Using this open source model in production? Consider switching to pyannoteAI for better and faster options. 🎹 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 Processing on GPU pyannote.audio pipelines run on CPU by default. You can send them to GPU with the following lines: Processing from memory Pre loading audio files in memory may result in faster processing: Monitoring progress Hooks are avail…
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