WilliamZhang06
3 years ago
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GitHub
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a6ec3a26f1
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([简体中文](./README_cn.md)|English)
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# Speech Server
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## Introduction
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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.
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## Usage
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### 1. Installation
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see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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You can choose one way from easy, meduim and hard to install paddlespeech.
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### 2. Prepare config File
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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.
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The input of ASR client demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
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Here are sample files for thisASR client demo that can be downloaded:
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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. Server Usage
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- Command Line (Recommended)
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```bash
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# start the service
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paddlespeech_server start --config_file ./conf/application.yaml
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```
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Usage:
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```bash
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paddlespeech_server start --help
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```
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Arguments:
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- `config_file`: yaml file of the app, defalut: ./conf/application.yaml
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- `log_file`: log file. Default: ./log/paddlespeech.log
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Output:
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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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Output:
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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 Client Usage
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- Command Line (Recommended)
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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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Usage:
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```bash
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paddlespeech_client asr --help
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```
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Arguments:
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- `server_ip`: server ip. Default: 127.0.0.1
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- `port`: server port. Default: 8090
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- `input`(required): Audio file to be recognized.
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- `sample_rate`: Audio ampling rate, default: 16000.
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- `lang`: Language. Default: "zh_cn".
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- `audio_format`: Audio format. Default: "wav".
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Output:
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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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asrclient_executor = ASRClientExecutor()
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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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```
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Output:
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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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time cost 0.604353 s.
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```
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### 5. TTS Client Usage
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- Command Line (Recommended)
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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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Usage:
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```bash
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paddlespeech_client tts --help
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```
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Arguments:
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- `server_ip`: server ip. Default: 127.0.0.1
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- `port`: server port. Default: 8090
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- `input`(required): Input text to generate.
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- `spk_id`: Speaker id for multi-speaker text to speech. Default: 0
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- `speed`: Audio speed, the value should be set between 0 and 3. Default: 1.0
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- `volume`: Audio volume, the value should be set between 0 and 3. Default: 1.0
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- `sample_rate`: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0
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- `output`: Output wave filepath. Default: `output.wav`.
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Output:
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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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[2022-02-23 15:20:37,875] [ INFO] - RTF: 0.096346
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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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ttsclient_executor = TTSClientExecutor()
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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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```
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Output:
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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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Response time: 0.388317 s.
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RTF: 0.107493
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```
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## Pretrained Models
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### ASR model
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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:
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| Model | Language | Sample Rate
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| :--- | :---: | :---: |
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| conformer_wenetspeech| zh| 16000
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| transformer_librispeech| en| 16000
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### TTS model
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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:
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- Acoustic model
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| Model | Language
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| :--- | :---: |
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| speedyspeech_csmsc| zh
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| fastspeech2_csmsc| zh
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| fastspeech2_aishell3| zh
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| fastspeech2_ljspeech| en
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| fastspeech2_vctk| en
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- Vocoder
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| Model | Language
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| :--- | :---: |
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| pwgan_csmsc| zh
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| pwgan_aishell3| zh
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| pwgan_ljspeech| en
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| pwgan_vctk| en
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| mb_melgan_csmsc| zh
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Here is a list of **TTS pretrained static models** released by PaddleSpeech, both command line and python interfaces are available:
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- Acoustic model
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| Model | Language
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| speedyspeech_csmsc| zh
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| fastspeech2_csmsc| zh
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- Vocoder
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| Model | Language
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| :--- | :---: |
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| pwgan_csmsc| zh
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| mb_melgan_csmsc| zh
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| hifigan_csmsc| zh
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#!/bin/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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paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
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# This is the parameter configuration file for PaddleSpeech Serving.
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##################################################################
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# SERVER SETTING #
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##################################################################
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host: '0.0.0.0'
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port: 8090
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##################################################################
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# CONFIG FILE #
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##################################################################
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# add engine type (Options: asr, tts) and config file here.
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engine_backend:
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asr: 'conf/asr/asr.yaml'
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tts: 'conf/tts/tts.yaml'
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model: 'conformer_wenetspeech'
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lang: 'zh'
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sample_rate: 16000
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cfg_path:
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ckpt_path:
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decode_method: 'attention_rescoring'
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force_yes: False
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# This is the parameter configuration file for TTS server.
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##################################################################
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# ACOUSTIC MODEL SETTING #
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# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc',
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# 'fastspeech2_ljspeech', 'fastspeech2_aishell3',
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# 'fastspeech2_vctk']
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##################################################################
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am: 'fastspeech2_csmsc'
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am_config:
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am_ckpt:
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am_stat:
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phones_dict:
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tones_dict:
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speaker_dict:
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spk_id: 0
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##################################################################
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# VOCODER SETTING #
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# voc choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3',
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# 'pwgan_vctk', 'mb_melgan_csmsc']
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##################################################################
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voc: 'pwgan_csmsc'
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voc_config:
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voc_ckpt:
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voc_stat:
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##################################################################
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# OTHERS #
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##################################################################
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lang: 'zh'
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device: 'gpu:2'
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# This is the parameter configuration file for TTS server.
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# These are the static models that support paddle inference.
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##################################################################
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# ACOUSTIC MODEL SETTING #
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# am choices=['speedyspeech_csmsc', 'fastspeech2_csmsc']
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##################################################################
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am: 'fastspeech2_csmsc'
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am_model: # the pdmodel file of am static model
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am_params: # the pdiparams file of am static model
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am_sample_rate: 24000
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phones_dict:
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tones_dict:
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speaker_dict:
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spk_id: 0
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am_predictor_conf:
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use_gpu: True
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enable_mkldnn: True
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switch_ir_optim: True
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##################################################################
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# VOCODER SETTING #
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# voc choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc']
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##################################################################
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voc: 'pwgan_csmsc'
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voc_model: # the pdmodel file of vocoder static model
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voc_params: # the pdiparams file of vocoder static model
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voc_sample_rate: 24000
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voc_predictor_conf:
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use_gpu: True
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enable_mkldnn: True
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switch_ir_optim: True
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##################################################################
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# OTHERS #
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##################################################################
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lang: 'zh'
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device: paddle.get_device()
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#!/bin/bash
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paddlespeech_server start --config_file ./conf/application.yaml
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#!/bin/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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Reference in new issue