Denoising the sound prediction files

The ASR models for sound prediction are usually pre trained on clean speech. However, the samples in this dataset appear to have a variety of noise added. Has anyone tried any denoising approaches to clean those out/ any pointers to some libraries?

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Submission format for the NLP feature engineering challenge

Thank you so much for the information. It’s clear now.
Yes, those were my mistakes. I was not running it on colab at first hence the different format and may have lead to changes in the markdown.
Noted, I’ll keep that unchanged and submit.
Thanks again.

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Notebook submissions at the end

I think if you’re using the submission as per the starter kit, the notebook is bundled together with the test.csv. So, you won’t need to submit that separately.

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Submission format for the NLP feature engineering challenge

Hi!
I tried submitting a notebook to the challenge, but it showed error suggesting a zip file was to be submitted. Uploading the zip file using the command in the starter kit also gave error. Could you please clarify the submission format? The submission section has very few details.

Thanks.

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Kernel died error while running the colab

I tried a smaller model and this still fails in the Generate Predictions On Test Data phase. The logs do not point to any particular code failure.

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Kernel died error while running the colab

This time the validation step ran fine and it stopped during predictions on test data. https://www.aicrowd.com/challenges/ai-blitz-9/problems/nlp-feature-engineering/submissions/146420

Ideally though, as per my understanding if it worked on validation, it should’ve been same process on test data - right?

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Kernel died error while running the colab

Thank you so much for looking into this and the fix. Trying out now.

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Kernel died error while running the colab

I was running the colab for generating word vectors for the NLP feature engineering task where I get this error log: https://aicrowd-evaluation-logs.s3.us-west-002.backblazeb2.com/logs/desiml/blitz-9/feature-engineering/17724c17-2a58-4083-84d9-1c3c9766a802.log
It’s unclear to me what the issue is in this case. Any help would be very much appreciated. Thanks!

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Nlp.pipe() processing

Hi!
Try out the BERT based model from the notebook I just shared: https://www.aicrowd.com/showcase/bert-for-emotion-detection
Should get you above 0.8 I think. Let me know in case of any issues.

Thanks.

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