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Conditional Generation of Audio from Video via Foley Analogies

This is the official PyTorch implementation of "Conditional Generation of Audio from Video via Foley Analogies". [Project Page] [Arxiv] [Video] [Poster]

News

[Oct. 2023] We have just updated the instruction for metric evaluation

[Sept. 2023]We have uploaded the pre-trained model vis google drive, feel free to have a try now!

Environment

To setup the environment, please run

conda env create -f conda_env.yml
conda activate condfoley

To setup SparseSync re-ranking environment, please run

cd SparseSync
conda env create -f conda_env.yml
conda activate sparse_sync

Demo

A quick demonstration to generate 6-sec audio with our model is to simply run

mkdir logs
python audio_generation.py --gh_demo --model_name 2022-05-03T11-33-05_greatesthit_transformer_with_vNet_randshift_2s_GH_vqgan_no_earlystop --target_log_dir demo_output --W_scale 3 --spec_take_first 192

The generated video will located at logs/demo_output/2sec_full_generated_video_0.

You may check the audio_generation.py to change the input videos and play with different videos of your own!

Datasets

Greatest Hits

We use the Greatest Hits dataset to train and evaluate our model both qualitatively and quantitatively. Data can be downloaded from here.

Countix-AV

We use the Countix-AV dataset to demonstrate our method in a more realistic scenario. Data can be downloaded following the configs from RepetitionCounting repo.

Data Pre-processing

As described in the paper, we resampled the videos into 15FPS and resampled the audio into 22050Hz. The video is also resized to (640, 360) for faster loading. The audio is denoised with noisereduce package.

For the training preprocess, please use feature_extraction\video_preprocess.py, which will build the correct training data structure. See the file for more details. We have also updated the script so that you can use --greatesthit flag to process data for the Greatest Hits dataset and ignore this flag for the CountixAV dataset. Note that there is no need for further denoise for the Greatest Hits dataset.

For evaluation & demonstration purposes, please use video_preprocess.py.

Data structure

Greatest Hits

The Greatest Hits dataset should be placed under the data/ folder following such structure:

path/to/CondFoleyGen/
    data/
        greatesthit/
            greatesthit-process-resized/
                {video_idx}/
                    audio/
                        {videoIdx}_denoised.wav
                        {videoIdx}_denoised_resampled.wav
                    frames/
                        frame000001.jpg
                        frame000002.jpg
                        ...
                    hit_record.json
                    meta.json
                ...

The meta.json and hit_record.json files can be found at data/greatest_hit_meta_info.tar.gz, which contains all the necessary information in the correct structure. In fact, you may only use them when you train the GreatestHit model with spectrograms, which is deprecated. The current training scheme only uses the .wav audio file.

Countix-AV

Similarly, the Countix-AV dataset should be placed under the data/ folder following such structure:

path/to/CondFoleyGen/
    data/
        ImpactSet/
            impactset-proccess-resize/
                {video_idx}/
                    audio/
                        {videoIdx}.wav
                        {videoIdx}_resampled.wav
                        {videoIdx}_resampled_denoised.wav
                    frames/
                        frame000001.jpg
                        frame000002.jpg
                        ...
                ...

Train/Validation/Test split

We split each dataset on the video level randomly. The split file is under the data/ folder, named data/greatesthit_[train/val/test].json and data/countixAV_[train/val/test].json

Quantitative Evaluation of Greatest Hits

To conduct a fair evaluation of the Greatest Hit dataset, we build a fixed test set composed of 2-sec. conditional and target video pairs cropped from the previous test split following the description in the paper. Please check data/AMT_test_set.json for the detailed information. We also provide the corresponding action information in the data/AMT_test_set_type_dict.json and whether the action in the two videos matches or not in data/AMT_test_set_match_dict.json

The path of the target and conditional video is at data/AMT_test_set_path.json. The data should be placed under the logs/ folder following such structure

path/to/CondFoleyGen/
    logs/
        AMT_test_target_video_trimmed_15fps/
            <video_1>.mp4
            <video_2>.mp4
            ...
        AMT_test_cond_video_trimmed_15fps/
            <video_1>.mp4
            <video_2>.mp4
            ...

We also provide the pre-processed videos for downloading at google drive, you may download it and extract it to the logs/ dir directly.

Pre-trained Models

We release the pre-trained model on both datasets via Google Drive here.

The old Codebook for Greatest Hits is trained with spectrogram, rather than wave files, which does not follow our current file structure. But you may still load this model for transformer training and inference. We also provide the new Codebook model that trained with correct wave file and the same training configuration.

You may also need to download the pre-trained model and the configuration for MelGAN vocoder, which can be found in SpecVQGAN. Please place the downloaded model to ./vocoder/logs/vggsound folder.

Train

The training of our model with default configs requires 1 NVIDIA A40 40G GPU for the first stage, and 4 NVIDIA A40 40G GPUs for the second stage. You may change the --gpus argument to use different number of GPUS. You may also update the configurations under config/ folder to adjust the batch size.

Step 1: training VQ-GAN codebook model

The first step of the training process is to train the VQ-GAN codebook model.

• To train the model on the Greatest Hit dataset, run

python train.py --base configs/greatesthit_codebook.yaml -t True --gpus 0,

• To train the model on the Countix-AV dataset, run

python train.py --base configs/countixAV_codebook_denoise.yaml -t True --gpus 0,

Step 2: training Conditional Transformer model

The second step of the training process is to train the conditional transformer model.

• To train the model on the Greatest Hit dataset, please first fill the relative path of the previously trained codebook checkpoint path to the config file at configs/greatesthit_transformer_with_vNet_randshift_2s_GH_vqgan_no_earlystop.yaml. The path should be put at model.params.first_stage_config.params.ckpt_path After that, you may train the transformer model by running

python train.py --base configs/greatesthit_transformer_with_vNet_randshift_2s_GH_vqgan_no_earlystop.yaml -t True --gpus 0,1,2,3,

• To train the model on the Countix AV dataset, please first fill the relative path of the previously trained codebook checkpoint path to the config file at configs/countixAV_transformer_denoise.yaml, then run

python train.py --base configs/countixAV_transformer_denoise.yaml -t True --gpus 0,1,2,3,

Audio Generation

We provide a sample script to generate audio with a pre-trained model and a pair of sample videos at audio_generation.py.

1. To generate audio with a transformer model trained on the Greatest Hit dataset

python audio_generation.py --gh_gen --model_name <pre_trained_model_folder_name> --target_log_dir <target_output_dir_name>

you may change the orig_videos and cond_videos in the script to generate audio for different videos

2. To generate audio with a transformer model trained on the Countix-AV dataset

python audio_generation.py --countix_av_gen --model_name <pre_trained_model_folder_name> --target_log_dir <target_output_dir_name>

3. To generate audio for the Greatest Hit test set, run

python audio_generation.py --gh_testset --model_name <pre_trained_model_folder_name> --target_log_dir <target_output_dir_name>

The Greatest Hit test data should be placed following the instructions in the previous section

4. To generate multiple audio for re-ranking, please use the --multiple argument. The output will be at logs/{target_log_dir}/{gen_cnt}_times_split_{split}_wav_dict.pt. You may then generate the re-ranking output by running

cd SparseSync
conda activate sparse_sync
python predict_best_sync.py -d 0 --dest_dir <path_to_generated_file> --tolerance 0.2 --split <split> --cnt <gen_cnt>

The output will be in the SparseSync/logs/<path_to_generated_file> folder, under the same folder of previously generated output.

Onset Transfer Baseline

As one another simple yet impressive baseline we proposed in this paper, we provide the full set of the train and test code for the onset transfer baseline. All the related files can be found in the specvqgan/onset_baseline/. More details about this baseline can be found in the paper appendix section A.4.

Data

This baseline uses the same data as the CondFoleyGen model, you just need to create a soft symbolic link of ./data directory under the specvqgan/onset_baseline/ directory like this:

ln -s ./data ./specvqgan/onset_baseline/

The dataloader will automatically load data from the directory with the same pre-processing.

Train & Test

The train and test script are all at the specvqgan/onset_baseline/main.py and specvqgan/onset_baseline/main_cxav.py. Both models uses the same model and training settings, but just different dataloaders. You may train these two models with following command:

cd ./specvqgan/onset_baseline/
# Greatest Hits
CUDA_VISIBLE_DEVICES=0 python main.py --exp='EXP1' --epochs=100 --batch_size=12 --num_workers=8 --save_step=10 --valid_step=1 --lr=0.0001 --optim='Adam' --repeat=1 --schedule='cos'
# Countix-AV
CUDA_VISIBLE_DEVICES=0 python main.py --exp='EXP1' --epochs=100 --batch_size=12 --num_workers=8 --save_step=10 --valid_step=1 --lr=0.0001 --optim='Adam' --repeat=1 --schedule='cos'

And the trained model will locate in ./specvqgan/onset_baseline/checkpoints folder. During test time, please add --test_mode flag and use --resume flag to indicate the model to be used.

Generate video with sound with Onset baseline

To generate videos with sound with this baseline model. Please use the specvqgan/onset_baseline/onset_gen.py and specvqgan/onset_baseline/onset_gen_cxav.py script.

Please change the resume element in these two scripts to indicate the model to be used, and change the read_folder element to indicate a directory that is generated with audio_generation.py.

If you don't want to first generate video with sound with the CondFoleyGen model first, you may also modify these parts (L176-187 in specvqgan/onset_baseline/onset_gen.py and L177-182 in specvqgan/onset_baseline/onset_gen_cxav.py) to load your own video and audio.

Note that the videos to be used for generation need to contain sound (to copy-and-paste) and be located under the specvqgan/onset_baseline/ folder.

Evaluation

Data preparation

To evaluate the output model, please first download the pre-trained model and generate the audio for the test set at google drive following the instruction in the previous section. As an result, you should have 582 generated videos for 194 target video with 3 conditional video each in your generated folder.

Create Test folder

To evaluate the model, you need to create a test folder with the create_test_folder.py under ./data/AMT_test/, which match the generated videos with the target videos and condition videos. It also generate a list that indicate if the action and material type in the target and conditional video are matched or not. It will arrange the videos in the correct order to better evaluate them. To create the test folder, run

python create_test_folder.py <path_to_generated_dir>

The output will be placed under ./data/AMT_test/ folder with the name derived from the input generated folder name.

Onset Eval.

To conduct the evaluation with respect to the Onset Acc. and Onset AP, you may run the following command:

python predict_onset.py --gen_dir <AMT_test_dir> 

You may use --delta option the control the size of detection window when calculating the AP. The default value is 0.1s.

Type Eval.

To conduct the type evaluation, you need first pre-train a VGG-ishish model that predict the action/material type of the sound, the code can be found under ./specvqgan/modules/losses/train_vggishish_gh.py, with the pre-trained model, you may predict the type of the generated audio by running

python ./specvqgan/modules/losses/train_vggishish_gh.py <vggishish_config> <AMT_test_dir> <model_path>

prediction will locate at <AMT_test_dir>_<action/material>_preds.json. The config files can be found under ./specvqgan/modules/losses/configs/. Lastly, please move each prediction json to ./data/AMT_test/<action/material>_pred/, where you should have the prediction for ground truth videos as well.

With the predictions result, you may evaluate the performance of generation quality by running:

python evaluation_auto.py --task <action/material>

Please use --match, --mismatch to control whether evaluate the result when target and condition are match/mismatch.

Citation

If you find this work useful, please consider citing:

@inproceedings{
du2023conditional,
title={Conditional Generation of Audio from Video via Foley Analogies},
author={Du, Yuexi and Chen, Ziyang and Salamon, Justin and Russell, Bryan and Owens, Andrew},
booktitle={Conference on Computer Vision and Pattern Recognition 2023},
year={2023},
}

Acknowledgement

We thank Jon Gillick, Daniel Geng, and Chao Feng for the helpful discussions. Our code base is developed upon two amazing projects proposed by Vladimir Iashin, check out those projects here (SpecVQGAN, SparseSync). This work was funded in part by DARPA Semafor and Cisco Systems, and by a gift from Adobe. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.