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Awesome GitHub RepositoriesClustering Algorithms
Techniques for grouping audio embeddings into distinct speaker identities using distance metrics.
Distinct from Speaker Diarization: Focuses on the specific clustering implementation rather than the general diarization task
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Awesome Clustering Algorithms GitHub Repositories
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- pyannote/pyannote-audio
pyannote/pyannote-audio
9,203Auf GitHub ansehenPyannote.audio is a PyTorch toolkit for speaker diarization, speaker identification, and speech activity detection. Its primary purpose is to partition audio recordings into segments and assign each segment to a specific speaker identity to determine who spoke when. The project includes a framework for classifying speaker identities and a pipeline for distinguishing human speech from background noise. It provides specialized tools for handling symmetric-overlap speech, where multiple speakers talk simultaneously, and employs learnable band-pass filters for raw waveform feature extraction. Th
Implements clustering-based algorithms to group similar audio embeddings into distinct speaker identities.
Jupyter Notebookoverlapped-speech-detectionpretrained-modelspytorch
