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 a 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.
Setting up the experiments environment
This repo uses forked versions of sacred for configuration and logging, and pytorch-lightning for training.
For setting up Mamba is recommended and faster then 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. Now install the forked versions of sacred and pl-lightning and ba3l.
# dependencies
conda activate ba3l
pip install https://github.com/kkoutini/sacred/archive/ba3l.zip
pip install https://github.com/kkoutini/pytorch-lightning/archive/ba3l.zip
pip install https://github.com/kkoutini/ba3l/archive/master.zip
In order to check 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
Each dataset has an experiment file such as ex_audioset.py and ex_openmic.py and a dataset folder with a readme file. In general, you can prob the experiment file for help:
python ex_audioset.py help
you can override any of the configuration using the sacred syntax. In order to see the available options either use omniboard or use:
python ex_audioset.py print_config
There are many pre-defined configuration options in config_updates.py. These include different models, setups etc... You can list these configurations with:
python ex_audioset.py print_named_configs
The overall configurations looks 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
trainerare pytorch lightning trainer api. For example, to turn off cuda benchmarking addtrainer.benchmark=Falseto the command line. models.netare the PaSST (or the chosen NN) options.models.melare the preprocessing options (mel spectrograms).
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 -m mongodb_server:27000:audioset21_balanced -c "PaSST base"
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 -m mongodb_server:27000:audioset21_balanced -c "Unstructured PaSST base"
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 -m mongodb_server:27000:audioset21_balanced -c "PaSST base 2 GPU"
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 this 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 passt_s_swa_p16_s16_128_ap473
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
Contact
The repo will be updated, in the mean time if you have any questions or problems feel free to open an issue on GitHub, or contact the authors directly.
and I run the code
but I encontered the issue:
Is this the wrong version of pytorch-lighting and sarced?When I upgrad the pytorch-lighting to the latest version,the issue is solved but the issue with sacred has not been solved.
Could you please provide some help?Thank you very much!
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