Simplified diarization pipeline using some pretrained models.

Made to be a simple as possible to go from an input audio file to diarized segments.

import soundfile as sf
import matplotlib.pyplot as plt

from simple_diarizer.diarizer import Diarizer
from simple_diarizer.utils import combined_waveplot

diar = Diarizer(
                  embed_model='xvec', # 'xvec' and 'ecapa' supported
                  cluster_method='sc' # 'ahc' and 'sc' supported
               )

segments = diar.diarize(WAV_FILE, num_speakers=NUM_SPEAKERS)

signal, fs = sf.read(WAV_FILE)
combined_waveplot(signal, fs, segments)
plt.show()

Simplified diarization is available on PyPI:

pip install simple-diarizer

"Some Quick Advice from Barack Obama!"

The following pretrained models are used:

• Voice Activity Detection (VAD)
  • Silero VAD
• Deep speaker embedding extraction
  • SpeechBrain
    • X-Vector
    • ECAPA-TDNN
• (Optional/Experimental) Speech-to-text
  • ESPnet Model Zoo
    • English ASR model

It can be checked out in the above link, where it will try and diarize any input YouTube URL.

• Spectral clustering methods lifted from https://github.com/wq2012/SpectralCluster