,287] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,294] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,302] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,310] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,318] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,326] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,833] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,842] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,850] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,858] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,866] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,874] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,882] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:07,400] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,408] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,416] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,424] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,432] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,440] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,447] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,455] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,984] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:07,992] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,001] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,008] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,016] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,024] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:12,883] [ INFO] - final receive msg={'status': 'ok', 'signal': 'finished', 'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:12,884] [ INFO] - 我认为跑步最重要的就是给我带来了身体健康
[2022-04-21 15:59:12,884] [ INFO] - Response time 9.051567 s.
```
@ -125,122 +290,66 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
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.
[2022-04-21 15:59:03,904] [ INFO] - receive msg={"status": "ok", "signal": "server_ready"}
[2022-04-21 15:59:03,960] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:03,973] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:03,987] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,000] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,012] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,024] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,036] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,047] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,607] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,620] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,633] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,645] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,657] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,669] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:04,680] [ INFO] - receive msg={'asr_results': ''}
[2022-04-21 15:59:05,176] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,185] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,192] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,200] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,208] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,216] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,224] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,232] [ INFO] - receive msg={'asr_results': '我认为跑'}
[2022-04-21 15:59:05,724] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,732] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,740] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,747] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,755] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,763] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:05,770] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的'}
[2022-04-21 15:59:06,271] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,279] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,287] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,294] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,302] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,310] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,318] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,326] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是'}
[2022-04-21 15:59:06,833] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,842] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,850] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,858] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,866] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,874] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:06,882] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给'}
[2022-04-21 15:59:07,400] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,408] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,416] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,424] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,432] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,440] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,447] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,455] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了'}
[2022-04-21 15:59:07,984] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:07,992] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,001] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,008] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,016] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:08,024] [ INFO] - receive msg={'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:12,883] [ INFO] - final receive msg={'status': 'ok', 'signal': 'finished', 'asr_results': '我认为跑步最重要的就是给我带来了身体健康'}
[2022-04-21 15:59:12,884] [ INFO] - 我认为跑步最重要的就是给我带来了身体健康
```
### 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.

2
paddlespeech/server/bin/paddlespeech_client.py
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@ -35,7 +35,7 @@ from paddlespeech.server.utils.audio_process import wav2pcm
from paddlespeech.server.utils.util import wav2base64
__all__ = [
'TTSClientExecutor', 'ASRClientExecutor', 'ASRClientExecutor',
'TTSClientExecutor', 'ASRClientExecutor', 'ASROnlineClientExecutor',
'CLSClientExecutor'
]

35
paddlespeech/server/tests/asr/online/README.md
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@ -0,0 +1,35 @@
([简体中文](./README_cn.md)|English)
# Speech Service
## Introduction
This document introduces a client for streaming asr service: microphone
## Usage
### 1. Install
Refer [Install](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
**paddlepaddle 2.2.1** 或以上版本。
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 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
```
### 2. Streaming ASR Client Usage
- microphone
```
python microphone_client.py
```

42
paddlespeech/server/tests/asr/online/README_cn.md
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@ -0,0 +1,42 @@
([English](./README.md)|中文)
# 语音服务
## 介绍
本文档介绍如何使用流式ASR的一种不同客户端:麦克风。
## 使用方法
### 1. 安装
请看 [安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
推荐使用 **paddlepaddle 2.2.1** 或以上版本。
你可以从 medium，hard 三中方式中选择一种方式安装 PaddleSpeech。
### 2. 准备测试文件
这个 ASR client 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
可以下载此 ASR client的示例音频：
```bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
```
### 2. 流式 ASR 客户端使用方法
- Python模拟流式服务命令行
```
# 流式ASR
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8091 --input ./zh.wav
```
- 麦克风
```
# 直接调用麦克风设备
python microphone_client.py
```

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paddlespeech/server/tests/asr/online/microphone_client.py
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@ -0,0 +1,161 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
record wave from the mic
"""
import asyncio
import json
import logging
import threading
import wave
from signal import SIGINT
from signal import SIGTERM
import pyaudio
import websockets
class ASRAudioHandler(threading.Thread):
def __init__(self, url="127.0.0.1", port=8091):
threading.Thread.__init__(self)
self.url = url
self.port = port
self.url = "ws://" + self.url + ":" + str(self.port) + "/ws/asr"
self.fileName = "./output.wav"
self.chunk = 5120
self.format = pyaudio.paInt16
self.channels = 1
self.rate = 16000
self._running = True
self._frames = []
self.data_backup = []
def startrecord(self):
"""
start a new thread to record wave
"""
threading._start_new_thread(self.recording, ())
def recording(self):
"""
recording wave
"""
self._running = True
self._frames = []
p = pyaudio.PyAudio()
stream = p.open(
format=self.format,
channels=self.channels,
rate=self.rate,
input=True,
frames_per_buffer=self.chunk)
while (self._running):
data = stream.read(self.chunk)
self._frames.append(data)
self.data_backup.append(data)
stream.stop_stream()
stream.close()
p.terminate()
def save(self):
"""
save wave data
"""
p = pyaudio.PyAudio()
wf = wave.open(self.fileName, 'wb')
wf.setnchannels(self.channels)
wf.setsampwidth(p.get_sample_size(self.format))
wf.setframerate(self.rate)
wf.writeframes(b''.join(self.data_backup))
wf.close()
p.terminate()
def stoprecord(self):
"""
stop recording
"""
self._running = False
async def run(self):
aa = input("是否开始录音？ (y/n)")
if aa.strip() == "y":
self.startrecord()
logging.info("*" * 10 + "开始录音，请输入语音")
async with websockets.connect(self.url) as ws:
# 发送开始指令
audio_info = json.dumps(
{
"name": "test.wav",
"signal": "start",
"nbest": 5
},
sort_keys=True,
indent=4,
separators=(',', ': '))
await ws.send(audio_info)
msg = await ws.recv()
logging.info("receive msg={}".format(msg))
# send bytes data
logging.info("结束录音请: Ctrl + c。继续请按回车。")
try:
while True:
while len(self._frames) > 0:
await ws.send(self._frames.pop(0))
msg = await ws.recv()
logging.info("receive msg={}".format(msg))
except asyncio.CancelledError:
# quit
# send finished
audio_info = json.dumps(
{
"name": "test.wav",
"signal": "end",
"nbest": 5
},
sort_keys=True,
indent=4,
separators=(',', ': '))
await ws.send(audio_info)
msg = await ws.recv()
logging.info("receive msg={}".format(msg))
self.stoprecord()
logging.info("*" * 10 + "录音结束")
self.save()
elif aa.strip() == "n":
exit()
else:
print("无效输入!")
exit()
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
logging.info("asr websocket client start")
handler = ASRAudioHandler("127.0.0.1", 8091)
loop = asyncio.get_event_loop()
main_task = asyncio.ensure_future(handler.run())
for signal in [SIGINT, SIGTERM]:
loop.add_signal_handler(signal, main_task.cancel)
try:
loop.run_until_complete(main_task)
finally:
loop.close()
logging.info("asr websocket client finished")
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