Speaker Diarization Alternatives

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 Community 1 is a model hosted on Hugging Face for speaker diarization. It forms part of the pyannote.audio library and addresses the task of determining who spoke when in an audio recording. The model is provided under the cc-by-4.0 license. It is listed alongside capabilities in automatic speech recognition, speaker change detection, voice activity detection, and overlapped speech detection. Four papers are referenced via arxiv links. It is delivered as a downloadable model that loads directly into Python code through the pyannote.audio package. Inference runs on a complete audio file or on a selected time segment. The latter is performed by first cropping the waveform with the Audio utility and then passing the resulting dictionary containing waveform and sample rate to the loaded pipeline. The model card supplies example code for both full-file and excerpt-based inference. It is intended for use in research and development involving speech and audio processing.

pyannote-speaker-diarization-3.1 is a pipeline model based on pyannote.audio for performing speaker diarization, voice activity detection, and overlapped speech detection. It can process entire audio files or specific excerpts and is designed for research and production audio analysis tasks. The model is hosted on Hugging Face and integrates directly with the pyannote.audio library.

Reverb-diarization-v1 is a speaker diarization model built on the pyannote.audio framework. It identifies different speakers in an audio file and assigns time segments to each speaker. The model can process entire files or specific excerpts and is useful for transcription, meeting analysis, and archival audio processing.

Whisper Speaker Diarization is a web app that transcribes uploaded audio recordings and automatically labels speakers with timestamps. It is designed for journalists, researchers, and professionals needing accurate multi-speaker transcripts.

This model is a fine-tuned or adapted version of the pyannote.audio segmentation model (version 3.0). It is designed for tasks including speaker diarization, voice activity detection, overlapped speech detection, and speaker change detection. The model is hosted on Hugging Face under the ivrit-ai organization and is available for PyTorch inference.