[REQ] Delossifier function

[MarcoRavich]

Hi there,
after the recent cool super resolution feature add, it would be great to have a tool able to "delossify" - as far as possible - compressed audio.

@kroll-software has recently updated their Audio Delossifier and a brunch of pre-trained models (for mp3 delossification) are provided too.

Last but not least, I've just created a simple script to inference on Colab: kroll-software/AudioDelossifier#5 (comment)

Hope that inspires !

[RyanMetcalfeInt8]

Thanks for the idea. I also see some MP3 restoration models listed here: https://github.com/ZFTurbo/Music-Source-Separation-Training/blob/main/docs/pretrained_models.md#single-stem-models

As always, it would be very helpful to get some help from the community to evaluate & validate quality of the projects & available pre-trained models.

[MarcoRavich]

Thanks for the hint and happy new year, @RyanMetcalfeInt8 !

Just added Apollo by @JusperLee under AUDIO \ AI-based \ Restorers:

Apollo consistently outperforms existing SR-GAN models across various bit rates and music genres, particularly excelling in complex scenarios involving mixtures of multiple instruments and vocals. Apollo significantly improves music restoration quality while maintaining computational efficiency.

Anyway I honestly didn't understand exactly how it can work since it relies on music separation nnet, even if:

During data preprocessing, we drew inspiration from music separation techniques and implemented the following steps:

Source Activity Detection (SAD):
We used a Source Activity Detector (SAD) to remove silent regions from the audio tracks, retaining only the significant portions for training.

Data Augmentation:
We performed real-time data augmentation by mixing tracks from different songs. For each mix, we randomly selected between 1 and 8 stems from the 11 available tracks, extracting 3-second clips from each selected stem. These clips were scaled in energy by a random factor within the range of [-10, 10] dB relative to their original levels. The selected clips were then summed together to create simulated mixed music.

Simulating Dynamic Bitrate Compression:
We simulated various bitrate scenarios by applying MP3 codecs with bitrates of [24000, 32000, 48000, 64000, 96000, 128000].

Rescaling:
To ensure consistency across all samples, we rescaled both the target and the encoded audio based on their maximum absolute values.

Saving as HDF5:
After preprocessing, all data (including the source stems, mixed tracks, and compressed audio) was saved in HDF5 format, making it easy to load for training and evaluation purposes.

Hope that active nnet-based "audio restoration" projects' devs (such as @asjad895, @AakashRevankar, @matthewmcq, @shaws34, @bkraad47 and @kroll-software of course) will join this discussion too.

If can help I'll try to engage Hydrogenaudio's experts again for qualitative evaluation of restorers' outputs.

[Brodvd]

If want to try fast: https://huggingface.co/spaces/patriotyk/Apollo
(there's also the checkpoint "apollo-universal" is more or less Audio SR but not the same)

[MarcoRavich]

@yoongi43 - Exploiting Time-Frequency Conformers for Music Audio Enhancement author - just noticed about this discussion.