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Whisper: pass processing_class instead of removed tokenizer kwarg #290

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Whisper: pass processing_class instead of removed tokenizer kwarg #290

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2 changes: 1 addition & 1 deletion nb/Kaggle-Whisper.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -675,7 +675,7 @@
" train_dataset = train_dataset,\n",
" data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor = tokenizer),\n",
" eval_dataset = test_dataset,\n",
" tokenizer = tokenizer.feature_extractor,\n",
" processing_class = tokenizer.feature_extractor,\n",

@gemini-code-assist gemini-code-assist Bot Jun 12, 2026

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medium

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",

" compute_metrics = compute_metrics,\n",
" args = Seq2SeqTrainingArguments(\n",
" # predict_with_generate = True,\n",
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2 changes: 1 addition & 1 deletion nb/Whisper.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -675,7 +675,7 @@
" train_dataset = train_dataset,\n",
" data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor = tokenizer),\n",
" eval_dataset = test_dataset,\n",
" tokenizer = tokenizer.feature_extractor,\n",
" processing_class = tokenizer.feature_extractor,\n",

@gemini-code-assist gemini-code-assist Bot Jun 12, 2026

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medium

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",

" compute_metrics = compute_metrics,\n",
" args = Seq2SeqTrainingArguments(\n",
" # predict_with_generate = True,\n",
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2 changes: 1 addition & 1 deletion original_template/Whisper.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -682,7 +682,7 @@
" train_dataset = train_dataset,\n",
" data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=tokenizer),\n",
" eval_dataset = test_dataset,\n",
" tokenizer = tokenizer.feature_extractor,\n",
" processing_class = tokenizer.feature_extractor,\n",

@gemini-code-assist gemini-code-assist Bot Jun 12, 2026

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medium

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",

" compute_metrics=compute_metrics,\n",
" args = Seq2SeqTrainingArguments(\n",
" # predict_with_generate=True,\n",
Expand Down
2 changes: 1 addition & 1 deletion python_scripts/Kaggle-Whisper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) ->
train_dataset = train_dataset,
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor = tokenizer),
eval_dataset = test_dataset,
tokenizer = tokenizer.feature_extractor,
processing_class = tokenizer.feature_extractor,

@gemini-code-assist gemini-code-assist Bot Jun 12, 2026

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medium

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,

compute_metrics = compute_metrics,
args = Seq2SeqTrainingArguments(
# predict_with_generate = True,
Expand Down
2 changes: 1 addition & 1 deletion python_scripts/Whisper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) ->
train_dataset = train_dataset,
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor = tokenizer),
eval_dataset = test_dataset,
tokenizer = tokenizer.feature_extractor,
processing_class = tokenizer.feature_extractor,

@gemini-code-assist gemini-code-assist Bot Jun 12, 2026

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medium

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,

compute_metrics = compute_metrics,
args = Seq2SeqTrainingArguments(
# predict_with_generate = True,
Expand Down
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