SepFormer WHAMR
| Property | Value |
|---|---|
| Author | SpeechBrain |
| Performance | 13.7 dB SI-SNRi |
| Paper | ICASSP 2021: Attention is All You Need in Speech Separation |
| License | Open Source |
What is sepformer-whamr?
SepFormer-WHAMR is a state-of-the-art speech separation model implemented using the SpeechBrain toolkit. It's specifically trained on the WHAMR! dataset, which is an enhanced version of the WSJ0-Mix dataset that includes environmental noise and reverberation effects. The model demonstrates impressive performance with 13.7 dB SI-SNRi and 12.7 dB SDRi on test sets.
Implementation Details
The model operates on 8kHz single-channel audio inputs and utilizes the Transformer architecture for source separation. It's implemented in PyTorch through the SpeechBrain framework, offering both CPU and GPU inference capabilities.
- Built on SpeechBrain framework for robust audio processing
- Supports real-time audio source separation
- Handles reverberant and noisy environments
- 8kHz sampling rate requirement
Core Capabilities
- Separates mixed audio sources in complex environments
- Processes environmental noise and reverberation
- Achieves 13.7 dB SI-SNRi performance
- Supports both CPU and GPU inference
- Easy integration through Python API
Frequently Asked Questions
Q: What makes this model unique?
This model specifically addresses the challenging task of speech separation in reverberant and noisy conditions, making it more practical for real-world applications compared to models trained on clean speech only.
Q: What are the recommended use cases?
The model is ideal for applications requiring speech separation in challenging acoustic environments, such as meeting transcription systems, hearing aids, and audio preprocessing for speech recognition systems.
