Masked Modeling Duo (M2D)

This repository provides demo implementations of our paper "Masked Modeling Duo: Towards a Universal Audio Pre-training Framework", "Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input", and so on.

Pre-trained/Fine-tuned Weights

AudioSet pre-trained weights

Weight	Recommendation	Description	Fur-PT Ready	AS2M mAP
m2d_as_vit_base-80x1001p16x16-240213_AS-FT_enconly	Best for audio tagging (AT) / sound event detection (SED).	M2D-AS fine-tuned on AS2M	N/A	0.485
m2d_vit_base-80x1001p16x16-221006-mr7_as_46ab246d	2nd best for AT/SED.	M2D/0.7 fine-tuned on AS2M	N/A	0.479
m2d_vit_base-80x200p16x4-230529	General-purpose transfer learning and further pre-training w/ finer time frame.	M2D/0.7 (t.f. 40ms)	✅	-
m2d_as_vit_base-80x608p16x16-240213	General-purpose transfer learning and further pre-training, especially when application data is closer to the AudioSet ontology.	M2D-AS	✅	-
m2d_vit_base-80x608p16x16-221006-mr7	General-purpose transfer learning and further pre-training.	M2D/0.7	✅	-
m2d_vit_base-80x608p16x16-221006-mr6	General-purpose transfer learning and further pre-training.	M2D/0.6	✅	-
m2d_vit_base-80x608p16x16-221006-mr7_enconly	General-purpose transfer learning. (Encoder only)	M2D/0.7	N/A	-
m2d_vit_base-80x608p16x16-220930-mr7_enconly	General-purpose transfer learning. (Encoder only)	M2D/0.7	N/A	-

Weight	Recommendation	Description	Fur-PT Ready	AS2M mAP
m2d_as_vit_base-80x1001p16x16p32k-240413_AS-FT_enconly	Best for audio tagging (AT) / sound event detection (SED) at 32 kHz.	M2D-AS fine-tuned on AS2M@32kHz	N/A	0.480
m2d_as_vit_base-80x608p16x16p32k-240413_enconly	General-purpose transfer learning at 32 kHz. (Encoder only)	M2D-AS@32kHz	N/A	-

LibriSpeech pre-trained weights

Weight	Recommendation	Description	Fur-PT Ready	AS2M mAP
m2d_s_vit_base-80x608p80x2-230220	Speech transfer learning and further pre-training.	M2D-S/0.6 6-s input	✅	-
m2d_s_vit_base-80x512p80x2-230301	Speech transfer learning and further pre-training.	M2D-S/0.6 5-s input	✅	-
m2d_s_vit_base-80x400p80x2-230201	Speech transfer learning and further pre-training.	M2D-S/0.6 4-s input	✅	-

Quick Start

Description	Notebook
Audio tagging example	examples/Colab_M2D_example_Tagging.ipynb
Feature extraction example	(TBD)

You can use simple code to load an M2D model and encode your audio.

# Create a model and load pre-trained weights.
from examples.portable_m2d import PortableM2D  # The portable_m2d is a simple one-file loader.
model = PortableM2D('m2d_as_vit_base-80x1001p16x16-240213_AS-FT_enconly/weights_ep69it3124-0.48494.pth')

# Prepare test audios. (a dummy example of three 10-s waveforms)
import torch
batch_audio = 2 * torch.rand((3, 10 * 16000)) - 1.0 # input range = [-1., 1]

# Encode raw audio into frame-level features.
frame_level = model(batch_audio)
print(frame_level.shape)  # torch.Size([3, 63, 3840]). 3 frame-level 3840-d feature vectors for 63 time frames.

# Make clip-level features by averaging frame-level features along time frames.
clip_level = torch.mean(frame_level, dim=1)
print(clip_level.shape)  # torch.Size([3, 3840])

Application Resources

👉 Application Guide (alpha) is available. -- Our guidelines may provide useful information on how to plan further pre-train your models.

• 👉 Resources for M2D-X medical applications (ICBHI2017/SPRSound), further pre-training examples.
• 👉 Resources for M2D medical application (CirCor DigiScope heart sound).
• 👉 Resources for M2D-AS (M2D-X specialized in AudioSet).
• 👉 Resources for M2D-S (M2D-X specialized in Speech).
• TBD Preparing one more thing.

A schematic illustration of M2D-X further pre-training:

1. Setup

The repository is based on the codes from facebookresearch/mae, and we patch our changes on these files.

1. Download external source files and apply a patch.

   git clone https://github.com/nttcslab/m2d.git
   cd m2d
   curl -o util/lars.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/lars.py
   curl -o util/lr_decay.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/lr_decay.py
   curl -o util/lr_sched.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/lr_sched.py
   curl -o util/misc.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/misc.py
   curl -o util/analyze_repr.py https://raw.githubusercontent.com/daisukelab/general-learning/master/SSL/analyze_repr.py
   curl -o m2d/pos_embed.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py
   curl -o train_audio.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/main_pretrain.py
   curl -o speech/train_speech.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/main_pretrain.py
   curl -o audioset/train_as.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/main_pretrain.py
   curl -o mae_train_audio.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/main_pretrain.py
   curl -o m2d/engine_pretrain_m2d.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/engine_pretrain.py
   curl -o m2d/models_mae.py https://raw.githubusercontent.com/facebookresearch/mae/efb2a8062c206524e35e47d04501ed4f544c0ae8/models_mae.py
   curl -o m2d/timm_layers_pos_embed.py https://raw.githubusercontent.com/huggingface/pytorch-image-models/e9373b1b925b2546706d78d25294de596bad4bfe/timm/layers/pos_embed.py
   patch -p1 < patch_m2d.diff

2. Install external modules listed on requirements.txt.

   pip install -r requirements.txt

2. Evaluating M2D

We use the EVAR for our evaluation.

2-1. Setup EVAR

EVAR is an evaluation package for audio representations used by our research papers such as BYOL-A.

The following steps setup EVAR.

1. In the folder of your copy of the M2D repository, clone the EVAR repository and prepare basic items.

   (cd to your M2D folder)
   git clone https://github.com/nttcslab/eval-audio-repr.git evar
   cd evar
   curl https://raw.githubusercontent.com/daisukelab/general-learning/master/MLP/torch_mlp_clf2.py -o evar/utils/torch_mlp_clf2.py
   curl https://raw.githubusercontent.com/daisukelab/sound-clf-pytorch/master/for_evar/sampler.py -o evar/sampler.py
   curl https://raw.githubusercontent.com/daisukelab/sound-clf-pytorch/master/for_evar/cnn14_decoupled.py -o evar/cnn14_decoupled.py
   cd ..

2. Setup downstream task datasets according to Preparing-datasets.md. The following is an example for setting up CREMA-D dataset.

   cd evar
   python evar/utils/download_cremad.py downloads/cremad
   python prepare_wav.py downloads/cremad work/16k/cremad 16000
   cd ..

2-2. Linear Evaluation

Once you setup the EVAR, you can evaluate your models as follows.

• For evaluating a model with an absolute path /your/path/to/model.pth.

  cd evar
  python lineareval.py config/m2d.yaml cremad weight_file=/your/path/to/model.pth

• If you want to save GPU memory, set a fewer batch size as follows. This example sets it as 16.

  cd evar
  python lineareval.py config/m2d.yaml cremad batch_size=16,weight_file=/your/path/to/model.pth

We used the all_eval.sh script to evaluate on all downstream tasks.

2-3. Fine-tuning

We have fin-tuned our models using the scripts in the util folder.

The following examples will fine-tune on each task for three times with the random seed 43, 44, and 45, and the m2d_vit_base-80x608p16x16-221006-mr7/checkpoint-300.pth will be tested. Replace the /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 to yours.

cd evar
bash (your m2d)/util/ft-as2m.sh /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 3 42 300
bash (your m2d)/util/ft-as0k.sh /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 3 42 300
bash (your m2d)/util/ft-esc50.sh /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 3 42 300
bash (your m2d)/util/ft-spc.sh /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 3 42 300
bash (your m2d)/util/ft-vc1.sh /your/path/to/m2d_vit_base-80x608p16x16-221006-mr7 3 42 300

NOTE: Please set your data path in the util/ft-as2m.sh

The ft-as2m.sh requires the path to your log-mel spectrogram AudioSet samples in .npy, configure the script with yours.

3. Pre-training From Scratch

3-1. Prepare pre-training data samples

The pre-trainer (e.g., train_audio.py for audio) loads data from the data folder by default (--data_path), using a list of samples in a CSV data/files_audioset.csv by default (--dataset). Follow the steps in data/README.md.

The following is an example using the FSD50K dataset.

1. Preprocess .wav files into log-mel spectrogram .npy files. The following converts from a source folder /your/local/fsd50k/FSD50K.dev_audio to a new folder data/fsd50k_lms.

   python wav_to_lms.py /your/local/fsd50k/FSD50K.dev_audio data/fsd50k_lms

2. Create a CSV file that will be used as a list of pre-training samples, containing a single column file_name. The following example creates files_f_s_d_5_0_k.csv.

   echo file_name > data/files_f_s_d_5_0_k.csv
   (cd data && find fsd50k_lms/FSD50K.dev_audio -name "*.npy") >> data/files_f_s_d_5_0_k.csv

Example of created folder structure:

data/
    files_f_s_d_5_0_k.csv
    fsd50k_lms/
        FSD50K.dev_audio/
            2931.npy
            408195.npy
                :

3-2. Start pre-training

Once your data is ready, start pre-training as follows.

python train_audio.py --dataset data/files_fssd50k.csv

3-3. Evalidation during and after the training

The training loop automatically evaluates the pre-trained model.

• During pre-training, train_audio.py runs a script called quick_eval.sh as a sub-process. You can edit quick_eval.sh for your purposes.
• When the pre-training is finished, the final evaluation script all_eval.sh is executed.

3-4. Complete pre-training command lines

The command lines for pre-training full-performance models follow:

# M2D
OMP_NUM_THREADS=1 torchrun --nproc_per_node=4 -m train_audio --input_size 80x608 --patch_size 16x16 --epochs 300 --batch_size 512 --accum_iter 1 --save_freq 50 --seed 3 --model m2d_vit_base --csv_main data/files_audioset.csv --data_path /path/to/your/data --loss_off 0.
# M2D-AS
OMP_NUM_THREADS=1 torchrun --nproc_per_node=4 -m audioset.train_as --input_size 80x608 --patch_size 16x16 --epochs 300 --batch_size 512 --accum_iter 1 --save_freq 50 --seed 3 --data_path /path/to/your/data --loss_off 1.

Example logs are available: example_logs.zip.

We explain the details in the Guide_app.md.

4. Other Pre-trained/fine-tuned Weights

Please find all pre-trained/fine-tuned weights published on the releases.

5. License

See LICENSE.pdf for details.

Citations

If you find our M2D useful in your research, please consider citing our papers.

@article{niizumi2024m2dx,
    title   = {{Masked Modeling Duo: Towards a Universal Audio Pre-training Framework}},
    author  = {Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Kunio Kashino},
    journal = {IEEE/ACM Trans. Audio, Speech, Language Process.},
    year    = {2024},
    volume  = {32},
    pages   = {2391-2406},
    url     = {https://ieeexplore.ieee.org/document/10502167},
    doi     = {10.1109/TASLP.2024.3389636}}

@inproceedings{niizumi2023m2d,
    title   = {{Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input}},
    author  = {Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Kunio Kashino},
    booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
    year    = {2023},
    url     = {https://ieeexplore.ieee.org/document/10097236},
    doi     = {10.1109/ICASSP49357.2023.10097236}}

@inproceedings{niizumi2023m2d4speech,
    title   = {{Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation}},
    author  = {Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Kunio Kashino},
    year    = {2023},
    booktitle={Proc. INTERSPEECH 2023},
    pages   = {1294--1298},
    doi     = {10.21437/Interspeech.2023-221}}

@article{niizumi2024embc,
    title   = {{Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection}},
    author  = {Daisuke Niizumi and Daiki Takeuchi and Yasunori Ohishi and Noboru Harada and Kunio Kashino},
    journal = {to appear at IEEE EMBC},
    year    = {2024},
    url     = {https://arxiv.org/abs/2404.17107}}

Acknowledgements

• Our code is based on the MAE PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners.
• We use nnAudio (KinWaiCheuk/nnAudio) for converting raw audio into log-mel spectrogram.

We appreciate these publicly available implementations and all the modules our experiments heavily depend on!

References

• MAE: Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick "Masked Autoencoders Are Scalable Vision Learners," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 16000-16009.
• FSD50K: Eduardo Fonseca and Xavier Favory and Jordi Pons and Frederic Font and Xavier Serra, “FSD50K: an Open Dataset of Human-Labeled Sound Events,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 829-852, 2022.
• MSM-MAE: Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, and Kunio Kashino "Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation," HEAR: Holistic Evaluation of Audio Representations (NeurIPS 2021 Competition), PMLR 166:1-24, 2022.