Negative CTC loss while training TFWav2Vec2ForCTC model
### System Info

`transformers` version: 4.21.0.dev0
- Platform: Linux-5.13.0-48-generic-x86_64-with-glibc2.31
- Python version: 3.7.13
- PyTorch version (GPU?): 1.11.0 (False)
- Tensorflow version (GPU?): 2.9.1 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>

### Who can help?

@Rocketknight1 @gante

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Colab link to reproduce: https://colab.research.google.com/drive/1HXOdDhaIWcLF_4xF-zKZ_gYRf-sMfHkL?usp=sharing

```
Epoch 1/5
 28/3859 [..............................] - ETA: 47:03 - loss: -0.5141
```

### Expected behavior

The model should train with positive CTC loss. I have been able to figure out the source of the error which
is that the target sequence never reaches the model at the forward pass and CTC loss is calculated over
empty targets (None).

I have also figured out the solution which is:

add `@unpack_inputs`  here:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1583

However, with this, the CTC loss now gets the targets and calculates the loss but it raises another error:

```
InvalidArgumentError                      Traceback (most recent call last)
/tmp/ipykernel_33658/3396866883.py in <module>
      3 tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
      4 
----> 5 model.fit(train, validation_data = validation, epochs=5)

~/anaconda3/envs/gsoc-2/lib/python3.7/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

~/anaconda3/envs/gsoc-2/lib/python3.7/site-packages/transformers/modeling_tf_utils.py in train_step(self, data)
   1024 
   1025             if self._using_dummy_loss:
-> 1026                 loss = self.compiled_loss(y_pred.loss, y_pred.loss, sample_weight, regularization_losses=self.losses)
   1027             else:
   1028                 loss = None

InvalidArgumentError: slice index 0 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/
```

To solve this I added `loss = tf.reshape(loss, (1,))` after CTC loss calculation here:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py#L1707

These solve the error and I can train my model now. I am hoping the changes get pushed to the main branch.

The issue was previously mentioned here: https://github.com/huggingface/transformers/issues/15114
But since @Rocketknight1 mentioned that he is working with loss calculation across HF TF models, I thought I would open
a new issue.
Hi @Sreyan88, this looks like a real bug and a good solution - would you be willing to file a PR with your changes, and we'll review it?<|||||>Hi @Rocketknight1 ,

The pull request is up! You can review it and assign reviewers too!<|||||>Hi @Rocketknight1 ,

Though this works perfectly now, I notice that the proper functioning of this code requires me to:

`tf.config.run_functions_eagerly(True)`

otherwise, I get an error:

`raise ValueError(f"Label values must be <= vocab_size: 30")`

Though everything is perfect and just adding the above line works perfectly.

any reason for this? Why would this not work without eager execution?

You can refer to the notebook above too to reproduce. Thank You!<|||||>Can this warning speak anything about this?

```
Epoch 1/2
WARNING: AutoGraph could not transform <bound method ArrowWriter._build_writer of <datasets.arrow_writer.ArrowWriter object at 0x7f3033179b10>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: annotated name 'schema' can't be nonlocal (__autograph_generated_file8pfv1pvr.py, line 70)
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <bound method Dataset.set_format of Dataset({
    features: ['predictions', 'references'],
    num_rows: 2
})> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: annotated name 'self' can't be nonlocal (__autograph_generated_file7oqoxv4_.py, line 29)
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
839/901 [==========================>...] - ETA: 1:51 - loss: 447.1289 - compute_wer: 1.0000
```<|||||>Hi @Sreyan88 - I can't figure out where that error is coming from. In your example scripts above, you're running everything eagerly, which means that AutoGraph should not be doing anything. I think this is probably related to the issues in #18096, but let me know if you resolve those and this issue is still occurring!<|||||>Hi @Rocketknight1 ,

Yes, I am, trying to figure out #18096  though it's a bit difficult for me as I am a bit new to Keras/Tensorflow. @gante 's suggestion did not work so I am still investigating!

Thank You for the reply! 