README.md

Repository for paper Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
https://arxiv.org/abs/2010.11803

Requirement

Install pytorch using instructions from https://pytorch.org.
Install pyannote-audio using instructions from https://github.com/pyannote/pyannote-audio.

Evaluation

Prepare test data

Download AMI Headset-mix dataset using the script from https://github.com/pyannote/pyannote-audio/tree/master/tutorials/data_preparation.

Run test script for all experiments in the paper

Run the command
python diarization_pipeline.py [YOUR_AMI_DATA_PATH]
will generate rttm formated diarization results for all experiments.

Diarization for a single wav file (for ISAT)

Use command
python isat_diarization.py [WAV_PATH] [OUTPUT_DIR]
to generate both rttm formated VAD and diarization results for a 16k Hz wav file.

Training

Prepare trainin data

Train the model