ECAPA-TDNN Speaker Recognition Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch (SpeechBrain) |
| Dataset | VoxCeleb 1 + VoxCeleb 2 |
| Performance | 0.80% EER on VoxCeleb1-test |
| Paper | Link to Paper |
What is spkrec-ecapa-voxceleb?
The spkrec-ecapa-voxceleb is a state-of-the-art speaker recognition model based on the ECAPA-TDNN architecture. Developed by SpeechBrain, this model excels at speaker verification and embedding extraction tasks, trained on the extensive VoxCeleb dataset.
Implementation Details
The model implements an ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network) architecture, combining convolutional and residual blocks with attentive statistical pooling for embedding extraction. It's trained using Additive Margin Softmax Loss and performs speaker verification using cosine distance between embeddings.
- Trained on 16kHz audio samples
- Supports automatic audio normalization
- Implements GPU inference capabilities
- Achieves 0.80% Equal Error Rate (EER)
Core Capabilities
- Speaker embedding extraction from audio
- Speaker verification between two audio samples
- Automatic audio preprocessing and normalization
- Batch processing support
Frequently Asked Questions
Q: What makes this model unique?
The model's ECAPA-TDNN architecture combines advanced channel attention mechanisms with time delay neural networks, achieving state-of-the-art performance in speaker recognition tasks with a remarkably low 0.80% EER.
Q: What are the recommended use cases?
This model is ideal for speaker verification systems, biometric authentication, speaker diarization, and any application requiring robust speaker embeddings. It's particularly effective for real-world applications due to its automatic audio preprocessing capabilities.
