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([简体中文](./PPVPR_cn.md)|English)
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# PP-VPR
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## Catalogue
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- [1. Introduction](#1)
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- [2. Characteristic](#2)
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- [3. Tutorials](#3)
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- [3.1 Pre-trained Models](#31)
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- [3.2 Training](#32)
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- [3.3 Inference](#33)
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- [3.4 Service Deployment](#33)
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- [4. Quick Start](#4)
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<a name="1"></a>
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## 1. Introduction
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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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<a name="2"></a>
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## 2. Characteristic
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The basic process of VPR is shown in the figure below:
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<center><img src=https://ai-studio-static-online.cdn.bcebos.com/3aed59b8c8874046ad19fe583d15a8dd53c5b33e68db4383b79706e5add5c2d0 width="800" ></center>
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The main characteristics of PP-ASR are shown below:
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- Provides pre-trained models on Chinese open source datasets: VoxCeleb(English). The models include ecapa-tdnn.
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- Complete quasi-industrial solutions, including labelless training, cross-domain adaptive, super-large scale speaker training, data long tail problem solving, etc.
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- Support model training/evaluation.
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- 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.
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- Support interface operations and container deployment.
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<a name="3"></a>
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## 3. Tutorials
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<a name="31"></a>
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## 3.1 Pre-trained Models
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The support pre-trained model list: [released_model](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/released_model.md).
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For more information about model design, you can refer to the aistudio tutorial:
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- [ecapa-tdnn](https://aistudio.baidu.com/aistudio/projectdetail/4027664)
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<a name="32"></a>
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## 3.2 Training
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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.
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The specific steps of executing the script are recorded in `run.sh`.
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For more information, you can refer to [sv0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0)
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<a name="33"></a>
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## 3.3 Inference
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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`.
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Specific supported functions include:
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- Prediction of single audio
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- Score the similarity between the two audios
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- Support RTF calculation
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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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<a name="34"></a>
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## 3.4 Service Deployment
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PP-VPR supports Docker containerized service deployment. Through Milvus, MySQL performs high performance library building search.
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Demo of VPR Server: [audio_searching](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/audio_searching)
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For more information about service deployment, you can refer to the aistudio tutorial:
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- [speaker_recognition](https://aistudio.baidu.com/aistudio/projectdetail/4027664)
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<a name="4"></a>
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## 4. Quick Start
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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.
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