chore: Add Speech Recognition with DDP Example

[zren11]

What this PR does / why we need it:
This is an simple Speech Recognition example for Issue 2040.

Key Features:

• Complete end-to-end workflow in example.ipynb - from data preparation to distributed training
• PyTorch DDP integration with Kubeflow Trainer for multi-node speech recognition
• Transformer-based model (4 layers, 4 attention heads) for audio classification
• Use Google Speech Command dataset - 35 words, 105K+ 1-second audio files for classification
• Local to distributed progression - starts with local training, scales to Kubernetes

Details can be found in PR's README.md file.

Which issue(s) this PR fixes (optional, in Fixes #<issue number>, #<issue number>, ... format, will close the issue(s) when PR gets merged):
Fixes #Issue 2040 Issue 2040.

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Pull Request Test Coverage Report for Build 17773026722

Warning: This coverage report may be inaccurate.

This pull request's base commit is no longer the HEAD commit of its target branch. This means it includes changes from outside the original pull request, including, potentially, unrelated coverage changes.

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Details

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• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 55.137%

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Totals
Change from base Build 17772927799:	0.0%
Covered Lines:	1084
Relevant Lines:	1966

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[zren11]

@andreyvelich I notice the GPU E2E test failed. Is there anything I can do to fix it? Thanks!

[jaiakash]

/ok-to-test

[andreyvelich]

@andreyvelich I notice the GPU E2E test failed. Is there anything I can do to fix it? Thanks!

Can you rebase your PR again please ?

[andreyvelich]

You can remove all of your files and keep only Jupyter Notebooks.
You can check out this example for knowledge distillation BERT fine-tuning: https://github.com/kubeflow/trainer/blob/master/examples/pytorch/question-answering/fine-tune-distilbert.ipynb
In your Notebook you can reference to the getting started guide which explains how to deploy kind cluster and torch-distributed Runtime.