speaker diarization 3.1 EU AI Act Compliance Profile

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🎹 Speaker diarization 3.1

This pipeline is the same as pyannote/speaker-diarization-3.0 except it removes the problematic use of onnxruntime.
Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference.
It requires pyannote.audio version 3.1 or higher.

It ingests mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance:

• stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels.
• audio files sampled at a different rate are resampled to 16kHz automatically upon loading.

Requirements

1. Install pyannote.audio 3.1 with pip install pyannote.audio
2. Accept pyannote/segmentation-3.0 user conditions
3. Accept pyannote/speaker-diarization-3.1 user conditions
4. Create access token at hf.co/settings/tokens.

Usage

# instantiate the pipeline
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
  "pyannote/speaker-diarization-3.1",
  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)

Processing on GPU

pyannote.audio pipelines run on CPU by default. You can send them to GPU with the following lines:

import torch
pipeline.to(torch.device("cuda"))

Processing from memory

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Tags

Library