A platform for visualizing, analyzing and generating music and audio with deep learning.
The name's a portmanteau of a bunch of words like Audio, Audience and Ambiance.
Python 3.8 and Pytorch 1.12.1+cu116
Developed on an RTX 3070, so expect a maximum of 8 GB VRAM needed for all computation. Will note if I spend more on an instance and the VRAM requirements increase.
See requirements.txt for other python libraries used..
- Visualize Music
- As Waveplots
- As Spectrograms
- As MFCCs
- With Embeddings
- Implement Datasets
- GTZAN
- FMA
- MTG
- Predict Music Genres
- Inference with Pretrained Model
- Train from Scratch
- Within existing classes
- Zero-Shot
- Recommend Similar Music
- Based on Visual embeddings
- Based on Audio embeddings
- Generate new Music
- GAN-Based Model
- Diffusion-Based Model
- Music2Music
- Extend from Generation models
| argument | description | possible values |
|---|---|---|
| -t | b | d |
Run scripts/train.py
tbd
tbd
tbd
| Dataset | URL | Notes |
|---|---|---|
| GTZAN | Dataset on Kaggle | Yes, it's not a great dataset but it's a classic one. Train/Test/Val Split was done manually but I may add a splitting script later |
Somewhat tentative and may change in the future.
│
├─── data\
│ ├─── gtzan\
│ └─── other datasets\
│
├─── datasets\
│ ├─── preprocessing\
│ │ ├─── __init__.py
│ │ └─── transforms.py
│ ├─── __init__.py
│ └─── gtzan_dataset.py
│
├─── models\
│ ├─── classify\
│ │ ├─── __init__.py
│ │ ├─── custom_mlp.py
│ │ └─── pretrained_model.py
│ └─── generate\
│ ├─── __init__.py
│ └─── coming_soon.py
│
├─── output\
│ ├─── results\
│ └─── saved_models\
│
├─── trainers\
│ ├─── __init__.py
│ └─── classification_trainer.py
│
├─── scripts\
│ ├─── evaluate.py
│ └─── train.py
│
├─── utils\
│ ├─── plotting.py
│ ├─── preprocess_data.py
│ ├─── process_imgs.py
│ ├─── regenerate_audio.py
│ └─── visualize_audio.py
│
├─── .gitignore
├─── main.py
├─── README.md
└─── requirements.txt
└─── gtzan\
├─── train\
│ ├─── audio\
│ │ ├─── [genres]
│ │ │ ├─── [songs].wav
│ └─── image\
│ │ ├─── [genres]
│ │ │ ├─── [songs].png
├─── test\
│ ├─── <identical to train>
└─── val\
├─── <identical to train>