fix: use dynamic label names for inference to match training dataset

[Mani-Babapour]

Previously, the inference section used a hardcoded list of labels:
['no', 'yes', 'down', 'go', 'left', 'up', 'right', 'stop'],
which may not match the order of class names in train_ds.class_names.
This could lead to incorrect labeling in prediction visualizations.

This commit replaces the hardcoded labels with label_names from the training dataset to ensure the model's outputs are correctly mapped to their respective labels.

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[github-actions]

Preview

Preview and run these notebook edits with Google Colab:

• site/en/tutorials/audio/simple_audio.ipynb

Rendered notebook diffs available on ReviewNB.com.

Format and style

Use the TensorFlow docs notebook tools to format for consistent source diffs and lint for style:

$ python3 -m pip install -U --user git+https://github.com/tensorflow/docs

$ python3 -m tensorflow_docs.tools.nbfmt notebook.ipynb

$ python3 -m tensorflow_docs.tools.nblint --arg=repo:tensorflow/docs notebook.ipynb

If commits are added to the pull request, synchronize your local branch: git pull origin fix-simple-audio