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@meronym
meronym committed Apr 1, 2023
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Expand Up @@ -8,7 +8,7 @@ This model receives an audio file and identifies the individual speakers within

The model is based on a pre-trained speaker diarization pipeline from the [`pyannote.audio`](pyannote.github.io) package, with a post-processing layer that cleans up the output segments and computes input-wide speaker embeddings.

`pyannote.audio` is an open-source toolkit written in Python for speaker diarization. Based on the [PyTorch](pytorch.org) machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines.
`pyannote.audio` is an open-source toolkit written in Python for speaker diarization based on [PyTorch](pytorch.org). It provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines.

The main pipeline makes use of:

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