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PaSST: Efficient Training of Audio Transformers with Patchout

This is the implementation for Efficient Training of Audio Transformers with Patchout

Patchout significantly reduces the training time and GPU memory requirements to train transformers on audio spectrograms, while improving their performance.

Patchout works by dropping out some of the input patches during training. In either an unstructured way (randomly, similar to dropout), or entire time-frames or frequency bins of the extracted patches (similar to SpecAugment), which corresponds to rows/columns in step 3 of the figure below.

Table of contents

• Pre-trained models for Inference and embeddings extractions
  • Getting the logits from the pretrained models
  • Getting a pre-trained model for fine-tuning
• Development environment
  • Setting up the development experiments environment
  • Setting up using the exported conda environment
  • Checking the environment
• Getting started
  • General information
  • Configuring the experiment
• Training on Audioset
• Examples with Pre-trained models
• Examples fine-tuning on downstream datasets
• Citation
• Contact

Pre-trained models for Inference and embeddings extractions

If you only want to use the embeddings generated by the pretrained models, use your own fine-tuning framework, or you need it only for inference, you can find a stripped down version of this repo here. The package follows HEAR 2021 NeurIPS Challenge API, and can be installed:

pip install hear21passt

This repo is a complete framework for training the models and fine-tuning pre-trained models on Audioset on downstream tasks.

Getting the logits from the pretrained models

from hear21passt.base import get_basic_model,get_model_passt
import torch
# get the PaSST model wrapper, includes Melspectrogram and the default pre-trained transformer
model = get_basic_model(mode="logits")
print(model.mel) # Extracts mel spectrogram from raw waveforms.
print(model.net) # the transformer network.

# example inference
model.eval()
model = model.cuda()
with torch.no_grad():
    # audio_wave has the shape of [batch, seconds*32000] sampling rate is 32k
    # example audio_wave of batch=3 and 10 seconds
    audio = torch.ones((3, 32000 * 10))*0.5
    audio_wave = audio.cuda()
    logits=model(audio_wave) 

Getting a pre-trained model for fine tuning

from hear21passt.base import get_basic_model,get_model_passt
import torch
# get the PaSST model wrapper, includes Melspectrogram and the default pre-trained transformer
model = get_basic_model(mode="logits")
print(model.mel) # Extracts mel spectrogram from raw waveforms.

# optional replace the transformer with one that has the required number of classes i.e. 50
model.net = get_model_passt(arch="passt_s_swa_p16_128_ap476",  n_classes=50)
print(model.net) # the transformer network.


# now model contains mel + the transformer pre-trained model ready to be fine tuned.
# It's still expecting input of the shape [batch, seconds*32000] sampling rate is 32k

model.train()
model = model.cuda()

Development environment

If you want to use the same environment as in the paper, you can follow the instructions below.

Setting up the development experiments environment

For training models from scratch or fine-tuning using the same setup as in the paper:

1. If needed, create a new environment with python 3.8 and activate it:

conda create -n passt python=3.8
conda activate passt

1. Install pytorch build that suits your system. For example:

conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

1. Install the requirements:

pip install -r requirements.txt

Setting up using the exported conda environment

Alternatively, you can use the exported conda environment environment.yml to create the environment.

For setting up Mamba is recommended since it works faster than conda:

conda install mamba -n base -c conda-forge

Now you can import the environment from environment.yml

mamba env create -f environment.yml

Now you have an environment named ba3l.

Checking the environment

In order to check if your environment matched the environment we used in our runs, please check the environment.yml and pip_list.txt files, which were exported using:

conda env export --no-builds | grep -v "prefix" > environment.yml
pip list > pip_list.txt

Getting started

If you want to use your setup and only use the models from this repo, you can get the models train them from scratch or fine-tune them on your own dataset as explained above Pre-trained models for Inference and embeddings extractions. The rest of this section explains using this repo for training and fine-tuning the models. For that, first you need to set up the development environment as explained above.

General information

The repo is built using sacred for experiment management and configuration, pytorch-lightning for training, and wandb for logging.

Each dataset has a main experiment file such as ex_audioset.py and ex_openmic.py and a dataset folder. The experiment file contains the main training and validation logic. The dataset folder contains the dataset specific code needed to download, preprocess and load the dataset for training.

In general, you can prob the experiment file for help, this will print the available commands and basic options:

python ex_audioset.py help

Configuring the experiment

Each experiment has a set of default configuration options, defined in the experiment file, e.g. ex_audioset.py. You can override any of the configuration using the sacred syntax. You can use the print_config command to print the configuration values without training a model:

 python ex_audioset.py print_config

You can use then use the command line interface to override any of the configuration options (sacred syntax), using with e.g.:

python ex_audioset.py with trainer.precision=16 

This will train on Audioset using 16-bit precision.

The overall configurations look like this:

  ...
  seed = 542198583                  # the random seed for this experiment
  slurm_job_id = ''
  speed_test_batch_size = 100
  swa = True
  swa_epoch_start = 50
  swa_freq = 5
  use_mixup = True
  warm_up_len = 5
  weight_decay = 0.0001
  basedataset:
    base_dir = 'audioset_hdf5s/'     # base directory of the dataset, change it or make a link
    eval_hdf5 = 'audioset_hdf5s/mp3/eval_segments_mp3.hdf'
    wavmix = 1
    ....
    roll_conf:
      axis = 1
      shift = None
      shift_range = 50
  datasets:
    test:
      batch_size = 20
      dataset = {CMD!}'/basedataset.get_test_set'
      num_workers = 16
      validate = True
    training:
      batch_size = 12
      dataset = {CMD!}'/basedataset.get_full_training_set'
      num_workers = 16
      sampler = {CMD!}'/basedataset.get_ft_weighted_sampler'
      shuffle = None
      train = True
  models:
    mel:
      freqm = 48
      timem = 192
      hopsize = 320
      htk = False
      n_fft = 1024
      n_mels = 128
      norm = 1
      sr = 32000
      ...
    net:
      arch = 'passt_s_swa_p16_128_ap476'
      fstride = 10
      in_channels = 1
      input_fdim = 128
      input_tdim = 998
      n_classes = 527
      s_patchout_f = 4
      s_patchout_t = 40
      tstride = 10
      u_patchout = 0
      ...
  trainer:
    accelerator = None
    accumulate_grad_batches = 1
    amp_backend = 'native'
    amp_level = 'O2'
    auto_lr_find = False
    auto_scale_batch_size = False
    ...

There are many things that can be updated from the command line. In short:

• All the configuration options under trainer are pytorch lightning trainer api. For example, to turn off cuda benchmarking add trainer.benchmark=False to the command line.
• wandb is the wandb configuration. For example, to change the wandb project wandb.project="test_project" to the command line.
• models.net are the PaSST (or the chosen NN) options. Examples: models.net.u_patchout, models.net.s_patchout_f models.net.s_patchout_t control the unstructured patchout and structured patchout over frequency and time. input_fdim and input_tdim are the input spectrogram dimensions over frequency and time. models.net.fstride and models.net.tstride are the strides of the input patches over frequency and time, setting these to 16 means no patch overlap.
• models.mel are the preprocessing options (mel spectrograms). mel.sr is the sampling rate, mel.hopsize is the hop size of the STFT window, mel.n_mels is the number of mel bins, mel.freqm and mel.timem are the frequency and time masking parameters of spec-augment.

There are many pre-defined configuration bundles (called named_configs) in config_updates.py. These include different models, setups etc... You can list these configurations with:

python ex_audioset.py print_named_configs

For example, passt_s_20sec is a configuration bundle that sets the model to PaSST-S pre-trained on Audioset, and accepts up to 20 second clips.

Training on Audioset

Download and prepare the dataset as explained in the audioset page

The base PaSST model can be trained for example like this:

python ex_audioset.py with trainer.precision=16  models.net.arch=passt_deit_bd_p16_384 -p

For example using only unstructured patchout of 400:

python ex_audioset.py with trainer.precision=16  models.net.arch=passt_deit_bd_p16_384  models.net.u_patchout=400  models.net.s_patchout_f=0 models.net.s_patchout_t=0 -p

Multi-gpu training can be enabled by setting the environment variable DDP, for example with 2 gpus:

 DDP=2 python ex_audioset.py with trainer.precision=16  models.net.arch=passt_deit_bd_p16_384 -p -c "PaSST base 2 GPU"

Examples with Pre-trained models

Please check the releases page, to download pre-trained models. In general, you can get a pretrained model on Audioset using

from models.passt import get_model
model  = get_model(arch="passt_s_swa_p16_128_ap476", pretrained=True, n_classes=527, in_channels=1,
                   fstride=10, tstride=10,input_fdim=128, input_tdim=998,
                   u_patchout=0, s_patchout_t=40, s_patchout_f=4)

this will get automatically download pretrained PaSST on audioset with with mAP of 0.476. the model was trained with s_patchout_t=40, s_patchout_f=4 but you can change these to better fit your task/ computational needs.

There are several pretrained models availble with different strides (overlap) and with/without using SWA: passt_s_p16_s16_128_ap468, passt_s_swa_p16_s16_128_ap473, passt_s_swa_p16_s14_128_ap471, passt_s_p16_s14_128_ap469, passt_s_swa_p16_s12_128_ap473, passt_s_p16_s12_128_ap470. For example, In passt_s_swa_p16_s16_128_ap473: p16 mean patch size is 16x16, s16 means no overlap (stride=16), 128 mel bands, ap473 refers to the performance of this model on Audioset mAP=0.479.

In general, you can get a pretrained model using:

from models.passt import get_model
passt = get_model(arch="passt_s_swa_p16_s16_128_ap473", fstride=16, tstride=16)

Using the framework, you can evaluate this model using:

python ex_audioset.py evaluate_only with  trainer.precision=16  passt_s_swa_p16_s16_128_ap473 -p

Ensemble of these models are provided as well: A large ensemble giving mAP=.4956

python ex_audioset.py evaluate_only with  trainer.precision=16 ensemble_many

An ensemble of 2 models with stride=14 and stride=16 giving mAP=.4858

python ex_audioset.py evaluate_only with  trainer.precision=16 ensemble_s16_14

As well as other ensembles ensemble_4, ensemble_5

Examples of fine-tuning on downstream datasets

1. ESC-50: Dataset for Environmental Sound Classification
2. OpenMIC-2018 dataset
3. FSD50K

Citation

The citation to the accepted paper in Interspeech 2022:

@inproceedings{koutini22passt,
  author       = {Khaled Koutini and
                  Jan Schl{\"{u}}ter and
                  Hamid Eghbal{-}zadeh and
                  Gerhard Widmer},
  title        = {Efficient Training of Audio Transformers with Patchout},
  booktitle    = {Interspeech 2022, 23rd Annual Conference of the International Speech
                  Communication Association, Incheon, Korea, 18-22 September 2022},
  pages        = {2753--2757},
  publisher    = {{ISCA}},
  year         = {2022},
  url          = {https://doi.org/10.21437/Interspeech.2022-227},
  doi          = {10.21437/Interspeech.2022-227},
}

Contact

The repo will be updated, in the meantime if you have any questions or problems feel free to open an issue on GitHub, or contact the authors directly.