Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]
Done in a VM with a GPU (Ubuntu) following the [Whisper Event - PYTHON](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#python-script) instructions.  

Attempted using [RuntimeError: he size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000  - WEB](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010/10 ) - another person experiencing the same issue. But could not resolve the issue with the google/fleurs data. __Not clear what can be modified in the PY code to resolve the input data size mismatch, as the training data is already very small__. 

Tried posting on Discord, @sanchit-gandhi and @vaibhavs10. Was hoping that the event is over and some input/help is now available. [Hugging Face - whisper-small-amet](https://huggingface.co/drmeeseeks/whisper-small-amet).

The paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)  am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. (Whisper small WER=120.2). 

# ---> Initial Training Output
 /usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
[INFO|trainer.py:1641] 2022-12-18 05:23:28,799 >> ***** Running training *****
[INFO|trainer.py:1642] 2022-12-18 05:23:28,799 >>   Num examples = 446
[INFO|trainer.py:1643] 2022-12-18 05:23:28,799 >>   Num Epochs = 72
[INFO|trainer.py:1644] 2022-12-18 05:23:28,799 >>   Instantaneous batch size per device = 16
[INFO|trainer.py:1645] 2022-12-18 05:23:28,799 >>   Total train batch size (w. parallel, distributed & accumulation) = 32
[INFO|trainer.py:1646] 2022-12-18 05:23:28,799 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:1647] 2022-12-18 05:23:28,800 >>   Total optimization steps = 1000
[INFO|trainer.py:1648] 2022-12-18 05:23:28,801 >>   Number of trainable parameters = 241734912
 
 # ---> Error
 14% 9/65 [07:07<48:34, 52.04s/it][INFO|configuration_utils.py:523] 2022-12-18 05:03:07,941 >> Generate config GenerationConfig {
  "begin_suppress_tokens": [
    220,
    50257
  ],
  "bos_token_id": 50257,
  "decoder_start_token_id": 50258,
  "eos_token_id": 50257,
  "max_length": 448,
  "pad_token_id": 50257,
  "transformers_version": "4.26.0.dev0",
  "use_cache": false
}

Traceback (most recent call last):
  File "run_speech_recognition_seq2seq_streaming.py", line 629, in <module>
    main()
  File "run_speech_recognition_seq2seq_streaming.py", line 578, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1534, in train
    return inner_training_loop(
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 1859, in _inner_training_loop
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2122, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 78, in evaluate
    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 2818, in evaluate
    output = eval_loop(
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer.py", line 3000, in evaluation_loop
    loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
  File "/usr/local/lib/python3.8/dist-packages/transformers/trainer_seq2seq.py", line 213, in prediction_step
    outputs = model(**inputs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1197, in forward
    outputs = self.model(
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 1066, in forward
    decoder_outputs = self.decoder(
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/transformers/models/whisper/modeling_whisper.py", line 873, in forward
    hidden_states = inputs_embeds + positions
RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1
100% 1000/1000 [2:52:21<00:00, 10.34s/it]

Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). 

There are two options here:
1. Increase the model's `max_length` parameter: 
```python
model.config.max_length = 512
```
2. Filter data with labels longer than max length: https://discuss.huggingface.co/t/open-to-the-community-whisper-fine-tuning-event/26681/21?u=sanchit-gandhi

Note that the datasets repo is reserved for issues directly related to the HF datasets library. Issues related to custom fine-tuning implementations are more applicable to the HF Forum: https://discuss.huggingface.co. You're more likely to get a response by posting your issue in the most applicable place and boost the chance of someone sharing a working solution! @sanchit-gandhi Thank you for all your work on this topic.

I'm finding that changing the `max_length` value does not make this error go away.