spkrec-xvect-voxceleb
Maintainer: speechbrain
48
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| 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 spkrec-xvect-voxceleb model is a speaker verification system that uses x-vector embeddings trained on the VoxCeleb dataset. This model is provided by the SpeechBrain team, who are known for their general-purpose SpeechBrain toolkit. The SpeechBrain team has also released similar speaker verification models like the spkrec-ecapa-voxceleb which uses the ECAPA-TDNN architecture.
Model inputs and outputs
Inputs
- Audio recordings of speech, preferably sampled at 16 kHz
Outputs
- Speaker embeddings, which are fixed-size vector representations of the input speech that can be used for speaker verification tasks
- Scores and predictions indicating whether two speech samples belong to the same speaker or not
Capabilities
This model can be used to extract speaker embeddings from audio recordings, which can then be used for tasks like speaker diarization, speaker clustering, and speaker recognition. The model achieves 3.2% equal error rate (EER) on the VoxCeleb1-test set, which is a strong performance for a speaker verification system.
What can I use it for?
The spkrec-xvect-voxceleb model can be used as a building block in various speech processing applications that require speaker recognition capabilities. For example, it could be used in a call center to identify speakers and route calls accordingly, or in a conferencing system to attribute each utterance to the correct participant. Additionally, the extracted speaker embeddings could be used as features in downstream machine learning models for tasks like speaker diarization or identification.
Things to try
One interesting thing to try with this model is to use the extracted speaker embeddings as inputs to a custom speaker recognition or diarization system. By leveraging the pre-trained embeddings, you may be able to achieve better performance on your specific use case compared to training a model from scratch. Additionally, you could experiment with combining this speaker verification model with other SpeechBrain models, such as the emotion-recognition-wav2vec2-IEMOCAP model, to create a more comprehensive speech processing pipeline.
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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