For more information on how to run this diarization model see https://github.com/revdotcom/reverb/tree/main/diarization
Reverb diarization V2 provides a 22.25% relative improvement in WDER (Word Diarization Error Rate) compared to the baseline pyannote3.0 model, evaluated on over 1,250,000 tokens across five different test suites.
Test suite | WDER |
---|---|
earnings21 | 0.046 |
rev16 | 0.078 |
Usage
# taken from https://huggingface.co/pyannote/speaker-diarization-3.1 - see for more details
# instantiate the pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"Revai/reverb-diarization-v2",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
# run the pipeline on an audio file
diarization = pipeline("audio.wav")
# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
License
See LICENSE for details.
- Downloads last month
- 36