PPVPR.md

([简体中文](./PPVPR_cn.md)|English)
# PP-VPR

## Catalogue
- [1. Introduction](#1)
- [2. Characteristic](#2)
- [3. Tutorials](#3)
    - [3.1 Pre-trained Models](#31)
    - [3.2 Training](#32)
    - [3.3 Inference](#33)
    - [3.4 Service Deployment](#33)
- [4. Quick Start](#4)

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## 1. Introduction

PP-VPR is a tool that provides voice print feature extraction and retrieval functions.  Provides a variety of quasi-industrial solutions, easy to solve the difficult problems in complex scenes, support the use of command line model reasoning.  PP-VPR also supports interface operations and container deployment.  

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## 2. Characteristic
The basic process of VPR is shown in the figure below:  
<center><img src=https://ai-studio-static-online.cdn.bcebos.com/3aed59b8c8874046ad19fe583d15a8dd53c5b33e68db4383b79706e5add5c2d0 width="800" ></center>


The main characteristics of PP-ASR are shown below:
-  Provides pre-trained models on Chinese open source datasets: VoxCeleb(English). The models include ecapa-tdnn.
-  Support model training/evaluation.
-  Support model inference using the command line. You can use to use `paddlespeech vector --task spk --input xxx.wav` to use the pre-trained model to do model inference. 
-  Support interface operations and container deployment.

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## 3. Tutorials

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## 3.1 Pre-trained Models
The support pre-trained model list: [released_model](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/released_model.md).  
For more information about model design, you can refer to the aistudio tutorial:
- [ecapa-tdnn](https://aistudio.baidu.com/aistudio/projectdetail/4027664)

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## 3.2 Training
The referenced script for model training is stored in [examples](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples) and stored according to "examples/dataset/model". The dataset mainly supports VoxCeleb. The model supports ecapa-tdnn.
The specific steps of executing the script are recorded in `run.sh`.

For more information, you can refer to [sv0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0)


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## 3.3 Inference

PP-VPR supports use `paddlespeech vector --task spk --input xxx.wav` to use the pre-trained model to do inference after install `paddlespeech` by `pip install paddlespeech`.

Specific supported functions include:

- Prediction of single audio
- Score the similarity between the two audios
- Support RTF calculation

For specific usage, please refer to: [speaker_verification](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/demos/speaker_verification/README_cn.md) 


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## 3.4 Service Deployment

PP-VPR supports Docker containerized service deployment.  Through Milvus, MySQL performs high performance library building search.  

Demo of VPR Server: [audio_searching](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/audio_searching)

![arch](https://ai-studio-static-online.cdn.bcebos.com/7b32dd0200084866863095677e8b40d3b725b867d2e6439e9cf21514e235dfd5)

For more information about service deployment, you can refer to the aistudio tutorial:
- [speaker_recognition](https://aistudio.baidu.com/aistudio/projectdetail/4027664)

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## 4. Quick Start

To use PP-VPR, you can see here [install](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md), It supplies three methods to install `paddlespeech`, which are **Easy**, **Medium** and **Hard**. If you want to experience the inference function of paddlespeech, you can use **Easy** installation method.