SepFormer WHAM16k Enhancement
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch/SpeechBrain |
| Paper | SepFormer Paper |
| Performance | 13.8 dB SI-SNR, 2.20 PESQ |
What is sepformer-wham16k-enhancement?
This is a specialized speech enhancement model based on the SepFormer architecture, implemented through SpeechBrain. It's designed to perform high-quality audio denoising on 16kHz sampled speech, trained on the WHAM! dataset. The model leverages transformer-based architecture to separate clean speech from background noise effectively.
Implementation Details
The model is built using the SpeechBrain framework and employs the SepFormer architecture, which utilizes self-attention mechanisms for speech separation. It processes audio at 16kHz sampling frequency and has been specifically optimized for the WHAM! dataset conditions.
- Achieves 13.8 dB SI-SNR on test set
- PESQ score of 2.20
- Compatible with GPU acceleration
- Easy integration through SpeechBrain's API
Core Capabilities
- Speech enhancement and denoising
- Environmental noise removal
- Real-time audio processing capability
- Handling of reverberant conditions
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines the power of transformer-based architecture with specialized training on the WHAM! dataset, making it particularly effective for real-world speech enhancement scenarios with environmental noise and reverberation.
Q: What are the recommended use cases?
The model is ideal for applications requiring high-quality speech enhancement, such as audio preprocessing for ASR systems, cleaning up recorded speech, and improving audio quality in communication systems operating at 16kHz sampling rate.
