spkrec-resnet-voxceleb
| Property | Value |
|---|---|
| Framework | SpeechBrain |
| Training Data | VoxCeleb 1 + VoxCeleb 2 |
| Performance | EER: 1.05%, minDCF: 0.1082 |
| Paper | https://doi.org/10.1016/j.csl.2019.101026 |
What is spkrec-resnet-voxceleb?
spkrec-resnet-voxceleb is a state-of-the-art speaker recognition model that combines ResNet and TDNN architectures for robust speaker verification and embedding extraction. Developed by the SpeechBrain team, this model has been trained on the comprehensive VoxCeleb dataset and achieves impressive performance metrics with a 1.05% Equal Error Rate.
Implementation Details
The model implements a ResNet TDNN architecture trained with Additive Margin Softmax Loss. It performs speaker verification using cosine distance between speaker embeddings and can be easily integrated into existing applications using the SpeechBrain framework. The system supports both CPU and GPU inference, making it versatile for different deployment scenarios.
- Trained on VoxCeleb 1 + VoxCeleb 2 datasets
- Uses Additive Margin Softmax Loss for training
- Supports batch processing for embedding extraction
- Includes GPU acceleration support
Core Capabilities
- Speaker embedding extraction from audio files
- Binary speaker verification between two audio samples
- Batch processing support for efficient inference
- Cross-platform compatibility through SpeechBrain
Frequently Asked Questions
Q: What makes this model unique?
The model combines ResNet and TDNN architectures with state-of-the-art performance on speaker verification tasks, achieving a remarkably low 1.05% EER. Its integration with SpeechBrain makes it particularly accessible for both research and production environments.
Q: What are the recommended use cases?
This model is ideal for speaker verification systems, voice authentication applications, speaker diarization tasks, and any application requiring reliable speaker embedding extraction. It's particularly well-suited for scenarios requiring both accuracy and processing efficiency.
