amalad / Multi-Scale-Music-Tagger

A Multi Scale Convolutional Neural Network Architecture for Music Auto-Tagging

Multi Scale Music Tagger

A Multi Scale Convolutional Neural Network Architecture for Music Auto-Tagging

A PyTorch implementation of the model developed for the Study Oriented Project Course (CS F266) at BITS Pilani - Hyderabad Campus, Jan 2017 - April 2017.

Project by: Amala Deshmukh and Tanmaya Dabral

Requirements

1. Python 3.6
2. PyTorch 0.4.1
3. Librosa 0.6.2 (Required for generating spectrograms)

Check "requirements.txt".

Downloading and Processing the Data:

We used the MagnaTagATune dataset for our experiments. The dataset can be downloaded from here and should be stored in the "data" subfolder of the repository. After downloading the mp3.zip.* files, execute the following commands to generate the raw data: cat mp3.zip.* > mp3_all.zip, followed by unzip mp3_all.zip

To generate spectrograms from the raw audio clips, run the "generate_python.py" script. This will create the following files in the data subfolder: "Spectrogram.data" and "annotations_cleaned.pickle".

We consider only the top 50 tags (can be changed in "config.py") for classification as tag frequencies are extremely skewed (See this).

Furthermore, we merge the following synonym tags, as proposed here:

synonyms = [['beat', 'beats'],
			['chant', 'chanting'],
			['choir', 'choral'],
			['classical', 'clasical', 'classic'],
			['drum', 'drums'],
			['electro', 'electronic', 'electronica', 'electric'],
			['fast', 'fast beat', 'quick'],
			['female', 'female singer', 'female singing', 'female vocals', 'female voice', 'woman', 'woman singing', 'women'],
			['flute', 'flutes'],
			['guitar', 'guitars'],
			['hard', 'hard rock'],
			['harpsichord', 'harpsicord'],
			['heavy', 'heavy metal', 'metal'],
			['horn', 'horns'],
			['india', 'indian'],
			['jazz', 'jazzy'],
			['male', 'male singer', 'male vocal', 'male vocals', 'male voice', 'man', 'man singing', 'men'],
			['no beat', 'no drums'],
			['no singer', 'no singing', 'no vocal','no vocals', 'no voice', 'no voices', 'instrumental'],
			['opera', 'operatic'],
			['orchestra', 'orchestral'],
			['quiet', 'silence'],
			['singer', 'singing'],
			['space', 'spacey'],
			['string', 'strings'],
			['synth', 'synthesizer'],
			['violin', 'violins'],
			['vocal', 'vocals', 'voice', 'voices'],
			['strange', 'weird']]

Training the model:

To train the model, run the following command: python train_music_tagger.py.

The training parameters can be modified in "config.py".

Results:

We evaluated our model using the average ROC AUC score. The data was split randomly in the ratio of 13:1:3 (train:validation:test) over the entire dataset. The model weights giving the best AUC score on the validation set for 100 epochs were saved to "data/trained_model_state_dict.pth" and the test score was calculated on these weights. The scores for one of our runs are reported below:

Best validation AUC score: 0.91590

Corresponding training cost: 0.11139

Corresponding test AUC score: 0.90936

Note: Since we're splitting the dataset randomly in our "train_music_tagger.py" script, these scores may vary on each run. For most of our runs, the best validation score and corresponding test score were both between 0.90 and 0.915.

To Do:

We intend to modify our model a little bit and test it on the Million Songs Dataset. Will post results soon.

Contact:

Feel free to ping me if you need the pretrained weights, the training logs for the reported scores or if you have any questions about the model.