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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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服务接口定义请参考:
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- [PaddleSpeech Server RESTful API](https://github.com/PaddlePaddle/PaddleSpeech/wiki/PaddleSpeech-Server-RESTful-API)
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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 2.4rc** 或以上版本。
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你可以从简单，中等，困难 几种方式中选择一种方式安装 PaddleSpeech。
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**如果使用简单模式安装，需要自行准备 yaml 文件，可参考 conf 目录下的 yaml 文件。**
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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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### 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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> **注意：** 中英文混合语音识别请使用 `./conf/conformer_talcs_application.yaml` 配置文件
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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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```text
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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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```text
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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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ASR 客户端的输入是一个 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 ASR 客户端的示例音频：
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/ch_zh_mix.wav
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```
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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 asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
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# 中英文混合语音识别 , 请使用 `./conf/conformer_talcs_application.yaml` 配置文件
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paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./ch_zh_mix.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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```text
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[2022-08-01 07:54:01,646] [ INFO] - ASR result: 我认为跑步最重要的就是给我带来了身体健康
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[2022-08-01 07:54:01,646] [ INFO] - Response time 4.898965 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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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)
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```
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输出:
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```text
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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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```text
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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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```text
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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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可以下载 CLS 客户端的示例音频：
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
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```
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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 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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```text
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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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```text
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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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可以下载声纹客户端的示例音频：
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav
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```
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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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```text
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[2022-08-01 09:01:22,151] [ INFO] - vector http client start
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[2022-08-01 09:01:22,152] [ INFO] - the input audio: 85236145389.wav
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[2022-08-01 09:01:22,152] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector
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[2022-08-01 09:01:27,093] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.4217487573623657, 5.626248836517334, -5.342073440551758, 1.177390217781067, 3.308061122894287, 1.7565997838974, 5.1678876876831055, 10.806346893310547, -3.822679042816162, -5.614130973815918, 2.6238481998443604, -0.8072965741157532, 1.963512659072876, -7.312864780426025, 0.011034967377781868, -9.723127365112305, 0.661963164806366, -6.976816654205322, 10.213465690612793, 7.494767189025879, 2.9105641841888428, 3.894925117492676, 3.7999846935272217, 7.106173992156982, 16.905324935913086, -7.149376392364502, 8.733112335205078, 3.423002004623413, -4.831653118133545, -11.403371810913086, 11.232216835021973, 7.127464771270752, -4.282831192016602, 2.4523589611053467, -5.13075065612793, -18.17765998840332, -2.611666440963745, -11.00034236907959, -6.731431007385254, 1.6564655303955078, 0.7618184685707092, 1.1253058910369873, -2.0838277339935303, 4.725739002227783, -8.782590866088867, -3.5398736000061035, 3.8142387866973877, 5.142062664031982, 2.162053346633911, 4.09642219543457, -6.416221618652344, 12.747454643249512, 1.9429889917373657, -15.152948379516602, 6.417416572570801, 16.097013473510742, -9.716649055480957, -1.9920448064804077, -3.364956855773926, -1.8719490766525269, 11.567351341247559, 3.6978795528411865, 11.258269309997559, 7.442364692687988, 9.183405876159668, 4.528151512145996, -1.2417811155319214, 4.395910263061523, 6.672768592834473, 5.889888763427734, 7.627115249633789, -0.6692016124725342, -11.889703750610352, -9.208883285522461, -7.427401542663574, -3.777655601501465, 6.917237758636475, -9.848749160766602, -2.094479560852051, -5.1351189613342285, 0.49564215540885925, 9.317541122436523, -5.9141845703125, -1.809845209121704, -0.11738205701112747, -7.169270992279053, -1.0578246116638184, -5.721685886383057, -5.117387294769287, 16.137670516967773, -4.473618984222412, 7.66243314743042, -0.5538089871406555, 9.631582260131836, -6.470466613769531, -8.54850959777832, 4.371622085571289, -0.7970349192619324, 4.479003429412842, -2.9758646488189697, 3.2721707820892334, 2.8382749557495117, 5.1345953941345215, -9.19078254699707, -0.5657423138618469, -4.874573230743408, 2.316561460494995, -5.984307289123535, -2.1798791885375977, 0.35541653633117676, -0.3178458511829376, 9.493547439575195, 2.114448070526123, 4.358088493347168, -12.089820861816406, 8.451695442199707, -7.925461769104004, 4.624246120452881, 4.428938388824463, 18.691999435424805, -2.620460033416748, -5.149182319641113, -0.3582168221473694, 8.488557815551758, 4.98148250579834, -9.326834678649902, -2.2544236183166504, 6.64176607131958, 1.2119656801223755, 10.977132797241211, 16.55504035949707, 3.323848247528076, 9.55185317993164, -1.6677050590515137, -0.7953923940658569, -8.605660438537598, -0.4735637903213501, 2.6741855144500732, -5.359188079833984, -2.6673784255981445, 0.6660736799240112, 15.443212509155273, 4.740597724914551, -3.4725306034088135, 11.592561721801758, -2.05450701713562, 1.7361239194869995, -8.26533031463623, -9.304476737976074, 5.406835079193115, -1.5180232524871826, -7.746610641479492, -6.089605331420898, 0.07112561166286469, -0.34904858469963074, -8.649889945983887, -9.998958587646484, -2.5648481845855713, -0.5399898886680603, 2.6018145084381104, -0.31927648186683655, -1.8815231323242188, -2.0721378326416016, -3.4105639457702637, -8.299802780151367, 1.4836379289627075, -15.366002082824707, -8.288193702697754, 3.884773015975952, -3.4876506328582764, 7.362995624542236, 0.4657321572303772, 3.1326000690460205, 12.438883781433105, -1.8337029218673706, 4.532927513122559, 2.726433277130127, 10.145345687866211, -6.521956920623779, 2.8971481323242188, -3.3925881385803223, 5.079156398773193, 7.759725093841553, 4.677562236785889, 5.8457818031311035, 2.4023921489715576, 7.707108974456787, 3.9711389541625977, -6.390035152435303, 6.126871109008789, -3.776031017303467, -11.118141174316406]}}
|
|
|
[2022-08-01 09:01:27,094] [ INFO] - Response time 4.941739 s.
|
|
|
```
|
|
|
|
|
|
* Python API
|
|
|
|
|
|
``` python
|
|
|
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
|
|
|
import json
|
|
|
|
|
|
vectorclient_executor = VectorClientExecutor()
|
|
|
res = vectorclient_executor(
|
|
|
input="85236145389.wav",
|
|
|
server_ip="127.0.0.1",
|
|
|
port=8090,
|
|
|
task="spk")
|
|
|
print(res.json())
|
|
|
```
|
|
|
|
|
|
输出:
|
|
|
```text
|
|
|
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.4217487573623657, 5.626248836517334, -5.342073440551758, 1.177390217781067, 3.308061122894287, 1.7565997838974, 5.1678876876831055, 10.806346893310547, -3.822679042816162, -5.614130973815918, 2.6238481998443604, -0.8072965741157532, 1.963512659072876, -7.312864780426025, 0.011034967377781868, -9.723127365112305, 0.661963164806366, -6.976816654205322, 10.213465690612793, 7.494767189025879, 2.9105641841888428, 3.894925117492676, 3.7999846935272217, 7.106173992156982, 16.905324935913086, -7.149376392364502, 8.733112335205078, 3.423002004623413, -4.831653118133545, -11.403371810913086, 11.232216835021973, 7.127464771270752, -4.282831192016602, 2.4523589611053467, -5.13075065612793, -18.17765998840332, -2.611666440963745, -11.00034236907959, -6.731431007385254, 1.6564655303955078, 0.7618184685707092, 1.1253058910369873, -2.0838277339935303, 4.725739002227783, -8.782590866088867, -3.5398736000061035, 3.8142387866973877, 5.142062664031982, 2.162053346633911, 4.09642219543457, -6.416221618652344, 12.747454643249512, 1.9429889917373657, -15.152948379516602, 6.417416572570801, 16.097013473510742, -9.716649055480957, -1.9920448064804077, -3.364956855773926, -1.8719490766525269, 11.567351341247559, 3.6978795528411865, 11.258269309997559, 7.442364692687988, 9.183405876159668, 4.528151512145996, -1.2417811155319214, 4.395910263061523, 6.672768592834473, 5.889888763427734, 7.627115249633789, -0.6692016124725342, -11.889703750610352, -9.208883285522461, -7.427401542663574, -3.777655601501465, 6.917237758636475, -9.848749160766602, -2.094479560852051, -5.1351189613342285, 0.49564215540885925, 9.317541122436523, -5.9141845703125, -1.809845209121704, -0.11738205701112747, -7.169270992279053, -1.0578246116638184, -5.721685886383057, -5.117387294769287, 16.137670516967773, -4.473618984222412, 7.66243314743042, -0.5538089871406555, 9.631582260131836, -6.470466613769531, -8.54850959777832, 4.371622085571289, -0.7970349192619324, 4.479003429412842, -2.9758646488189697, 3.2721707820892334, 2.8382749557495117, 5.1345953941345215, -9.19078254699707, -0.5657423138618469, -4.874573230743408, 2.316561460494995, -5.984307289123535, -2.1798791885375977, 0.35541653633117676, -0.3178458511829376, 9.493547439575195, 2.114448070526123, 4.358088493347168, -12.089820861816406, 8.451695442199707, -7.925461769104004, 4.624246120452881, 4.428938388824463, 18.691999435424805, -2.620460033416748, -5.149182319641113, -0.3582168221473694, 8.488557815551758, 4.98148250579834, -9.326834678649902, -2.2544236183166504, 6.64176607131958, 1.2119656801223755, 10.977132797241211, 16.55504035949707, 3.323848247528076, 9.55185317993164, -1.6677050590515137, -0.7953923940658569, -8.605660438537598, -0.4735637903213501, 2.6741855144500732, -5.359188079833984, -2.6673784255981445, 0.6660736799240112, 15.443212509155273, 4.740597724914551, -3.4725306034088135, 11.592561721801758, -2.05450701713562, 1.7361239194869995, -8.26533031463623, -9.304476737976074, 5.406835079193115, -1.5180232524871826, -7.746610641479492, -6.089605331420898, 0.07112561166286469, -0.34904858469963074, -8.649889945983887, -9.998958587646484, -2.5648481845855713, -0.5399898886680603, 2.6018145084381104, -0.31927648186683655, -1.8815231323242188, -2.0721378326416016, -3.4105639457702637, -8.299802780151367, 1.4836379289627075, -15.366002082824707, -8.288193702697754, 3.884773015975952, -3.4876506328582764, 7.362995624542236, 0.4657321572303772, 3.1326000690460205, 12.438883781433105, -1.8337029218673706, 4.532927513122559, 2.726433277130127, 10.145345687866211, -6.521956920623779, 2.8971481323242188, -3.3925881385803223, 5.079156398773193, 7.759725093841553, 4.677562236785889, 5.8457818031311035, 2.4023921489715576, 7.707108974456787, 3.9711389541625977, -6.390035152435303, 6.126871109008789, -3.776031017303467, -11.118141174316406]}}
|
|
|
```
|
|
|
|
|
|
#### 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:
|
|
|
```text
|
|
|
[2022-08-01 09:04:42,275] [ INFO] - vector score http client start
|
|
|
[2022-08-01 09:04:42,275] [ INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
|
|
|
[2022-08-01 09:04:42,275] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector/score
|
|
|
[2022-08-01 09:04:44,611] [ INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
|
|
|
[2022-08-01 09:04:44,611] [ INFO] - Response time 2.336258 s.
|
|
|
```
|
|
|
|
|
|
* Python API
|
|
|
|
|
|
```python
|
|
|
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
|
|
|
import json
|
|
|
|
|
|
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.json())
|
|
|
```
|
|
|
|
|
|
输出:
|
|
|
```text
|
|
|
{'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:
|
|
|
```text
|
|
|
[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:
|
|
|
```text
|
|
|
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。
|
