PyDiar

This repo contains simple to use, pretrained/training-less models for speaker diarization.

Supported Models

  • Binary Key Speaker Modeling

    Based on pyBK by Jose Patino which implements the diarization system from “The EURECOM submission to the first DIHARD Challenge” by Patino, Jose and Delgado, H├ęctor and Evans, Nicholas

If you have any other models you would like to see added, please open an issue.

Usage

This library seeks to provide a very basic interface. To use the Binary Key model on a file, do something like this:

import numpy as np
from pydiar.models import BinaryKeyDiarizationModel, Segment
from pydiar.util.misc import optimize_segments
from pydub import AudioSegment

INPUT_FILE = "test.wav"

sample_rate = 32000
audio = AudioSegment.from_wav(test.wav)
audio = audio.set_frame_rate(sample_rate)
audio = audio.set_channels(1)

diarization_model = BinaryKeyDiarizationModel()
segments = diarization_model.diarize(
    sample_rate, np.array(audio.get_array_of_samples())
)
optimized_segments = optimize_segments(segments)

Now optimized_segments contains a list of segments with their start, length and speaker id

Example

A simple script which reads an audio file, diarizes it and transcribes it into the WebVTT format can be found in examples/generate_webvtt.py.
To use it, download a vosk model from https://alphacephei.com/vosk/models and then run the script using

poetry install
poetry run python -m examples.generate_webvtt -i PATH/TO/INPUT.wav -m PATH/TO/VOSK_MODEL

GitHub

View Github