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(简体中文|[English](./README.md))
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# 语音服务
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## 介绍
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这个demo是一个启动离线语音服务和访问服务的实现。它可以通过使用`paddlespeech_server` 和 `paddlespeech_client`的单个命令或 python 的几行代码来实现。
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## 使用方法
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### 1. 安装
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请看 [安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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推荐使用 **paddlepaddle 2.2.2** 或以上版本。
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你可以从 medium，hard 两种方式中选择一种方式安装 PaddleSpeech。
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### 2. 准备配置文件
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配置文件可参见 `conf/application.yaml` 。
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其中，`engine_list`表示即将启动的服务将会包含的语音引擎，格式为 <语音任务>_<引擎类型>。
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目前服务集成的语音任务有： asr(语音识别)、tts(语音合成)、cls(音频分类)、vector(声纹识别)以及text(文本处理)。
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目前引擎类型支持两种形式：python 及 inference (Paddle Inference)
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**注意：** 如果在容器里可正常启动服务，但客户端访问 ip 不可达，可尝试将配置文件中 `host` 地址换成本地 ip 地址。
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ASR client 的输入是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
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可以下载此 ASR client的示例音频：
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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. 服务端使用方法
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- 命令行 (推荐使用)
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```bash
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# 启动服务
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paddlespeech_server start --config_file ./conf/application.yaml
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```
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使用方法：
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```bash
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paddlespeech_server start --help
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```
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参数:
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- `config_file`: 服务的配置文件，默认： ./conf/application.yaml
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- `log_file`: log 文件. 默认：./log/paddlespeech.log
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输出:
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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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输出：
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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 客户端使用方法
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**注意：** 初次使用客户端时响应时间会略长
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- 命令行 (推荐使用)
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若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
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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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使用帮助:
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```bash
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paddlespeech_client asr --help
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```
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参数:
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- `server_ip`: 服务端ip地址，默认: 127.0.0.1。
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- `port`: 服务端口，默认: 8090。
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- `input`(必须输入): 用于识别的音频文件。
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- `sample_rate`: 音频采样率，默认值：16000。
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- `lang`: 模型语言，默认值：zh_cn。
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- `audio_format`: 音频格式，默认值：wav。
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输出:
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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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输出:
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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 客户端使用方法
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**注意：** 初次使用客户端时响应时间会略长
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- 命令行 (推荐使用)
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若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
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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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使用帮助:
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```bash
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paddlespeech_client tts --help
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```
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参数:
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- `server_ip`: 服务端ip地址，默认: 127.0.0.1。
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- `port`: 服务端口，默认: 8090。
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- `input`(必须输入): 待合成的文本。
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- `spk_id`: 说话人 id，用于多说话人语音合成，默认值： 0。
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- `speed`: 音频速度，该值应设置在 0 到 3 之间。 默认值：1.0
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- `volume`: 音频音量，该值应设置在 0 到 3 之间。 默认值： 1.0
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- `sample_rate`: 采样率，可选 [0, 8000, 16000]，默认与模型相同。 默认值：0
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- `output`: 输出音频的路径， 默认值：None，表示不保存音频到本地。
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输出:
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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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输出:
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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 客户端使用方法
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**注意：** 初次使用客户端时响应时间会略长
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- 命令行 (推荐使用)
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若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
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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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使用帮助:
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```bash
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paddlespeech_client cls --help
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```
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参数:
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- `server_ip`: 服务端ip地址，默认: 127.0.0.1。
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- `port`: 服务端口，默认: 8090。
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- `input`(必须输入): 用于分类的音频文件。
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- `topk`: 分类结果的topk。
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输出:
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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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输出:
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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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### 7. 声纹客户端使用方法
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#### 7.1 提取声纹特征
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注意： 初次使用客户端时响应时间会略长
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* 命令行 (推荐使用)
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若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
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``` bash
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paddlespeech_client vector --task spk --server_ip 127.0.0.1 --port 8090 --input 85236145389.wav
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```
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* 使用帮助:
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``` bash
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paddlespeech_client vector --help
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```
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* 参数:
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* server_ip: 服务端ip地址，默认: 127.0.0.1。
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* port: 服务端口，默认: 8090。
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* input(必须输入): 用于识别的音频文件。
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* task: vector 的任务，可选spk或者score。默认是 spk。
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* enroll: 注册音频；。
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* test: 测试音频。
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* 输出:
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``` bash
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[2022-05-08 00:18:44,249] [ INFO] - vector http client start
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[2022-05-08 00:18:44,250] [ INFO] - the input audio: 85236145389.wav
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[2022-05-08 00:18:44,250] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector
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[2022-05-08 00:18:44,250] [ INFO] - http://127.0.0.1:8590/paddlespeech/vector
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[2022-05-08 00:18:44,406] [ INFO] - The vector: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.421751856803894, 5.626245498657227, -5.342077255249023, 1.1773887872695923, 3.3080549240112305, 1.7565933465957642, 5.167886257171631, 10.806358337402344, -3.8226819038391113, -5.614140033721924, 2.6238479614257812, -0.8072972893714905, 1.9635076522827148, -7.312870025634766, 0.011035939678549767, -9.723129272460938, 0.6619706153869629, -6.976806163787842, 10.213476181030273, 7.494769096374512, 2.9105682373046875, 3.8949244022369385, 3.799983501434326, 7.106168746948242, 16.90532875061035, -7.149388313293457, 8.733108520507812, 3.423006296157837, -4.831653594970703, -11.403363227844238, 11.232224464416504, 7.127461910247803, -4.282842636108398, 2.452359437942505, -5.130749702453613, -18.17766761779785, -2.6116831302642822, -11.000344276428223, -6.731433391571045, 1.6564682722091675, 0.7618281245231628, 1.125300407409668, -2.0838370323181152, 4.725743293762207, -8.782588005065918, -3.5398752689361572, 3.8142364025115967, 5.142068862915039, 2.1620609760284424, 4.09643030166626, -6.416214942932129, 12.747446060180664, 1.9429892301559448, -15.15294361114502, 6.417416095733643, 16.09701156616211, -9.716667175292969, -1.9920575618743896, -3.36494779586792, -1.8719440698623657, 11.567351341247559, 3.6978814601898193, 11.258262634277344, 7.442368507385254, 9.183408737182617, 4.528149127960205, -1.2417854070663452, 4.395912170410156, 6.6727728843688965, 5.88988733291626, 7.627128601074219, -0.6691966652870178, -11.889698028564453, -9.20886516571045, -7.42740535736084, -3.777663230895996, 6.917238712310791, -9.848755836486816, -2.0944676399230957, -5.1351165771484375, 0.4956451654434204, 9.317537307739258, -5.914181232452393, -1.809860348701477, -0.11738915741443634, -7.1692705154418945, -1.057827353477478, -5.721670627593994, -5.117385387420654, 16.13765525817871, -4.473617076873779, 7.6624321937561035, -0.55381840467453, 9.631585121154785, -6.470459461212158, -8.548508644104004, 4.371616840362549, -0.7970245480537415, 4.4789886474609375, -2.975860834121704, 3.2721822261810303, 2.838287830352783, 5.134591102600098, -9.19079875946045, -0.5657302737236023, -4.8745832443237305, 2.3165574073791504, -5.984319686889648, -2.1798853874206543, 0.3554139733314514, -0.3178512752056122, 9.493552207946777, 2.1144471168518066, 4.358094692230225, -12.089824676513672, 8.451693534851074, -7.925466537475586, 4.624246597290039, 4.428936958312988, 18.69200897216797, -2.6204581260681152, -5.14918851852417, -0.3582090139389038, 8.488558769226074, 4.98148775100708, -9.326835632324219, -2.2544219493865967, 6.641760349273682, 1.2119598388671875, 10.977124214172363, 16.555034637451172, 3.3238420486450195, 9.551861763000488, -1.6676981449127197, -0.7953944206237793, -8.605667114257812, -0.4735655188560486, 2.674196243286133, -5.359177112579346, -2.66738224029541, 0.6660683155059814, 15.44322681427002, 4.740593433380127, -3.472534418106079, 11.592567443847656, -2.0544962882995605, 1.736127495765686, -8.265326499938965, -9.30447769165039, 5.406829833984375, -1.518022894859314, -7.746612548828125, -6.089611053466797, 0.07112743705511093, -0.3490503430366516, -8.64989185333252, -9.998957633972168, -2.564845085144043, -0.5399947762489319, 2.6018123626708984, -0.3192799389362335, -1.8815255165100098, -2.0721492767333984, -3.410574436187744, -8.29980754852295, 1.483638048171997, -15.365986824035645, -8.288211822509766, 3.884779930114746, -3.4876468181610107, 7.362999439239502, 0.4657334089279175, 3.1326050758361816, 12.438895225524902, -1.8337041139602661, 4.532927989959717, 2.7264339923858643, 10.14534854888916, -6.521963596343994, 2.897155523300171, -3.392582654953003, 5.079153060913086, 7.7597246170043945, 4.677570819854736, 5.845779895782471, 2.402411460876465, 7.7071051597595215, 3.9711380004882812, -6.39003849029541, 6.12687873840332, -3.776029348373413, -11.118121147155762]}}
|
|
|
[2022-05-08 00:18:44,406] [ INFO] - Response time 0.156481 s.
|
|
|
```
|
|
|
|
|
|
* Python API
|
|
|
|
|
|
``` python
|
|
|
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
|
|
|
|
|
|
vectorclient_executor = VectorClientExecutor()
|
|
|
res = vectorclient_executor(
|
|
|
input="85236145389.wav",
|
|
|
server_ip="127.0.0.1",
|
|
|
port=8090,
|
|
|
task="spk")
|
|
|
print(res)
|
|
|
```
|
|
|
|
|
|
* 输出:
|
|
|
|
|
|
``` bash
|
|
|
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.421751856803894, 5.626245498657227, -5.342077255249023, 1.1773887872695923, 3.3080549240112305, 1.7565933465957642, 5.167886257171631, 10.806358337402344, -3.8226819038391113, -5.614140033721924, 2.6238479614257812, -0.8072972893714905, 1.9635076522827148, -7.312870025634766, 0.011035939678549767, -9.723129272460938, 0.6619706153869629, -6.976806163787842, 10.213476181030273, 7.494769096374512, 2.9105682373046875, 3.8949244022369385, 3.799983501434326, 7.106168746948242, 16.90532875061035, -7.149388313293457, 8.733108520507812, 3.423006296157837, -4.831653594970703, -11.403363227844238, 11.232224464416504, 7.127461910247803, -4.282842636108398, 2.452359437942505, -5.130749702453613, -18.17766761779785, -2.6116831302642822, -11.000344276428223, -6.731433391571045, 1.6564682722091675, 0.7618281245231628, 1.125300407409668, -2.0838370323181152, 4.725743293762207, -8.782588005065918, -3.5398752689361572, 3.8142364025115967, 5.142068862915039, 2.1620609760284424, 4.09643030166626, -6.416214942932129, 12.747446060180664, 1.9429892301559448, -15.15294361114502, 6.417416095733643, 16.09701156616211, -9.716667175292969, -1.9920575618743896, -3.36494779586792, -1.8719440698623657, 11.567351341247559, 3.6978814601898193, 11.258262634277344, 7.442368507385254, 9.183408737182617, 4.528149127960205, -1.2417854070663452, 4.395912170410156, 6.6727728843688965, 5.88988733291626, 7.627128601074219, -0.6691966652870178, -11.889698028564453, -9.20886516571045, -7.42740535736084, -3.777663230895996, 6.917238712310791, -9.848755836486816, -2.0944676399230957, -5.1351165771484375, 0.4956451654434204, 9.317537307739258, -5.914181232452393, -1.809860348701477, -0.11738915741443634, -7.1692705154418945, -1.057827353477478, -5.721670627593994, -5.117385387420654, 16.13765525817871, -4.473617076873779, 7.6624321937561035, -0.55381840467453, 9.631585121154785, -6.470459461212158, -8.548508644104004, 4.371616840362549, -0.7970245480537415, 4.4789886474609375, -2.975860834121704, 3.2721822261810303, 2.838287830352783, 5.134591102600098, -9.19079875946045, -0.5657302737236023, -4.8745832443237305, 2.3165574073791504, -5.984319686889648, -2.1798853874206543, 0.3554139733314514, -0.3178512752056122, 9.493552207946777, 2.1144471168518066, 4.358094692230225, -12.089824676513672, 8.451693534851074, -7.925466537475586, 4.624246597290039, 4.428936958312988, 18.69200897216797, -2.6204581260681152, -5.14918851852417, -0.3582090139389038, 8.488558769226074, 4.98148775100708, -9.326835632324219, -2.2544219493865967, 6.641760349273682, 1.2119598388671875, 10.977124214172363, 16.555034637451172, 3.3238420486450195, 9.551861763000488, -1.6676981449127197, -0.7953944206237793, -8.605667114257812, -0.4735655188560486, 2.674196243286133, -5.359177112579346, -2.66738224029541, 0.6660683155059814, 15.44322681427002, 4.740593433380127, -3.472534418106079, 11.592567443847656, -2.0544962882995605, 1.736127495765686, -8.265326499938965, -9.30447769165039, 5.406829833984375, -1.518022894859314, -7.746612548828125, -6.089611053466797, 0.07112743705511093, -0.3490503430366516, -8.64989185333252, -9.998957633972168, -2.564845085144043, -0.5399947762489319, 2.6018123626708984, -0.3192799389362335, -1.8815255165100098, -2.0721492767333984, -3.410574436187744, -8.29980754852295, 1.483638048171997, -15.365986824035645, -8.288211822509766, 3.884779930114746, -3.4876468181610107, 7.362999439239502, 0.4657334089279175, 3.1326050758361816, 12.438895225524902, -1.8337041139602661, 4.532927989959717, 2.7264339923858643, 10.14534854888916, -6.521963596343994, 2.897155523300171, -3.392582654953003, 5.079153060913086, 7.7597246170043945, 4.677570819854736, 5.845779895782471, 2.402411460876465, 7.7071051597595215, 3.9711380004882812, -6.39003849029541, 6.12687873840332, -3.776029348373413, -11.118121147155762]}}
|
|
|
```
|
|
|
|
|
|
#### 7.2 音频声纹打分
|
|
|
|
|
|
注意： 初次使用客户端时响应时间会略长
|
|
|
* 命令行 (推荐使用)
|
|
|
|
|
|
若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
|
|
|
|
|
|
``` bash
|
|
|
paddlespeech_client vector --task score --server_ip 127.0.0.1 --port 8090 --enroll 85236145389.wav --test 123456789.wav
|
|
|
```
|
|
|
|
|
|
* 使用帮助:
|
|
|
|
|
|
``` bash
|
|
|
paddlespeech_client vector --help
|
|
|
```
|
|
|
|
|
|
* 参数:
|
|
|
* server_ip: 服务端ip地址，默认: 127.0.0.1。
|
|
|
* port: 服务端口，默认: 8090。
|
|
|
* input(必须输入): 用于识别的音频文件。
|
|
|
* task: vector 的任务，可选spk或者score。默认是 spk。
|
|
|
* enroll: 注册音频；。
|
|
|
* test: 测试音频。
|
|
|
* 输出:
|
|
|
|
|
|
``` bash
|
|
|
[2022-05-09 10:28:40,556] [ INFO] - vector score http client start
|
|
|
[2022-05-09 10:28:40,556] [ INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
|
|
|
[2022-05-09 10:28:40,556] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector/score
|
|
|
[2022-05-09 10:28:40,731] [ INFO] - The vector score is: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
|
|
|
[2022-05-09 10:28:40,731] [ INFO] - The vector: None
|
|
|
[2022-05-09 10:28:40,731] [ INFO] - Response time 0.175514 s.
|
|
|
```
|
|
|
|
|
|
* Python API
|
|
|
|
|
|
``` python
|
|
|
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
|
|
|
|
|
|
vectorclient_executor = VectorClientExecutor()
|
|
|
res = vectorclient_executor(
|
|
|
input=None,
|
|
|
enroll_audio="85236145389.wav",
|
|
|
test_audio="123456789.wav",
|
|
|
server_ip="127.0.0.1",
|
|
|
port=8090,
|
|
|
task="score")
|
|
|
print(res)
|
|
|
```
|
|
|
|
|
|
* 输出:
|
|
|
|
|
|
``` bash
|
|
|
[2022-05-09 10:34:54,769] [ INFO] - vector score http client start
|
|
|
[2022-05-09 10:34:54,771] [ INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
|
|
|
[2022-05-09 10:34:54,771] [ INFO] - endpoint: http://127.0.0.1:8590/paddlespeech/vector/score
|
|
|
[2022-05-09 10:34:55,026] [ INFO] - The vector score is: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
|
|
|
```
|
|
|
|
|
|
|
|
|
### 8. 标点预测
|
|
|
|
|
|
**注意：** 初次使用客户端时响应时间会略长
|
|
|
- 命令行 (推荐使用)
|
|
|
|
|
|
若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址
|
|
|
|
|
|
``` bash
|
|
|
paddlespeech_client text --server_ip 127.0.0.1 --port 8090 --input "我认为跑步最重要的就是给我带来了身体健康"
|
|
|
```
|
|
|
|
|
|
使用帮助:
|
|
|
|
|
|
```bash
|
|
|
paddlespeech_client text --help
|
|
|
```
|
|
|
参数:
|
|
|
- `server_ip`: 服务端ip地址，默认: 127.0.0.1。
|
|
|
- `port`: 服务端口，默认: 8090。
|
|
|
- `input`(必须输入): 用于标点预测的文本内容。
|
|
|
|
|
|
输出:
|
|
|
```bash
|
|
|
[2022-05-09 18:19:04,397] [ INFO] - The punc text: 我认为跑步最重要的就是给我带来了身体健康。
|
|
|
[2022-05-09 18:19:04,397] [ INFO] - Response time 0.092407 s.
|
|
|
```
|
|
|
|
|
|
- Python API
|
|
|
```python
|
|
|
from paddlespeech.server.bin.paddlespeech_client import TextClientExecutor
|
|
|
|
|
|
textclient_executor = TextClientExecutor()
|
|
|
res = textclient_executor(
|
|
|
input="我认为跑步最重要的就是给我带来了身体健康",
|
|
|
server_ip="127.0.0.1",
|
|
|
port=8090,)
|
|
|
print(res)
|
|
|
|
|
|
```
|
|
|
|
|
|
输出:
|
|
|
```bash
|
|
|
我认为跑步最重要的就是给我带来了身体健康。
|
|
|
```
|
|
|
|
|
|
## 服务支持的模型
|
|
|
### ASR支持的模型
|
|
|
通过 `paddlespeech_server stats --task asr` 获取ASR服务支持的所有模型，其中静态模型可用于 paddle inference 推理。
|
|
|
|
|
|
### TTS支持的模型
|
|
|
通过 `paddlespeech_server stats --task tts` 获取TTS服务支持的所有模型，其中静态模型可用于 paddle inference 推理。
|
|
|
|
|
|
### CLS支持的模型
|
|
|
通过 `paddlespeech_server stats --task cls` 获取CLS服务支持的所有模型，其中静态模型可用于 paddle inference 推理。
|
|
|
|
|
|
### Vector支持的模型
|
|
|
通过 `paddlespeech_server stats --task vector` 获取Vector服务支持的所有模型。
|
|
|
|
|
|
### Text支持的模型
|
|
|
通过 `paddlespeech_server stats --task text` 获取Text服务支持的所有模型。
|
