Clean up noisy audio
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denoise audio with no limit. Output MP3 192 kbps.
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Demo for audiobox-aesthetics
Speechbrain Sepformer Wham16k Enhancement is a state-of-the-art audio enhancement model developed using the SpeechBrain framework. It is specifically designed to clean up noisy audio by separating speech from background noise. The model is trained on the WHAM16k dataset, which contains pairs of noisy and clean speech, making it highly effective for real-world noisy environments. This tool is ideal for improving audio quality in applications such as voice calls, podcasts, and video recordings.
• Neural Network-Based Separation: Leverages advanced neural networks to separate speech from noise effectively.
• 16kHz Audio Support: Optimized for high-quality audio at 16kHz sample rate.
• WHAM16k Pre-Training: Trained on the WHAM16k dataset for robust noise suppression.
• Real-Time Capability: Designed to process audio in real-time, making it suitable for live applications.
• Open-Source: Part of the SpeechBrain ecosystem, ensuring transparency and customizability.
• Compatibility: Works with various audio formats and integrates seamlessly into existing workflows.
• Voice Activity Detection (VAD): Includes VAD to handle non-speech segments effectively.
pip install speechbrainfrom speechbrain.pretrained import SepformerWham16kEnhancement
enhancer = SepformerWham16kEnhancement()
3. **Load Audio**: Load your noisy audio file using the `read_audio` method:
```python
noisy_audio = enhancer.read_audio("noisy_audio.wav")
enhanced_audio = enhancer.enhance(noisy_audio)
enhancer.save_audio("enhanced_audio.wav", enhanced_audio)
What is the WHAM16k dataset?
The WHAM16k dataset is a collection of noisy and clean speech pairs, specifically designed for training speech separation models. It provides a diverse range of noise conditions, making models trained on it highly effective in real-world scenarios.
Can I use Speechbrain Sepformer Wham16k Enhancement for real-time applications?
Yes, Speechbrain Sepformer Wham16k Enhancement is optimized for real-time audio processing, making it suitable for applications like voice calls or live audio streaming.
How does it handle different types of noise?
The model is trained on a wide variety of noise conditions from the WHAM16k dataset, allowing it to handle diverse types of background noise effectively. For highly specific noise types, you can further fine-tune the model for better performance.