voxcelebs12_rawnet3
| Property | Value |
|---|---|
| Author | Jungjee (ESPnet) |
| Performance (EER) | 0.739% |
| Paper | ESPnet-SPK Paper |
| Framework | ESPnet2 |
What is voxcelebs12_rawnet3?
voxcelebs12_rawnet3 is a state-of-the-art speaker recognition model implemented in the ESPnet2 framework. It utilizes the RawNet3 architecture working directly with raw waveform inputs and demonstrates exceptional performance with an Equal Error Rate (EER) of 0.739%.
Implementation Details
The model employs a sophisticated architecture with several key components: a sinc-based frontend (256 filters), RawNet3 encoder with model scale 8 and 1024 dimensions, channel attention statistical pooling, and a 192-dimensional embedding projector. It's trained using AAM-Softmax loss with subcenter and top-k sampling.
- Raw waveform processing with 16kHz sampling rate
- Self-supervised front-end using sinc convolutions
- 1536-dimensional internal representations
- Integrated noise and RIR augmentation during training
Core Capabilities
- Speaker verification with high accuracy (0.739% EER)
- Robust against environmental variations through data augmentation
- Efficient 192-dimensional speaker embeddings
- Compatible with ESPnet2's ecosystem
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its end-to-end raw waveform processing approach, eliminating the need for traditional spectrogram features. It achieves state-of-the-art performance while maintaining relatively compact embeddings (192-dim).
Q: What are the recommended use cases?
This model is ideal for speaker verification tasks, particularly in scenarios requiring high accuracy and robustness. It's well-suited for applications like voice authentication, speaker diarization, and speaker identification in multi-speaker environments.
