Multi-instrument Music Synthesis with Spectrogram Diffusion

An unofficial PyTorch implementation of the paper Multi-instrument Music Synthesis with Spectrogram Diffusion, adapted from official codebase. We aim to increase the reproducibility of their work by providing training code and pre-trained models in PyTorch.

Data Preparation

Please download the following datasets.

• MusicNet
• Maestro
• GuitarSet
• URMP
• Slakh2100

Create Clean MIDI for URMP

The MIDI files in the URMP dataset mostly don't contain the correct program number. Use the clean_urmp_midi.py script to create a new set of MIDI files that contain the correct program number corresponding to the instruments in the file names.

Training

Small Autoregressive

python main.py fit --config cfg/ar_small.yaml

Diffusion, Small without Context

python main.py fit --config cfg/diff_small.yaml

Diffusion, Small with Context

python main.py fit --config cfg/diff_small.yaml --data.init_args.with_context true --model.init_args.with_context true

Diffusion, Base with Context

python main.py fit --config cfg/diff_base.yaml

Remember to change the path arguments under the data section of the yaml files to where you downloaded the dataset, or set them using --data.init_args.*_path keyword in commandline. You can also set the path to null if you want to ommit that dataset. Notice that URMP requires one extra path argument, which is where you create the clean MIDI.

To adjust other hyperparmeters, please refer to LightningCLI documentation for more information.

Evaluating

The following command will compute the Reconstruction and FAD metrics using the embeddings from the VGGish and TRILL models and reporting the averages across the whole test dataset.

python main.py test --config config.yaml --ckpt_path your_checkpoint.ckpt

Inferencing

To synthesize audio from MIDI with trained models:

python infer.py input.mid checkpoint.ckpt config.yaml output.wav

Pre-Trained Models

We provided three pre-trained models corresponding to the diffusion baselines in the paper.

• small without context
• small with context
• base with context
• generated samples

We trained them following the settings in the paper besides the batch size, which we reduced to 8 due to limited computational resources. We evaluated these models using our codebase and summarized them in the following table:

TODO

• [ ] Use MidiTok for tokenization.
• [ ] Use torchvggish for vggish embeddings.
• [ ] Remove context encoder and use inpainting techniques for segment-by-segment generation, similar to https://github.com/archinetai/audio-diffusion-pytorch.
• [ ] Encoder-free Classifier-Guidance generation with MT3.