deeprhythm 0.0.13

A fast, accurate Tempo Predictor

DeepRhythm: High-Speed Tempo Prediction

DeepRhythm is a convolutional neural network designed for rapid, precise tempo prediction for modern music. It runs on anything that supports Pytorch (I've tested Ubunbu, MacOS, Windows, Raspbian).

Audio is batch-processed using a vectorized Harmonic Constant-Q Modulation (HCQM), drastically reducing computation time by avoiding the usual bottlenecks encountered in feature extraction.

more details here

Classification Process

1. Split input audio into 8 second clips [len_batch, len_audio]
2. Compute the HCQM of each clip
   1. Compute STFT [len_batch, stft_bands, len_audio/hop]
   2. Sum STFT bins into 8 log-spaced bands using filter matrix [len_batch, 8, len_audio/hop]
   3. Flatten bands for parallel CQT processing [len_batch*8, len_audio/hop]
   4. For each of the six harmonics, compute the CQT [6, len_batch*8, num_cqt_bins]
   5. Reshape [len_batch, num_cqt_bins, 8, 6]
3. Feed HCQM through CNN [len_batch, num_classes (256)]
4. Softmax the outputs to get probabilities
5. Choose the class with the highest probability and convert to bpm (bpms = [len_batch])

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)

Installation

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

pip install deeprhythm

Usage

CLI Inference

Single

python -m deeprhythm.infer /path/to/song.wav -cq
> ([bpm], [confidence])

Flags:

• -c, --conf - include confidence scores
• -d, --device [cuda/cpu/mps] - specify model device
• -q, --quiet - prints only bpm/conf

Batch

To predict the tempo of all songs in a directory, run

python -m deeprhythm.batch_infer /path/to/dir

This will create in a jsonl file mapping filepath to predicted BPM.

Flags:

• -o output_path.jsonl - provide a custom output path (default 'batch_results.jsonl`)
• -c, --conf - include confidence scores
• -d, --device [cuda/cpu/mps] - specify model device
• -q, --quiet - doesn't print status / logs

Python Inference

To predict the tempo of a song:

from deeprhythm import DeepRhythmPredictor

model = DeepRhythmPredictor()

tempo = model.predict('path/to/song.mp3')

# to include confidence
tempo, confidence = model.predict('path/to/song.mp3', include_confidence=True)

print(f"Predicted Tempo: {tempo} BPM")

Audio is loaded with librosa, which supports most audio formats.

If you have already loaded your audio with librosa, for example to carry out pre-processing steps, you can predict the tempo in the following way:

import librosa
from deeprhythm import DeepRhythmPredictor

model = DeepRhythmPredictor()

audio, sr = librosa.load('path/to/song.mp3')

# ... other steps for processing the audio ...

tempo = model.predict_from_audio(audio, sr)

# to include confidence
tempo, confidence = model.predict_from_audio(audio, sr, include_confidence=True)

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.