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(Trying to support audio-to-audio generation is in my todo list, such that you can input music tracks and the LDM will help merge them~)
We have to admit that the Unet architecture still has some probability advantage in subjective musicality, but this is not measured in the metric. And, we did have some models that were better on the metric, or trained for longer, but we observed that the models generally became less musicality after training too long, so we picked a model that was moderate on the metric as an open source sample. If you need more models (extreme metric pursuit or extreme musically pursuit, please contact me)
This repository provides an implementation of QA-MDT, integrating state-of-the-art models for music generation. The code and methods are based on the following repositories:
Before training, you need to download extra ckpts needed in ./audioldm_train/config/mos_as_token/qa_mdt.yaml and offset_pretrained_checkpoints.json
