Enhance audio quality by removing noise
Remove noise from audio recordings
Generate lofi effect for your audio
Enhance audio quality with AI-driven denoising and enhancement
Generate audio with text and reference audio
Audio Compressor Upload an audio file and select the compres
Convert audio to different voice tones
Generate audio from text using a reference audio
Generate new voice from source with reference audio
Enhance audio by removing noise
Generate clean audio from noisy recordings
Tame audio by removing noise and normalizing
Voice conversion framework based on VITS
Speechbrain Sepformer Wham16k Enhancement is a state-of-the-art audio enhancement tool designed to improve speech quality by removing background noise and other unwanted sounds. Built on the Sepformer architecture, it is optimized for 16kHz audio signals, making it ideal for applications like voice calls, podcasts, and video conferencing. The model leverages advanced deep learning techniques to separate speech from noise, delivering crisp and clear audio outputs.
from speechbrain.pretrained import SepFormerWham16k command to load the pre-trained model.What input formats does Speechbrain Sepformer Wham16k Enhancement support?
The model supports common audio formats such as WAV, MP3, and RAW audio streams, with a preference for 16kHz, 16-bit, mono channel input.
Can I use Speechbrain Sepformer Wham16k for real-time audio?
Yes, the model is designed for real-time processing and can be integrated with streaming audio applications, though performance may vary based on hardware and implementation.
How do I improve noise reduction performance?
You can experiment with fine-tuning the model on your specific dataset or adjusting the noise reduction parameters to achieve better results. Additionally, pre-processing steps like noise normalization can enhance performance.