u/MusicProd28

FYI, I'm a full-time AI engineer and a part-time music producer. I finally finished training my own music generation model based on Meta's open music foundation model. It took months of experimentation and a ridiculous amount of compute, but it finally paid off.

The compute bill definitely hurt, but hearing the first genuinely usable compositions made all the debugging and GPU hours worth it.

https://preview.redd.it/lxo0hco3zlbh1.png?width=640&format=png&auto=webp&s=8bcd2752d090b38270806f120046196f47056975

https://preview.redd.it/gfxtxsmuqlbh1.png?width=1860&format=png&auto=webp&s=4b6f595e455268586db8679d1de481e5d926d60d

YAY

FYI, I'm a full-time AI engineer and a part-time music producer. I finally finished training my own music generation model based on Meta's open music foundation model. It took months of experimentation and a ridiculous amount of compute, but it finally paid off.

• Started with Meta's open music model as the base instead of training everything from scratch.
• Built a custom dataset by collecting and organizing thousands of genre-specific MIDI files, stems, and audio samples.
• Spent a lot of time cleaning, deduplicating, and tokenizing the data so training wouldn't be noisy.
• Fine-tuned the model on a multi-GPU setup using mixed precision and gradient checkpointing to squeeze as much as possible out of the hardware.
• Burned through several failed training runs because of unstable checkpoints, bad hyperparameters, and overfitting.
• Wrote custom evaluation scripts to compare generations across melody, rhythm, harmony, and overall musical coherence.
• Iterated on learning rates, context lengths, batch sizes, and dataset balancing until the outputs started sounding consistently good.
• Optimized inference so generations were significantly faster than my early prototypes.
• The compute bill definitely hurt, but hearing the first genuinely usable compositions made all the debugging and GPU hours worth it.

https://preview.redd.it/272i12vlqlbh1.png?width=1860&format=png&auto=webp&s=c4fb4437028fe37c820c7a0549fa0f8132de6605

Here is the money part

FYI, I'm a full-time AI engineer and a part-time music producer. I finally finished training my own music generation model based on Meta's open music foundation model. It took months of experimentation and a ridiculous amount of compute, but it finally paid off.

• Started with Meta's open music model as the base instead of training everything from scratch.
• Built a custom dataset by collecting and organizing thousands of genre-specific MIDI files, stems, and audio samples.
• Spent a lot of time cleaning, deduplicating, and tokenizing the data so training wouldn't be noisy.
• Fine-tuned the model on a multi-GPU setup using mixed precision and gradient checkpointing to squeeze as much as possible out of the hardware.
• Burned through several failed training runs because of unstable checkpoints, bad hyperparameters, and overfitting.
• Wrote custom evaluation scripts to compare generations across melody, rhythm, harmony, and overall musical coherence.
• Iterated on learning rates, context lengths, batch sizes, and dataset balancing until the outputs started sounding consistently good.
• Optimized inference so generations were significantly faster than my early prototypes.
• The compute bill definitely hurt, but hearing the first genuinely usable compositions made all the debugging and GPU hours worth it.

Happy to answer questions if anyone's working on audio or generative AI.

https://preview.redd.it/bdzcooxxplbh1.png?width=1860&format=png&auto=webp&s=a6457f3b3d99e2cc2c6e077e18a1462e660d206f

FYI, I'm a full-time AI engineer and a part-time music producer. I finally finished training my own music generation model based on Meta's open music foundation model. It took months of experimentation and a ridiculous amount of compute, but it finally paid off.

• Started with Meta's open music model as the base instead of training everything from scratch.
• Built a custom dataset by collecting and organizing thousands of genre-specific MIDI files, stems, and audio samples.
• Spent a lot of time cleaning, deduplicating, and tokenizing the data so training wouldn't be noisy.
• Fine-tuned the model on a multi-GPU setup using mixed precision and gradient checkpointing to squeeze as much as possible out of the hardware.
• Burned through several failed training runs because of unstable checkpoints, bad hyperparameters, and overfitting.
• Wrote custom evaluation scripts to compare generations across melody, rhythm, harmony, and overall musical coherence.
• Iterated on learning rates, context lengths, batch sizes, and dataset balancing until the outputs started sounding consistently good.
• Optimized inference so generations were significantly faster than my early prototypes.
• The compute bill definitely hurt, but hearing the first genuinely usable compositions made all the debugging and GPU hours worth it.

Happy to answer questions if anyone's working on audio or generative AI.

https://preview.redd.it/2lbiy3akplbh1.png?width=1477&format=png&auto=webp&s=4eb3242602b9718997c6ab0844c0bf3b36d3c4aa