SepFormer-WHAM
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | SpeechBrain |
| Paper | Attention is All You Need in Speech Separation |
| Performance | 16.3 dB SI-SNRi, 16.7 dB SDRi |
What is sepformer-wham?
SepFormer-WHAM is a sophisticated audio source separation model implemented using the SpeechBrain framework. Built on transformer architecture, it specifically tackles the challenge of separating mixed speech signals in the presence of environmental noise. The model was trained on the WHAM! dataset, which is an enhanced version of the WSJ0-Mix dataset incorporating real-world noise conditions.
Implementation Details
The model operates on 8kHz single-channel audio input and leverages the power of transformer architecture for source separation. It's implemented in PyTorch through the SpeechBrain toolkit, offering both CPU and GPU inference capabilities.
- Achieves state-of-the-art performance with 16.3 dB SI-SNRi on test set
- Supports real-time audio processing
- Implements the SepFormer architecture detailed in the paper
- Handles environmental noise effectively
Core Capabilities
- Speech separation in noisy environments
- Processing of single-channel 8kHz audio
- Separation of mixed speech signals
- Environmental noise handling
Frequently Asked Questions
Q: What makes this model unique?
The model uniquely combines transformer architecture with audio source separation, achieving exceptional performance on the WHAM! dataset while handling real-world noise conditions effectively.
Q: What are the recommended use cases?
The model is ideal for applications requiring speech separation in noisy environments, such as meeting transcription, hearing aids, and audio cleanup tasks. It works best with 8kHz single-channel audio input.
