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Integrate UrbanSoundDataset for Audio Data Processing #1179
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Quality Gate passedIssues Measures |
looks like one of the lints are failing. weird. @karkir0003 @DSGT-DLP/project-lead the error message is this: We just added torchaudio and soundata libraries in this pr to poetry so bc of that. |
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good start for the most part.
please address my questions and add unit tests to ensure the data loading logic works as intended
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soundData = tempData | ||
soundFormatted = torch.zeros([32000, 1]) | ||
soundFormatted[:32000] = soundData[::5] # Take every fifth sample of soundData |
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explain?
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the reason why we are taking every fifth sample from soundData and assigning it to the first 32000 elements of soundFormatted is to downsample the data if soundData is a high-frequency sound signal.
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ok. might want to clarify that
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will do
whats simpleaudio? is this a lib thats a dependency used by soundata? |
did we install the latest stable version of torchaudio and soundata? |
I'd try starting the debugging with the following:
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still tryna figure this one out. i went through the logs and couldnt find any hints |
yup i double checked. i just ran |
alright bet sounds good |
@codingwithsurya , looks like someone from poetry responded to this thread I created and shared with you in discord: https://github.com/orgs/python-poetry/discussions/9418 |
ok. we can add it as a dependency then. |
let's give this a try and see if that works @codingwithsurya |
adding urbansound-dataset and schemas.py
Github Issue Number Here: <ntegrate UrbanSoundDataset for Audio Trainspace #1156>
What user problem are we solving?
We are enhancing the Deep Learning Playground's capabilities to include audio data processing by integrating the UrbanSound8K dataset. This allows users to work with audio data seamlessly within the existing pipeline, expanding the versatility and application of the platform.
What solution does this PR provide?
This PR adds a new class, UrbanSoundDataset, to the training/core/dataset.py module. This class encapsulates functionalities for data ingestion, preprocessing, and loading specifically tailored for the UrbanSound8K dataset. It includes dataCreator, train_loader, and test_loader methods to facilitate efficient data loading into the model for training and testing. Additionally, it ensures compatibility with PyTorch's DataLoader mechanism and integrates smoothly with the existing training pipeline.
It also provides a schemas.py file that provides audio params. This file is still a WIP.
Testing Methodology
Any other considerations
Updated schemas.py to include AudioParams for defining non-tunable parameters specific to the UrbanSound8K dataset.
we also added 2 new dependencies to poetry -- torchaudio and soundata