Hey folks, I’m having trouble with a project where I’m making spectrograms from audio files. I’m using a tool to create these spectrograms, but I’m running into memory problems.
Here’s what I’m trying to do:
audioFiles.forEach(file => {
runBrowserTask(() => {
// Set up audio processor
// Make spectrogram
// Get spectrogram area info
})
captureImage()
// Need to clean up audio processor here
})
The issue is that if I don’t clear the audio processor after each file, the images start to stack up and eventually crash the program. I tried to pass the audio processor to a separate task to clean it up, but no luck. Any ideas on how to fix this? I’m pretty stuck!
I’d really appreciate any help or suggestions. Thanks in advance!
I’ve faced similar memory issues when working with audio processing in headless browsers. One approach that worked for me was to implement a garbage collection strategy. After each file is processed, you could try forcing garbage collection using:
if (global.gc) {
global.gc();
}
You’ll need to run Node with the --expose-gc flag for this to work. Another technique I found helpful was to use a worker pool. This allows you to distribute the processing across multiple workers, each with its own memory space. It helped prevent memory buildup in a single process.
Lastly, consider processing files in batches rather than all at once. This gives the system more opportunities to clear memory between batches. It’s a bit slower, but it significantly reduced crashes in my projects.
Have you considered using a library like puppeteer-cluster? It’s designed to handle multiple browser instances efficiently, which could help with your memory issues. Each cluster worker can process an audio file independently, allowing for better resource management.
Another approach worth exploring is streaming the audio data instead of loading entire files at once. This can significantly reduce memory usage, especially for large audio files. You might need to adjust your spectrogram generation process to work with streamed data, but it could solve your memory problems.
Lastly, if you’re not already doing so, try implementing a memory monitoring system. This can help you identify exactly where memory spikes occur and potentially optimize those specific areas of your code.
hey, i faced this too. try using web workers so each gets its own mem space. also, process files in smaller batches to give mem a breather. it might slow things a bit but should help with crashes.