overlapped speech detection

Introduction

The Pyannote Overlapped Speech Detection model is designed to identify segments of audio where multiple speakers are talking simultaneously. It is built on the pyannote.audio framework and supports various datasets such as AMI, DIHARD, and VoxConverse.

Architecture

The model uses the pyannote.audio 2.1 framework, which provides neural building blocks for speaker diarization tasks. It is specifically tailored to detect overlapping speech, leveraging pretrained pipelines to efficiently process audio data.

Training

The model is trained using public datasets like AMI, DIHARD, and VoxConverse. These datasets provide diverse audio environments, ensuring the model's robustness in detecting overlapping speech segments across different scenarios.

Guide: Running Locally

To run the Overlapped Speech Detection model locally, follow these steps:

1. Accept User Conditions: Visit Hugging Face to accept the user conditions.
2. Create an Access Token: Generate an access token from Hugging Face settings.
3. Install pyannote.audio: Follow the installation instructions on the GitHub repository.
4. Run the Model:

   from pyannote.audio import Pipeline
   pipeline = Pipeline.from_pretrained("pyannote/overlapped-speech-detection", use_auth_token="ACCESS_TOKEN_GOES_HERE")
   output = pipeline("audio.wav")
   
   for speech in output.get_timeline().support():
       # two or more speakers are active between speech.start and speech.end
       ...
   

For enhanced performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

The model is released under the MIT License, allowing for wide usage and modification with proper attribution.