segmentation

Introduction

Pyannote's Segmentation model, available on Hugging Face, is an open-source tool for audio processing tasks such as speaker segmentation, voice activity detection, and overlapped speech detection. It is built using PyTorch and is part of the pyannote.audio suite, designed to aid in speaker diarization and machine listening.

Architecture

The Segmentation model utilizes deep learning techniques to perform audio analysis tasks. It offers pre-trained pipelines for tasks including speaker segmentation and overlapped speech detection. The model relies on pyannote.audio version 2.1.1 for its operations.

Training

The model's development and training are detailed in the paper titled End-to-end speaker segmentation for overlap-aware resegmentation. Various hyper-parameters are used for different datasets such as AMI Mix-Headset, DIHARD3, and VoxConverse to fine-tune the model's performance across tasks like voice activity detection and resegmentation.

Guide: Running Locally

1. Setup: Visit the Hugging Face Segmentation page and accept the user conditions.
2. Token Generation: Create an access token at Hugging Face settings.
3. Model Instantiation:

   from pyannote.audio import Model
   model = Model.from_pretrained("pyannote/segmentation", 
                                 use_auth_token="ACCESS_TOKEN_GOES_HERE")
   

4. Voice Activity Detection: Implement detection using the pre-trained model.

   from pyannote.audio.pipelines import VoiceActivityDetection
   pipeline = VoiceActivityDetection(segmentation=model)
   pipeline.instantiate(HYPER_PARAMETERS)
   vad = pipeline("audio.wav")
   

5. Hardware Suggestions: For optimal performance, consider using cloud GPUs from providers like AWS or Google Cloud.

License

The Pyannote Segmentation model is released under the MIT License, allowing for wide usage and distribution, with the requirement to maintain the license details in derivative works.