wav2vec2-base-superb-sid
| Property | Value |
|---|---|
| Model Type | Speaker Identification |
| Base Architecture | Wav2Vec2 |
| Accuracy | 75.18% |
| Paper | SUPERB: Speech processing Universal PERformance Benchmark |
What is wav2vec2-base-superb-sid?
wav2vec2-base-superb-sid is a specialized speech processing model based on wav2vec2-base architecture, specifically fine-tuned for speaker identification tasks. Developed as part of the SUPERB benchmark, this model is designed to classify speakers from audio inputs, operating on 16kHz sampled speech audio.
Implementation Details
The model is built upon the wav2vec2-base architecture and has been optimized for speaker identification using the VoxCeleb1 dataset. It processes audio input through a feature extraction pipeline and outputs speaker classifications using a sequence classification head.
- Built on wav2vec2-base pretrained model
- Requires 16kHz audio sampling rate
- Implements multi-class classification for speaker identification
- Trained and evaluated on VoxCeleb1 dataset
Core Capabilities
- Speaker identity classification from audio input
- Multi-class classification across predefined speaker set
- Direct integration with Hugging Face's transformers library
- Support for batch processing and attention masks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for speaker identification as part of the SUPERB benchmark, achieving 75.18% accuracy. It combines wav2vec2's powerful audio processing capabilities with specialized training for speaker recognition tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring speaker identification from audio, such as speaker diarization systems, voice authentication, and audio content analysis. It works best with 16kHz audio input and predefined speaker sets.
