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Pre-training data

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 file data/files_audioset.csv by default (--dataset).

The CSV file should have a file_name column containing the relative pathname of the files containing a log-mel spectrogram (LMS) audio. Example:

file_name
audioset_lms/balanced_train_segments/--aE2O5G5WE_0.000.npy
audioset_lms/balanced_train_segments/--cB2ZVjpnA_30.000.npy
audioset_lms/balanced_train_segments/--aaILOrkII_200.000.npy
audioset_lms/balanced_train_segments/--ZhevVpy1s_50.000.npy
audioset_lms/balanced_train_segments/--aO5cdqSAg_30.000.npy
audioset_lms/balanced_train_segments/--PJHxphWEs_30.000.npy
audioset_lms/balanced_train_segments/--ekDLDTUXA_30.000.npy

The folders/files should look like the following:

(Example of the folder structure)
data/
    audioset_lms/
        balanced_train_segments/
            --aE2O5G5WE_0.000.npy
            --cB2ZVjpnA_30.000.npy
              :

If you also have pre-processed FSD50K data, the folder will be as follows:

(Example of the folder structure)
data/
    audioset_lms/
      :
    fsd50k_lms/
        FSD50K.dev_audio/
            2931.npy
            408195.npy
                :

Example preprocessing steps (AudioSet)

If you have downloaded the AudioSet samples and converted them into .wav files in /your/local/audioset folder, the following example steps will preprocess and create a new folder, data/audioset_lms.

  1. Convert your pre-training data to LMS using wav_to_lms.py. Example: python wav_to_lms.py /your/local/audioset data/audioset_lms

  2. Then, make a list of files under your data folder. Example follows:

    echo file_name > data/files_audioset.csv
(cd data && find audioset_lms -name "*.npy") >> data/files_audioset.csv