deeprhythm 0.0.9

A fast, accurate Tempo Predictor

DeepRhythm: High-Speed Tempo Prediction

Introduction

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

The implementation is heavily inspired by [1].

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)
DeepRhythm (cuda)	95.91	96.54	0.021	20.11
DeepRhythm (cpu)	95.91	96.54	0.12	115.02
TempoCNN (cnn)	84.78	97.69	1.21	1150.43
TempoCNN (fcn)	83.53	96.54	1.19	1131.51
Essentia (multifeature)	87.93	97.48	2.72	2595.64
Essentia (percival)	85.83	95.07	1.35	1289.62
Essentia (degara)	86.46	97.17	1.38	1310.69
Librosa	66.84	75.13	0.48	460.52

• Test done on 953 songs, mostly Electronic, Hip Hop, Pop, and Rock
• Acc1 = Prediction within +/- 2% of actual bpm
• Acc2 = Prediction within +/- 2% of actual bpm or a multiple (e.g. 120 ~= 60)
• Timed from filepath in to bpm out (audio loading, feature extraction, model inference)
• I could only get TempoCNN to run on cpu (it requires Cuda 10 and I'm not downgrading my Cuda install for curiosity's sake)

Installation

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

pip install deeprhythm

Usage

To predict the tempo of a song with DeepRhythm:

from deeprhythm import DeepRhythmPredictor

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

References

[1] Hadrien Foroughmand and Geoffroy Peeters, “Deep-Rhythm for Global Tempo Estimation in Music”, in Proceedings of the 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, Nov. 2019, pp. 636–643. doi: 10.5281/zenodo.3527890.

[2] K. W. Cheuk, H. Anderson, K. Agres and D. Herremans, "nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 161981-162003, 2020, doi: 10.1109/ACCESS.2020.3019084.