Whisper: pass processing_class instead of removed tokenizer kwarg

[danielhanchen]

transformers 5.x removed the deprecated tokenizer argument from Seq2SeqTrainer, so the Whisper notebook fails at trainer construction on current installs with:

TypeError: Seq2SeqTrainer.__init__() got an unexpected keyword argument 'tokenizer'

4.x has warned about this for a while (the notebook's own saved output shows the FutureWarning pointing at processing_class), and processing_class accepts the feature extractor on both 4.57.6 and 5.x, so the rename is forward and backward compatible.

Changes: one line in original_template/Whisper.ipynb, synced to nb/Whisper.ipynb, nb/Kaggle-Whisper.ipynb, python_scripts/Whisper.py and python_scripts/Kaggle-Whisper.py. I avoided a full update_all_notebooks.py regeneration on purpose: it currently rewrites unrelated notebooks (install blocks, embedded outputs), which would bury this one line change.

Validated by running the patched notebook end to end (training, transcription inference, LoRA save) on transformers 5.11 inside the unsloth Docker image on a B200: passes in 2 minutes. JSON validity checked for all three ipynb files and AST checked for both python scripts.

[gemini-code-assist]

Code Review

This pull request updates the trainer configuration across several Jupyter notebooks and Python scripts by replacing the deprecated tokenizer parameter with processing_class = tokenizer.feature_extractor. The reviewer recommends passing the full tokenizer processor directly to processing_class instead of just the feature extractor. This ensures that both the feature extractor and tokenizer configurations are saved properly when calling trainer.save_model(), making the saved model fully self-contained.

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[gemini-code-assist]

Since the tokenizer variable actually holds the full processor (as indicated by processor = tokenizer in the data collator), passing tokenizer directly to processing_class is highly recommended. If you only pass tokenizer.feature_extractor, the trainer will only save the feature extractor configuration (preprocessor_config.json) and will omit the tokenizer configuration files (like tokenizer_config.json, vocab.json, etc.) when trainer.save_model() is called. Passing the full processor ensures that both the feature extractor and tokenizer are properly saved, making the saved model fully self-contained and ready for inference or resuming training.

Suggested change

	" processing_class = tokenizer.feature_extractor,\n",
	" processing_class = tokenizer,\\n",

[gemini-code-assist]

Since the tokenizer variable actually holds the full processor (as indicated by processor = tokenizer in the data collator), passing tokenizer directly to processing_class is highly recommended. If you only pass tokenizer.feature_extractor, the trainer will only save the feature extractor configuration (preprocessor_config.json) and will omit the tokenizer configuration files (like tokenizer_config.json, vocab.json, etc.) when trainer.save_model() is called. Passing the full processor ensures that both the feature extractor and tokenizer are properly saved, making the saved model fully self-contained and ready for inference or resuming training.

Suggested change

	" processing_class = tokenizer.feature_extractor,\n",
	" processing_class = tokenizer,\\n",

[gemini-code-assist]

Since the tokenizer variable actually holds the full processor (as indicated by processor = tokenizer in the data collator), passing tokenizer directly to processing_class is highly recommended. If you only pass tokenizer.feature_extractor, the trainer will only save the feature extractor configuration (preprocessor_config.json) and will omit the tokenizer configuration files (like tokenizer_config.json, vocab.json, etc.) when trainer.save_model() is called. Passing the full processor ensures that both the feature extractor and tokenizer are properly saved, making the saved model fully self-contained and ready for inference or resuming training.

Suggested change

	" processing_class = tokenizer.feature_extractor,\n",
	" processing_class = tokenizer,\\n",

[gemini-code-assist]

Since the tokenizer variable actually holds the full processor (as indicated by processor = tokenizer in the data collator), passing tokenizer directly to processing_class is highly recommended. If you only pass tokenizer.feature_extractor, the trainer will only save the feature extractor configuration (preprocessor_config.json) and will omit the tokenizer configuration files (like tokenizer_config.json, vocab.json, etc.) when trainer.save_model() is called. Passing the full processor ensures that both the feature extractor and tokenizer are properly saved, making the saved model fully self-contained and ready for inference or resuming training.

Suggested change

	processing_class = tokenizer.feature_extractor,
	processing_class = tokenizer,

[gemini-code-assist]

Since the tokenizer variable actually holds the full processor (as indicated by processor = tokenizer in the data collator), passing tokenizer directly to processing_class is highly recommended. If you only pass tokenizer.feature_extractor, the trainer will only save the feature extractor configuration (preprocessor_config.json) and will omit the tokenizer configuration files (like tokenizer_config.json, vocab.json, etc.) when trainer.save_model() is called. Passing the full processor ensures that both the feature extractor and tokenizer are properly saved, making the saved model fully self-contained and ready for inference or resuming training.

Suggested change

	processing_class = tokenizer.feature_extractor,
	processing_class = tokenizer,