discussion / Acoustics  / 11 May 2025

Need advice for running BirdNET on big data

I have some 25,000 hours of acoustic recordings to process via BirdNET analyzer, most of it in 15 second chunks. I ran an initial ~4,000 hours, which took a few weeks running in the background on my laptop, and I suspect contributed to my laptop dying. It was old and not up to the task, considering the noise the fan was making. I now have a new laptop with a better GPU and CPU, which will hopefully help, but I don't want to burn this one out too! 

 

Has anyone run Birdnet on high volumes of data like this? Any advice?

 

In graphics settings (I'm on windows 11), should I set my GPU preference to high performance rather than 'let windows decide'?




I haven't tried BirdNET analyzer, but with regards to running any bigdata/ML processing, my advice would be to look at something like Google Colab instead of your own laptop. 
 

Hope this helps.

Would that be able to process locally stored acoustic data? 

 

One of the great things about birdnet analyzer is that it is local - it doesn't require uploading terabytes of data into the cloud, which would be expensive, take forever, and likely have some transfer errors in areas with poor internet connection (like the tropics where I do my research).