spkrec-ecapa-voxceleb
Maintainer: speechbrain
132
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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-ecapa-voxceleb model is a speaker verification system developed by the SpeechBrain team. It uses the ECAPA-TDNN architecture, which combines convolutional and residual blocks, to extract speaker embeddings from audio recordings. The model was trained on the Voxceleb 1 and Voxceleb 2 datasets, achieving an impressive Equal Error Rate (EER) of 0.8% on the Voxceleb1-test (Cleaned) dataset.
Similar models include the VoxLingua107 ECAPA-TDNN Spoken Language Identification Model and the Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0 model, both of which leverage the ECAPA-TDNN architecture for different tasks.
Model inputs and outputs
Inputs
- Audio recordings, typically sampled at 16kHz (single channel)
Outputs
- Speaker embeddings: A 768-dimensional vector that captures the speaker's voice characteristics
- Speaker verification score: A score indicating the likelihood that two audio recordings belong to the same speaker
Capabilities
The spkrec-ecapa-voxceleb model is highly capable at speaker verification tasks. It can be used to determine whether two audio recordings are from the same speaker by computing the cosine distance between their speaker embeddings. The model has demonstrated state-of-the-art performance on the Voxceleb benchmark, making it a reliable choice for applications that require accurate speaker identification.
What can I use it for?
The spkrec-ecapa-voxceleb model can be used in a variety of applications that require speaker verification, such as:
- Voice-based authentication systems: Verify the identity of users based on their voice characteristics.
- Speaker diarization: Identify and separate different speakers in an audio recording.
- Personalized digital assistants: Recognize the user's voice and tailor the experience accordingly.
- Biometric security: Enhance security by using voice as an additional biometric factor.
Things to try
One interesting thing to try with the spkrec-ecapa-voxceleb model is to use it as a feature extractor for other speaker-related tasks. The 768-dimensional speaker embeddings produced by the model can be a valuable input for training custom speaker recognition or speaker diarization models. Additionally, you could explore ways to combine the speaker embeddings with other modalities, such as text or visual information, to create multimodal speaker recognition systems.
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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