Audio-text Retrieval for DCASE2022 Challenge

Set up environment

$ git clone https://github.com/wsntxxn/DCASE2022T6_CLAP
$ cd DCASE2022T6_CLAP
$ pip install -r requirements.txt

Prepare the dataset

We use Clotho, AudioCaps and MACS for training. Due to the copyright problem of Youtube videos, we do not include the step-by-step data preparation scripts in this repo. However, once the audio and text files are downloaded, you can use the tools to convert the data into the desired format.

The recipe in the audio captioning repo can help.

First organize audio files to generate wav.csv. It is a tab separated table like this:

audio_id	file_name
Y_f_i28HxMDA.wav	/path/to/audioset/Y_f_i28HxMDA.wav
Ya67ihfUaKUc.wav	/path/to/audioset/Ya67ihfUaKUc.wav
...	...

data/utils/prepare_wav_csv.py can be used to generate wav.csv for a single dataset:

$ python data/utils/prepare_wav_csv.py $AUDIO_DIR $WAV_CSV

where $AUDIO_DIR contains all .wav audio files of a dataset, and $WAV_CSV is the path of the output wav.csv. Then data/utils/pack_waveform.py packs the .wav files into a single hdf5 file:

$ python data/utils/pack_waveform.py $WAV_CSV -o $HDF5_FILE --sample_rate $SAMPLE_RATE

Text annotations should be converted into the json format.

Finally hdf5 files and annotation files are concatenated into a single file by data/utils/concat_csv.py and data/utils/concat_json.py. During training we randomly select 1000 audio-text pairs as the validation set, which is done by data/utils/train_val_split.py.

Prepare the pre-trained models

We use several pre-trained audio encoders. For example, PANNs CNN14:

$ mkdir pretrained_models
$ wget -O pretrained_models/Cnn14_mAP=0.431.pth https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth

Similarly, other models should also be downloaded in advance.

Prepare the configuration

Example configuration files are in configs. Modify the corresponding settings (e.g. data files, training iterations, etc.).

Training using slurm

The training scripts use slurm wordload manager. It is also based on distributed data parallel. You can modify the script to use your own system for training.

$ sbatch scripts/run_ddp.sh

Pre-trained Checkpoints

We provide a pre-trained checkpoint of CNN14-BERTbase model here.

wsntxxn / DCASE2022T6_CLAP