Music genre classification is a task to classify audio clips into genres such as jazz, classical, etc. GTZAN is a music genre dataset containing 1000 30-second audio clips with 10 genres. In this codebase, we fine-tune PANNs [1] to build music classification systems.

The dataset can be downloaded from http://marsyas.info/downloads/datasets.html

0. Prepare data

Download and upzip data, the data looks like:

dataset_root
├── blues (100 files)
├── classical (100 files)
├── country (100 files)
├── disco (100 files)
├── hiphop (100 files)
├── jazz (100 files)
├── metal (100 files)
├── pop (100 files)
├── reggae (100 files)
└── rock (100 files)

1. Requirements

python 3.6 + pytorch 1.0

2. Then simply run:

$ Run the bash script ./runme.sh

Or run the commands in runme.sh line by line. The commands includes:

(1) Modify the paths of dataset and your workspace

(2) Extract features

(3) Train model

A 14-layer CNN of PANNs is fine-tuned. We use 10-fold cross validation for GTZAN classification. That is, 900 audio clips are used for training, and 100 audio clips are used for validation.

The system takes around 30 minutes to converge with a single card Tesla Tesla-V100 GPU card. Here is the result on 2nd fold. The results on different folds can be different.

Namespace(augmentation='mixup', batch_size=32, cuda=True, dataset_dir='/home/tiger/datasets/GTZAN/dataset_root', filename='main', freeze_base=False, holdout_fold='2', learning_rate=0.0001, loss_type='clip_nll', mode='train', model_type='Transfer_Cnn14', pretrained_checkpoint_path='/home/tiger/released_models/sed/Cnn14_mAP=0.431.pth', resume_iteration=0, stop_iteration=10000, workspace='workspaces/panns_transfer_to_gtzan')
Using GPU.
Load pretrained model from /home/tiger/released_models/sed/Cnn14_mAP=0.431.pth
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Iteration: 200
Validate accuracy: 0.780
    Dump statistics to /home/tiger/workspaces/panns_transfer_to_gtzan/statistics/main/holdout_fold=2/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=mixup/batch_size=32/freeze_base=False/statistics.pickle
    Dump statistics to /home/tiger/workspaces/panns_transfer_to_gtzan/statistics/main/holdout_fold=2/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=mixup/batch_size=32/freeze_base=False/statistics_2020-07-12_16-53-42.pkl
Sun, 12 Jul 2020 16:57:55 main.py[line:165] INFO Train time: 244.052 s, validate time: 3.158 s
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...
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Iteration: 2000
Validate accuracy: 0.890
    Dump statistics to /home/tiger/workspaces/panns_transfer_to_gtzan/statistics/main/holdout_fold=2/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=mixup/batch_size=32/freeze_base=False/statistics.pickle
    Dump statistics to /home/tiger/workspaces/panns_transfer_to_gtzan/statistics/main/holdout_fold=2/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=mixup/batch_size=32/freeze_base=False/statistics_2020-07-12_16-53-42.pkl
Train time: 234.912 s, validate time: 4.188 s
Model saved to /home/tiger/workspaces/panns_transfer_to_gtzan/checkpoints/main/holdout_fold=2/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=mixup/batch_size=32/freeze_base=False/2000_iterations.pth
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...

[1] Kong, Qiuqiang, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D. Plumbley. "PANNs: Large-scale pretrained audio neural networks for audio pattern recognition." arXiv preprint arXiv:1912.10211 (2019).