Speaker Diarization 3.1 is a pre-trained pipeline published on Hugging Face that performs speaker diarization on audio recordings. It identifies which speaker spoke when by combining voice activity detection, speaker change detection, and overlapped speech detection.
The model is provided by pyannote and is based on the pyannote.audio library. It accepts full audio files or cropped excerpts supplied as waveform and sample rate. Inference returns annotated segments that assign speaker labels to portions of the input. The pipeline is delivered as a downloadable model from the Hugging Face hub and runs locally through the pyannote.audio Python library. Example code demonstrates loading the pipeline and processing either an entire WAV file or a defined time segment.
It carries an MIT license. The associated research appears in two arXiv papers referenced on the model card. Notebooks for Google Colab and Kaggle are linked to support experimentation. The model page classifies the work under automatic speech recognition and lists tags for audio, voice, speech, speaker diarization, speaker change detection, voice activity detection, and overlapped speech detection.
Speaker Diarization sits in PulseGate's Voice, TTS & speech category. It focuses on determining which speaker said what and when in multi-speaker audio recordings. Speaker Diarization is an open-source project aimed at machine learning developers. The project is open source (MIT). Speaker Diarization is available on the web, the command line, and API.
pyannote builds and maintains Speaker Diarization, and the product first shipped in 2016. The project is developed in the open on GitHub with 10.3k stars and 13 commits in the last 90 days. Among its 4 catalogued features are Speaker Diarization, Voice Activity Detection, and Overlapped Speech Detection. It exposes integrations via a public API.
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