yocontra
commented
2 months ago These parameters might be useful to look at within the musicgen codebase, if you want to understand how it goes from json -> text:
dataset.train.merge_text_p
dataset.train.drop_desc_p
dataset.train.drop_other_p
conditioners.description.t5.word_dropoutCheck out this project for a working example on fine tuning: https://github.com/sakemin/cog-musicgen-fine-tuner
I would like to perform finetuning on a small musicgen model. I have a dataset consisting of different short sounds. Actually, it's not big. But I successfully trained on the melodies of one instrument, where the dataset size was extremely small, only 3 hours, and I got interesting results. However, with sounds everything is different. In 80% of cases or even more often, I encounter the fact that after the main attack sound I have a crackling sound in the generated audio track.
I've tried various ways to optimize training, but so far nothing obvious has helped, such as reducing the learning rate or dropout. Also, my logs look very suspicious from the very beginning of the training:
Train Summary | Epoch 1 | lr=1.00E+00 | grad_norm=INF | grad_scale=45645.824 | ce=0.962 | ppl=2.650 | duration=2472.758It's also interesting that when I enter a word with a small letter and a word with a capital letter in the prompt, I get different results. In this case, everything depends on the word. In one case, the result is as expected, but in the other there is a complete bunch of random sounds, as if the model had not been trained. (By the way, I checked the original models and during generation the situation with uppercase and lowercase letters for the same word is similar.) In fact, I'd be very interested to know more about how merging text tags that are packaged in json format for each sample works. I'm new to learning your model. Thanks in advance for your answer and help!