You can refer to the code below which denoises an audio signal using a deep-learning approach:
In the above code, we are using a pre-trained Model that replaces the DenoisingAutoencoder with a suitable pre-trained denoising model, Spectrogram Transformations that convert the waveform to a spectrogram for processing and back to the waveform post-denoising, and Inference that uses a torch.no_grad() to perform inference efficiently.
Hence, this pipeline handles noisy audio with a deep learning-based denoising approach.