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DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks

This repo contains code for running DrumGAN, a Generative Adversarial Network that synthesizes drum sounds offering control over prcetpual features. You can find details about the specific architecture and the experiment in our ISMIR paper. Some of the codes are borrowed from Facebook's GAN zoo repo.

Notes

THIS REPO IS NOT UP TO DATE YET! Please, come back later. Sorry for the inconvenience.

Install

  1. Install requirements:
pip install -r requirements.txt
  1. In order to compute the Fréchet Audio Distance (FAD) download and install google AI repo following the instructions here

The dataset

We train our model on a private, non-publicly available dataset containing 300k sounds of drum sounds equally distributed across kicks, snares and cymbals. This repo contains code for training a model on your own data. You will have to create a data loader, specific to the structure of your own dataset.

Training a new model

Train a new model from the module's root folder by executing:

python train.py $ARCH -c $PATH/TO/CONFIG/FILE

Available architectures:

Example of config file:

The experiments are defined in a configuration file with JSON format.

{
    "name": "mag-if_test_config",
    "comments": "dummy configuration",
    "output_path": "/path/to/output/folder",
    "loader_config": {
        "dbname": "nsynth",
        "data_path": "/path/to/nsynth/audio/folder",
        "attribute_file": "/path/to/nsynth/examples.json",
        "filter_attributes": {
            "instrument_family_str": ["brass", "guitar", "mallet", "keyboard"],
            "instrument_source_str": ["acoustic"]
        },
        "shuffle": true,
        "attributes": ["pitch", "instrument_family_str"],
        "balance_att": "instrument_family_str",
        "pitch_range": [44, 70],
        "load_metadata": true,
        "size": 1000
    },
        
    "transform_config": {
        "transform": "specgrams",
        "fade_out": true,
        "fft_size": 1024,
        "win_size": 1024,
        "n_frames": 64,
        "hop_size": 256,
        "log": true,
        "ifreq": true,
        "sample_rate": 16000,
        "audio_length": 16000
    },
    "model_config": {
        "formatLayerType": "default",
        "ac_gan": true,
        "downSamplingFactor": [
            [16, 16],
            [8, 8],
            [4, 4],
            [2, 2],
            [1, 1]
        ],
        "maxIterAtScale": [
            50,
            50,
            50,
            50,
            50
        ],
        "alphaJumpMode": "linear",
        "alphaNJumps": [
            600,
            600,
            600,
            600,
            1200
        ],
        "alphaSizeJumps": [
            32,
            32,
            32,
            32,
            32
        ],
        "transposed": false,
        "depthScales": [
            5,
            5,
            5,
            5,
            5
        ],
        "miniBatchSize": [
            2,
            2,
            2,
            2,
            2
        ],
        "dimLatentVector": 2,
        "perChannelNormalization": true,
        "lossMode": "WGANGP",
        "lambdaGP": 10.0,
        "leakyness": 0.02,
        "miniBatchStdDev": true,
        "baseLearningRate": 0.0006,
        "dimOutput": 1,
        "weightConditionG": 10.0,
        "weightConditionD": 10.0,
        "attribKeysOrder": {
            "pitch": 0,
            "instrument_family": 1
        },
        "startScale": 0,
        "skipAttDfake": []
    }
}

Evaluation

You can run the evaluation metrics described in the paper: Inception Score (IS), Kernel Inception Distance (KID), and the Fréchet Audio Distance (FAD).

  • For computing Inception Scores run:
python eval.py <pis or iis> --fake <path_to_fake_data> -d <output_path>
  • For distance-like evaluation run:
python eval.py <pkid, ikid or fad> --real <path_to_real_data> --fake <path_to_fake_data> -d <output_path>

Synthesizing audio with a model

python generate.py <random, scale, radial_interpolation, spherical_interpolation, or from_midi> -d <path_to_model_root_folder>

Audio examples

Here you can listen to audios synthesized with DrumGAN under different conditonal settings.