Ready to dive into the fascinating world of transforming audio into images and back again? With Riffusion Manipulation Tools, you can do just that! This blog will walk you through the essential commands you need to know, how to convert audio files to images, and verify your results seamlessly.

Understanding the Command Flags

The Riffusion tools come with several flags to enhance your experience when converting files. Think of these flags as special tools in a toolbox that help you customize your project.

• -i, --input INPUTFILE.ext – Specify the input audio file.
• -o, --output OUTPUTFILE.ext (on img2audio.py) – Define the output audio file’s name.
• -o, --output OUTPUT_FOLDER (on file2img.py) – Set the folder for saving output images.
• -m, --maxvol [integer] – Adjust the maximum volume (50+ for Okay, 100+ for Good Quality, 255+ for Max Quality).
• -p, --powerforimage [float] – Control the image power (optimal range 0.25-0.35).
• -n, --nmels [integer] – Specify the number of mel bands used (default 512).
• -d, --duration – Set the duration for each image chunk (1 second = 1000 ms).

Converting Audio to Image

Let’s walk through the process of converting an audio file into images using the file2img.py script. Imagine you are a chef preparing different small bites from a large dish—you’re turning a full audio track into manageable spectograms, each lasting 5119 ms.

The command you’ll use is:

python3 file2img.py -i INPUT_AUDIO.wav -o OUTPUT_FOLDER

This will create a folder containing your output spectrogram images. For example, to convert the file charmpoint.mp3, you would execute:

python3 file2img.py -i charmpoint.wav -o charmpoint_images

This generates a folder with spectograms of the entire song, each corresponding to the specified duration. Remember, if the audio length isn’t a perfect fit for the chunk duration, silence will be added to accommodate.

Verifying Conversion from Image to Audio

Just like a taste test after cooking, verifying your audio conversion ensures everything is as expected. To confirm that the image converted to audio correctly, use the img2audio.py script:

python3 img2audio.py -i INPUT_IMAGE.ext -o OUTPUT_AUDIO.ext

For example:

python3 img2audio.py -i charmpoint_chunks/charmpoint_43.png -o charmpoint_chunk_43.mp3

Troubleshooting Tips

Even the best chefs encounter some hiccups in the kitchen! Here are a few troubleshooting tips to help you along the way:

• If the audio does not appear as expected once converted: double-check your -n and -d values. They should align with your intended image specifications.
• Ensure that the audio input file is supported and correctly formatted.
• If you experience issues with output images, try adjusting the -p parameter to find the sweet spot between clarity and noise.

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

Additional Information

The resulting spectrogram images will be in a single channel (black and white). If you want these accepted by Stable Diffusion tools, you may need to convert them into RGB, which the Riffusion inference server does automatically.

Experimentation and Further Resources

Having fun with variables? Experimentation is available in the tests folder. An exciting experiment you might not want to miss is the one with Planet Girl from ALIEN POP. Dive in to see the various configurations used.

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.

Happy converting!