AALBERT

Here is also the official repository of AALBERT, which is Pytorch lightning reimplementation of the paper, Audio ALBERT: A Lite Bert for Self-Supervised Learning of Audio Representation. The original code is in AlbertNew branch of s3prl repo. In the paper, we proposed Audio ALBERT, which achieves performance comparable with massive pre-trained networks in the downstream tasks while having 91% fewer parameters.

Dependencies

• Python 3.8
• Computing power (high-end GPU) and memory space (both RAM/GPU's RAM) is extremely important if you'd like to train your own model.
• Required packages and their use are listed requirements.txt.
• pip install -r requirements.txt

Pretrain Stage

We use LibriSpeech as our pretraining stage dataset. You can download dataset by this link.

• Stage 1: modify dataset path to your local dataset path:

  • AALBERT: config path: upstream/aalbert/pretrain_config.yaml

        line 16: datarc:
                {Your dataset key name}: {your local dataset path}

  • Mockingjay: upstream/mockingjay/pretrain_config.yaml

        line 16: datarc:
                {Your dataset key name}: {your local dataset path}

• Stage 2: run pretraining script

  python run_pretrain.py -n aalbert_pretrained -u aalbert

  • -n : experiment_name
  • -u : upstream model: {two option: aalbert / mockingjay}
  • model will save on result folder after finish pretraining stage.

Downstream Stage

Here, we take voxceleb1 speaker classification as our downstream task. You can download dataset from their official website.

After pretraining, We can extract the pretrained model feature on different downstream tasks.

• Stage 1: modify dataset path to your local dataset path
  • voxceleb1_speaker: config path: downstream/voxceleb1_speaker/train_config.yaml

  line  9: datarc:
  line 10:    file_path: {your dataset folder path}
  line 11:    meta_path: {your label file path}

• Stage 2: run downstream script
  • voxceleb1_speaker:

    python run_downstream.py \
    -c downstream/voxceleb1_speaker/train_config.yaml \
    -g result/pretrain/{your_pretrained_model_folder}/model_config.yaml  \
    -t result/pretrain/{your_pretrained_model_folder}/pretrained_config.yaml \
    -u aalbert \
    -d voxceleb1_speaker \
    -k result/pretrained/{your pretrained_model_folder}/checkpoints/{checkpoint_you_want_to_use.ckpt} \
    -n voxceleb1_result

  • -n: experiment name
  • -c: downstream training config
  • -g: pretrained model config
  • -t: load pretrained model pretrained config
  • -u: upstream model: {two option: aalbert / mockingjay}
  • -d: downstream task name
  • -k: model checkpoint path
  • -f: finetune pretrained model or not, default=False