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audionet's Introduction

Hi there, I'm Vishnu! 👋

iamvishnuks

I'm a technocrat from India.

  • 🏢 I'm currently working at AsianLogic
  • ⚙️ I use daily: .py, .go, .yaml, .json and a lot of DevOps tools
  • 🌍 I'm mostly working in DevOps | SRE | Distributed Engineering | Architecting solutions on cloud
  • 🌱 Learning all about New in tech
  • 💬 Ping me about python, golang, k8s, AWS, Azure, GCP, microservices and building reliable observability platforms

Connect with me:

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iamvishnuks

Skilled in 👇:

Python3

Golang

K8s

Docker

Azure

AWS

GCP

GitHub


audionet's People

Contributors

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Watchers

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Forkers

suzhidong anigi98932 hunterhawk tomfisher ashishpatel26 anjelikasah guanyutaba samueljohnsiby

audionet's Issues

The network don't learning with other dataset

Hi, I have been a problem when trying to train the network with spectrograms creates from audio streaming in real-time. I collected 8k images, 4k for each folder. The spectrograms didn't create with .wav files. It is the only different about your approach.

Do you have any idea about this? My spectrogram images have a resolution of 553x396.

data maker eats all the memory over time

im running data maker to make spectrograms out of ~3000 wav files. for some reason after the spectrogram is made and saved to the file system its RAM memory isn't released. I need to restart the program every ~400 conversions because its becoming incredibly slow.

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