Awesome

MambaFoley: Foley Sound Generation using Selective State-Space Models

Marco Furio Colombo, Francesca Ronchini, Luca Comanducci, Fabio Antonacci

This repository contains the implementation of the paper, MambaFoley: Foley Sound Generation using Selective State-Space Models, submitted to ICASSP 2024.

In our paper, we propose MambaFoley, a waveform generation model for Foley sound synthesis, which can generate high-quality audio considering both sound class and when sound should be arranged. For the generation task we employ a diffusion model, with mamba layers in the UNet bottleneck for audio sequence modeling.

Audio Samples

A webpage providing a showcase for generated audio samples is available at this link.

Setup

To get started, please prepare the codes and python environment.

Clone this repository:

git clone https://github.com/FurioColombo/mamba-foley
cd ./mamba-foley

Install the required dependencies by running the following command:

# (Optional) Create a conda virtual environment
conda create -n env-mamba-foley python=3.8.0
conda activate env-mamba-foley
# Install dependency with pip. Choose appropriate cuda version
pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Dataset

To train and evaluate our model, we used DCASE 2023 Challenge Task 7 which was constructed for Foley Sound Synthesis. Click the link above links to download the corresponding datasets.

Download and unzip the DCASE 2023 task 7 dataset. Due to the mismatch between the provided csv and actual data files, please make valid filelists(.txt) using the provided scripts:

wget http://zenodo.org/records/8091972/files/DCASE_2023_Challenge_Task_7_Dataset.tar.gz
tar -zxvf DCASE_2023_Challenge_Task_7_Dataset.tar.gz
rm DCASE_2023_Challenge_Task_7_Dataset.tar.gz
sh scripts/bash/rename_dirs.sh
sh scripts/bash/make_filelist.sh

Scripts configurations

Every python script has a corresponding .json config file, which contains all settings for the script configuration.

You can find a scripts configuration explanation in the folders containing the relative config files:

Inference

To perform inference using our model, follow these steps:

Download the pre-trained model weights and configurations from the following link: pretrained model download.
```
wget https://sandbox.zenodo.org/records/111421/files/pretrained.zip 
```
Unzip and place the downloaded model weights and config json file in the ./pretrained directory.
```
unzip pretrained.zip
rm pretrained.zip
```
Check and edit the settings in the corresponding config file: inference_config.json
Run the inference script by executing the following command:
```
python scripts/python/inference/inference.py
```

Training

To train the T-Foley model, follow these steps:

If you plan to use a dataset different from the DCASE 2023 task 7 dataset, download it and make prepare it in a dedicated folder.
If you use other dataset, prepare file path list of your training data as .txt format and configure its path in the training configuration file, changing the corresponding fields: train_dirs, test_dirs.
(optional) - recommended to skip this step If you wish to load the conditioning from file instead of computing it on the go:
- In the training configuration file set the values of the 'train_cond_dirs' and 'train_cond_dirs' fields to the .
- Run:
```
python scripts/python/dataset/create_cond_dataset.py
```

Check the training configuration file settings, making sure it matches all your needs
Run the training:
```
python scripts/python/training/train_model.py
```
This will start the training process and save the trained model weights in the train_logs/ directory.

To see the training on tensorboard, run:
```
tensorboard --logdir train_logs/
```

Citation

@misc{colombo2024mambafoleyfoleysoundgeneration,
      title={MambaFoley: Foley Sound Generation using Selective State-Space Models}, 
      author={Marco Furio Colombo and Francesca Ronchini and Luca Comanducci and Fabio Antonacci},
      year={2024},
      eprint={2409.09162},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2409.09162}, 
}

License

This project is licensed under the MIT License. See the LICENSE file for more information.