Audiossl is developed as we implement our own audio self-supervised learning methods(see Methods below). This library provides general modules involved in audio pretraining, such as dataset loading, data transformation and etc.

Two official implemented audio self-supervised methods are included:

1. ATST: Audio Representation Learning with Teacher-Student Transformer (Published at (INTERSPEECH2022))

   See audiossl/methods/atst

2. Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks (Accepted by TASLP)

   See audiossl/methods/atstframe

1. install pytorch ( version 1.12.1 or higher )

conda install pytorch=1.12.1 torchvision torchaudio cudatoolkit=11.3 -c pytorch -c nvidia   

2. install audiossl

    git clone https://github.com/Audio-WestlakeU/audiossl
    cd audiossl
    pip install .

One of the difficult parts of doing research on audio self-supervised learning is that you need to evaluate pretrained model on diverse downstream datasets. Audiossl implements an unified dataset interface to make evaluation easier. It's also easy to implement a new dataset.

1. List available datasets

   from audiossl import datasets
   print(datasets.list_all_datasets())
   """ output:
   voxceleb1:
   { 'creator': <function create_voxceleb1 at 0x7fbe285d0f80>,
   'multi_label': False,
   'num_folds': 1,
   'num_labels': 1251}
   us8k:
   { 'creator': <function create_us8k at 0x7fbe285d6170>,
   'multi_label': False,
   'num_folds': 10,
   'num_labels': 10}
   nsynth:
   { 'creator': <function create_nsynth at 0x7fbe285d60e0>,
   'multi_label': False,
   'num_folds': 1,
   'num_labels': 11}
   spcv2:
   { 'creator': <function create_spcv2 at 0x7fbe285d64d0>,
   'multi_label': False,
   'num_folds': 1,
   'num_labels': 35}
   audioset_b:
   { 'creator': <function create_spcv2 at 0x7fbe285d6560>,
   'multi_label': True,
   'num_folds': 1,
   'num_labels': 527}
   audioset:
   { 'creator': <function create_spcv2 at 0x7fbe285d65f0>,
   'multi_label': True,
   'num_folds': 1,
   'num_labels': 527}
   """

2. Use a dataset

   • Data preparation

     See audiossl/methods/atst/docs/data_prep.md

   • Get a dataset

     from audiossl import datasets
     dsinfo=dataset.get_dataset("nsynth")
     ds = dsinfo.creat_fn(PATH_DATASET,split="train",transform=None,target_transform=None)

3. Transformations

   See audiossl.transforms.

4. An easy-to-use lightning data module

   from audiossl.lightning.datamodules import DownstreamDataModule
   data_module = DownStreamDataModule(data_path,
                                     dataset_name,
                                     batch_size=512
                                     transforms=[train_transform,
                                                 valid_transform,
                                                 test_transform],
                                                 )
                                     target_transforms=[train_targe_transform,
                                                       None,
                                                       None])