allin1 0.0.8

All-In-One Music Structure Analyzer

All-In-One Music Structure Analysis Model

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NOTE: This is a work in progress

Table of Contents

• Installation
• Usage
• Citation

Installation

1. Install PyTorch

Visit PyTorch and install the appropriate version for your system.

2. Install NATTEN (For Linux and Windows only, not required for macOS)

Linux

Visit NATTEN website and download the appropriate version for your system.

macOS

No need to install NATTEN, it will be installed automatically when installing allin1.

Windows

Build NATTEN from source:

pip install ninja # Recommended, not required
git clone https://github.com/SHI-Labs/NATTEN
cd NATTEN
make

3. Install the package

pip install git+https://github.com/CPJKU/madmom  # install the latest madmom directly from GitHub
pip install allin1  # install this package

4. (Optional) Install FFmpeg for MP3 support

For ubuntu:

sudo apt install ffmpeg

For macOS:

brew install ffmpeg

Usage

CLI

allin1 your_audio_file1.wav your_audio_file2.wav

The result will be saved in `./structures:

./structures
└── your_audio_file1.json
└── your_audio_file2.json

And a JSON analysis result has:

{
  "beats": [ 0.33, 0.75, 1.14, ... ],
  "downbeats": [ 0.33, 1.94, 3.53, ... ],
  "beat_positions": [ 1, 2, 3, 4, 1, 2, 3, 4, 1, ... ],
  "segments": [
    {
      "start": 0.0,
      "end": 0.33,
      "label": "start"
    },
    {
      "start": 0.33,
      "end": 13.13,
      "label": "intro"
    },
    {
      "start": 13.13,
      "end": 37.53,
      "label": "chorus"
    },
    {
      "start": 37.53,
      "end": 51.53,
      "label": "verse"
    },
    ...
  ]
}

Python

import allinone

# You can analyze a single file:
result = allinone.analyze('your_audio_file.wav')

# Or multiple files:
results = allinone.analyze(['your_audio_file1.wav', 'your_audio_file2.wav'])

A result is a dataclass instance containing:

AnalysisResult(
  beats=[0.33, 0.75, 1.14, ...],
  beat_positions=[1, 2, 3, 4, 1, 2, 3, 4, 1, ...],
  downbeats=[0.33, 1.94, 3.53, ...], 
  segments=[
    Segment(start=0.0, end=0.33, label='start'), 
    Segment(start=0.33, end=13.13, label='intro'), 
    Segment(start=13.13, end=37.53, label='chorus'), 
    Segment(start=37.53, end=51.53, label='verse'), 
    Segment(start=51.53, end=64.34, label='verse'), 
    Segment(start=64.34, end=89.93, label='chorus'), 
    Segment(start=89.93, end=105.93, label='bridge'), 
    Segment(start=105.93, end=134.74, label='chorus'), 
    Segment(start=134.74, end=153.95, label='chorus'), 
    Segment(start=153.95, end=154.67, label='end'),
  ]),

Citation

@inproceedings{taejun2023allinone,
  title={All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio},
  author={Kim, Taejun and Nam, Juhan},
  booktitle={IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  year={2023}
}