deeprhythm 0.0.2

A fast, accurate Tempo Predictor

DeepRhythm: High-Speed Tempo Prediction CNN

Introduction

DeepRhythm is a Convolutional Neural Network (CNN) designed for rapid, precise tempo prediction, specifically on modern music.

Audio is batch-processed using a vectorized HCQM, drastically reducing computation time by avoiding the common bottlenecks encountered in feature extraction.

Benchmarks

Method	Acc1 (%)	Acc2 (%)	Avg. Time (s)	Total Time (s)
Essentia (multifeature)	79.15	94.19	2.78	1635.48
Essentia (Percival)	80.51	94.87	1.46	851.91
Essentia (degara)	77.26	91.97	1.40	820.85
Librosa	52.82	63.93	0.51	299.68
DeepRhythm (cpu)	90.77	96.75	0.127	74.43
DeepRhythm (cuda)	90.77	96.75	0.0235	13.74

• Test done on 586 songs, mostly Hip Hop, Electronic, Pop, and Rock
• Acc1 = Prediction within +/- 2% of actual bpm
• Acc2 = Prediction within +/- 2% of actual bpm or a multiple (e.g. 120 ~= 60)

Installation

To install DeepRhythm, ensure you have Python and pip installed. Then run:

git clone https://github.com/Mitchell57/deeprhythm.git
cd deeprhythm
pip install -r requirements.txt

Usage

To predict the tempo of a song with DeepRhythm:

from deeprhythm import DeepRhythmPredictor

predictor = DeepRhythmPredictor()
tempo = model.predict('path/to/song.mp3')
print(f"Predicted Tempo: {tempo} BPM")

References