The “Sound of Pixels” project is an innovative endeavor that explores the intersection of audio and visual data, making the process of understanding media more enriching. This blog post aims to guide you through the steps of setting up and utilizing this codebase for your own projects.

Setting Up Your Environment

Before diving into training the model, it is vital to prepare your computing environment. Below are the necessary configurations for successful execution:

• Hardware: You can utilize 1-4 GPUs. Adjust the --num_gpus NUM_GPUS parameter accordingly.
• Software: Make sure your setup includes Ubuntu 16.04.3 LTS, CUDA 8.0, Python 3.5, and PyTorch 0.4.0.

Training Your Model

With your environment set up, the next steps involve preparing your video dataset. Here’s how you can do it:

Step 1: Prepare Video Dataset

• First, download the MUSIC dataset from this GitHub repository.
• Next, download the relevant videos you wish to include in your dataset.

Step 2: Preprocess Videos

Preprocessing involves extracting the necessary data from the videos. You can perform this in your preferred way, as long as the index files remain compatible. Follow these sub-steps:

• Extract frames at 8 fps and waveforms at 11025 Hz from the videos. Below is the directory structure you should follow:

data
audio
    acoustic_guitar
        M3dekVSwNjY.mp3
        ...
    trumpet
        STKXyBGSGyE.mp3
        ...
frames
    acoustic_guitar
        M3dekVSwNjY.mp4
        000001.jpg
        ...
    trumpet
        STKXyBGSGyE.mp4
        000001.jpg
        ...
        ...

• Create training and validation index files by running the following command:

python scripts/create_index_files.py

• This will generate train.csv and val.csv with each line containing the paths and the number of frames, structured like so:

.data/audio/acoustic_guitar/M3dekVSwNjY.mp3,.data/frames/acoustic_guitar/M3dekVSwNjY.mp4,1580
.data/audio/trumpet/STKXyBGSGyE.mp3,.data/frames/trumpet/STKXyBGSGyE.mp4,493

Step 3: Train the Model

The final preparation step is to train the model using the provided script:

bash ./scripts/train_MUSIC.sh

During the training process, visualizations will be saved in an HTML format under the ckpt/MODEL_ID/visualization directory.

Evaluating Your Model

Once the model is trained, evaluating its performance is essential:

• Optionally, you can download pre-trained model weights for evaluation using:

bash ./scripts/download_trained_model.sh

• Finally, execute the evaluation script:

bash ./scripts/eval_MUSIC.sh

Troubleshooting

While following the setup and training instructions, you may face some issues. Here are some common troubleshooting tips:

• Double-check your environment configuration. Incorrect versions of CUDA or Python can lead to compatibility issues.
• Ensure all necessary files are downloaded and in the correct directory structure as specified.
• If you encounter issues while running scripts, verify that you have the required permissions or adjust your paths accordingly.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

By following the above steps, you should be well on your way to utilizing the “Sound of Pixels” codebase in your own machine learning projects. Happy coding!