voice activity detection

Introduction

The Voice Activity Detection (VAD) model is a PyTorch-based implementation designed to detect voice activity in audio data. It utilizes resources from the pyannote audio toolkit and is suitable for applications in speaker diarization and audio segmentation. The model supports datasets such as DIHARD and is released under the MIT license.

Architecture

The VAD model is part of the pyannote.audio.models.segmentation module and is based on the PyTorch framework. It is imported from the pyannote-audio-hub repository and leverages neural network architectures optimized for audio analysis and segmentation tasks.

Training

The model was trained by Hervé Bredin and colleagues. It is designed for end-to-end domain-adversarial voice activity detection, enhancing its robustness across various audio environments. The training process and techniques are detailed in their publications, including presentations at ICASSP 2020.

Guide: Running Locally

To run the Voice Activity Detection model locally, follow these basic steps:

1. Install PyAnnote-Audio: Ensure you have pyannote.audio installed in your Python environment.
2. Import and Initialize: Use the following Python code to import and set up the model:

   from pyannote.audio.core.inference import Inference
   
   model = Inference('julien-c/voice-activity-detection', device='cuda')
   

   Replace 'cuda' with 'cpu' if you do not have a CUDA-compatible GPU.
3. Inference: Run the model on your audio file:

   model({
       "audio": "TheBigBangTheory.wav"
   })
   

For optimal performance, it is recommended to use a cloud GPU service such as AWS, Google Cloud, or Azure if a local GPU is not available.

License

The Voice Activity Detection model is released under the MIT License, allowing for wide-ranging freedom to use, modify, and distribute the software, subject to the terms of the license.