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:
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:
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)
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.
