PaddleSpeech/docs/source/vpr/PPVPR.md

(简体中文|English)

PP-VPR

Catalogue

• 1. Introduction
• 2. Characteristic
• 3. Tutorials
  • 3.1 Pre-trained Models
  • 3.2 Training
  • 3.3 Inference
  • 3.4 Service Deployment
• 4. Quick Start

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.

2. Characteristic

The basic process of VPR is shown in the figure below:

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.

3. Tutorials

3.1 Pre-trained Models

The support pre-trained model list: released_model.
For more information about model design, you can refer to the aistudio tutorial:

• ecapa-tdnn

3.2 Training

The referenced script for model training is stored in 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

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

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

For more information about service deployment, you can refer to the aistudio tutorial:

• speaker_recognition

4. Quick Start

To use PP-VPR, you can see here install, 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.