speaker-diarization-3.0
Maintainer: pyannote
142
🏋️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The speaker-diarization-3.0 model is an open-source pipeline for speaker diarization, trained by Sverin Baroudi using the pyannote.audio library version 3.0.0. It takes in mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance, which can be used to identify who is speaking when in the audio. The pipeline was trained on a combination of several popular speech datasets, including AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.
The model is similar to the speaker-diarization model, which uses an earlier version of the pyannote.audio library. Both models aim to perform the task of speaker diarization, identifying who is speaking when in an audio recording.
Model inputs and outputs
Inputs
- Mono audio sampled at 16kHz
Outputs
- An
Annotationinstance containing the speaker diarization information, which can be used to identify when each speaker is talking.
Capabilities
The speaker-diarization-3.0 model can effectively identify speakers and when they are talking in a given audio recording. It can handle stereo or multi-channel audio by automatically downmixing to mono, and can also resample audio files to 16kHz if needed. The model achieves strong performance, with a diarization error rate (DER) of around 14% on the AISHELL-4 dataset.
What can I use it for?
The speaker-diarization-3.0 model can be useful for a variety of applications that require identifying speakers in audio, such as:
- Transcription and captioning for meetings or interviews
- Speaker tracking in security or surveillance applications
- Audience analysis for podcasts or other audio content
- Improving speech recognition systems by leveraging speaker information
The maintainers of the model also offer consulting services for organizations looking to use this pipeline in production.
Things to try
One interesting aspect of the speaker-diarization-3.0 model is its ability to process audio on GPU, which can significantly improve the inference speed. The model achieves a real-time factor of around 2.5% when running on a single Nvidia Tesla V100 SXM2 GPU, meaning it can process a one-hour conversation in about 1.5 minutes.
Developers can also experiment with running the model directly from memory, which may provide further performance improvements. The pipeline also offers hooks to monitor the progress of the diarization process, which can be useful for debugging and understanding the model's behavior.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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