BYOL for Audio

This is a demo implementation of BYOL for Audio (BYOL-A), a self-supervised learning method for general-purpose audio representation, includes:

  • Training code that can train models with arbitrary audio files.
  • Evaluation code that can evaluate trained models with downstream tasks.
  • Pretrained weights.

If you find BYOL-A useful in your research, please use the following BibTeX entry for citation.

      title={BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation}, 
      author={Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Kunio Kashino},
      booktitle = {2021 International Joint Conference on Neural Networks, {IJCNN} 2021},

Getting Started

  1. Download external source files, and apply a patch. Our implementation uses the following.

    curl -O
    patch < byol_a/byol_pytorch.diff
    mv byol_a
    curl -O
    mv utils
  2. Install PyTorch 1.7.1, torchaudio, and other dependencies listed on requirements.txt.

Evaluating BYOL-A Representations

Downstream Task Evaluation

The following steps will perform a downstream task evaluation by linear-probe fashion.
This is an example with SPCV2; Speech commands dataset v2.

  1. Preprocess metadata (.csv file) and audio files, processed files will be stored under a folder work.

    # usage: python -m utils.preprocess_ds <downstream task> <path to its dataset>
    python -m utils.preprocess_ds spcv2 /path/to/speech_commands_v0.02
  2. Run evaluation. This will convert all .wav audio to representation embeddings first, train a lineaer layer network, then calculate accuracy as a result.

    python pretrained_weights/AudioNTT2020-BYOLA-64x96d2048.pth spcv2

You can also run an evaluation multiple times and take an average result. Following will evaluate on UrbanSound8K with a unit audio duration of 4.0 seconds, for 10 times.

# usage: python <your weight> <downstream task> <unit duration sec.> <# of iteration>
python pretrained_weights/AudioNTT2020-BYOLA-64x96d2048.pth us8k 4.0 10

Evaluating Representations In Your Tasks

This is an example to calculate a feature vector for an audio sample.

from byol_a.common import *
from byol_a.augmentations import PrecomputedNorm
from byol_a.models import AudioNTT2020

device = torch.device('cuda')
cfg = load_yaml_config('config.yaml')

# Mean and standard deviation of the log-mel spectrogram of input audio samples, pre-computed.
# See calc_norm_stats in for your reference.
stats = [-5.4919195,  5.0389895]

# Preprocessor and normalizer.
to_melspec = torchaudio.transforms.MelSpectrogram(
normalizer = PrecomputedNorm(stats)

# Load pretrained weights.
model = AudioNTT2020(d=cfg.feature_d)
model.load_weight('pretrained_weights/AudioNTT2020-BYOLA-64x96d2048.pth', device)

# Load your audio file.
wav, sr = torchaudio.load('work/16k/spcv2/one/00176480_nohash_0.wav') # a sample from SPCV2 for now
assert sr == cfg.sample_rate, "Let's convert the audio sampling rate in advance, or do it here online."

# Convert to a log-mel spectrogram, then normalize.
lms = normalizer((to_melspec(wav) + torch.finfo(torch.float).eps).log())

# Now, convert the audio to the representation.
features = model(lms.unsqueeze(0))

Training From Scratch

You can also train models. Followings are an example of training on FSD50K.

  1. Convert all samples to 16kHz. This will convert all FSD50K files to a folder work/16k/fsd50k while preserving folder structure.

    python -m utils.convert_wav /path/to/fsd50k work/16k/fsd50k
  2. Start training, this example trains with all development set audio samples from FSD50K.

    python work/16k/fsd50k/FSD50K.dev_audio

Refer to Table VI on our paper for the performance of a model trained on FSD50K.

Pretrained Weights

We include 3 pretrained weights of our encoder network.

Method Dim. Filename NSynth US8K VoxCeleb1 VoxForge SPCV2/12 SPCV2 Average
BYOL-A 512-d AudioNTT2020-BYOLA-64x96d512.pth 69.1% 78.2% 33.4% 83.5% 86.5% 88.9% 73.3%
BYOL-A 1024-d AudioNTT2020-BYOLA-64x96d1024.pth 72.7% 78.2% 38.0% 88.5% 90.1% 91.4% 76.5%
BYOL-A 2048-d AudioNTT2020-BYOLA-64x96d2048.pth 74.1% 79.1% 40.1% 90.2% 91.0% 92.2% 77.8%


This implementation is for your evaluation of BYOL-A paper, see LICENSE for the detail.


BYOL-A is built on top of byol-pytorch, a BYOL implementation by Phil Wang (@lucidrains). We thank Phil for open-source sophisticated code.

  author =       {Phil Wang},
  title =        {Bootstrap Your Own Latent (BYOL), in Pytorch},
  howpublished = {\url{}},
  year =         {2020}