Huggingface Seq2seqTrainer freezes on evaluation

I'm currently trying to train a Whisper model by following the Fine Tune Whisper Model tutorial. However, during the training phase where I call trainer.train(). I see the progress bar progresses through the training, but when it reaches the evaluation step defined at the training arguments, it will just freeze and the progress bar just stalls up. No error output, no nothing. And it will look like this.

I'm using Kaggle notebooks to write the code with GPU P100 turned on. Here are my training arguments leading up to the training function.

from transformers import WhisperForConditionalGeneration

model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
model.generation_config.language = "en"

from transformers import Seq2SeqTrainingArguments

training_args = Seq2SeqTrainingArguments(
    output_dir="./whisper-small-eng-gen",  # change to a repo name of your choice
    per_device_train_batch_size=16,
    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=1000,
    gradient_checkpointing=True,
    fp16=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=1000,
    eval_steps=1000,
    logging_steps=25,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=True,
    ignore_data_skip=True
)

from transformers import Seq2SeqTrainer

trainer = Seq2SeqTrainer(
    args=training_args,
    model=model,
    train_dataset=common_voice_train,
    eval_dataset=common_voice_test,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    tokenizer=processor.feature_extractor,
)

Initially, the max_steps for training is 4000, and it always stalls at step 1001.

I think it is also worth noting that my dataset is streamed, and it is an Iterable Dataset.

Any help is appreciated!

**Update** I edited my code to include verbose logging with

import transformers

transformers.logging.set_verbosity_info()

And this is the log after the evaluation step is reached.

You have passed language=en, but also have set forced_decoder_ids to [[1, None], [2, 50359]] which creates a conflict. forced_decoder_ids will be ignored in favor of language=en.

Yeah, I had this too. The thing to keep in mind is that after 1000 steps, your model saves, which can take some time depending on your machine's hardware. I'm currently running a very similar setup, but using the medium model instead of the small and the medium model is about 3 GB. So, be patient with it and it should finish, at least it did for me on my Google Colab instance.