Error when running pipeline with whisper and using the 'return_dict_in_generate=True' option
### System Info

- `transformers` version: 4.26.1
- Platform: macOS-13.1-x86_64-i386-64bit
- Python version: 3.9.16
- Huggingface_hub version: 0.12.1
- PyTorch version (GPU?): 1.13.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help?

 @sanchit-gandhi  @Narsil 

When running a simple whisper pipeline, e.g., using the options 'return_dict_in_generate': True and 'output_scores': True, e.g.,

```
from pathlib import Path
from transformers import pipeline, AutomaticSpeechRecognitionPipeline, Pipeline, GenerationConfig

audio_path = 'xxx.wav'

generate_kwargs = {'temperature': 1, 'max_length': 448, 'return_dict_in_generate': True, 'output_scores': True}

pipe = pipeline(
    model="openai/whisper-small",
    chunk_length_s=10,
    framework="pt",
    batch_size=1
)

print(pipe(audio_path, return_timestamps=True, generate_kwargs=generate_kwargs))
```

I am getting the following error:

```
Traceback (most recent call last):
  File "/Users/sofia/PycharmProjects/openAI-whisper/test4.py", line 39, in <module>
    print(pipe(audio_path, return_timestamps=True, generate_kwargs=generate_kwargs))
  File "/Users/sofia/miniforge3/envs/openAI-whisper/lib/python3.9/site-packages/transformers/pipelines/automatic_speech_recognition.py", line 378, in __call__
    return super().__call__(inputs, **kwargs)
  File "/Users/sofia/miniforge3/envs/openAI-whisper/lib/python3.9/site-packages/transformers/pipelines/base.py", line 1076, in __call__
    return next(
  File "/Users/sofia/miniforge3/envs/openAI-whisper/lib/python3.9/site-packages/transformers/pipelines/pt_utils.py", line 125, in __next__
    processed = self.infer(item, **self.params)
  File "/Users/sofia/miniforge3/envs/openAI-whisper/lib/python3.9/site-packages/transformers/pipelines/automatic_speech_recognition.py", line 611, in postprocess
    items = outputs[key].numpy()
AttributeError: 'ModelOutput' object has no attribute 'numpy'
```

### Information

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

### Tasks

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

### Reproduction

1. Run the code

```
from pathlib import Path
from transformers import pipeline, AutomaticSpeechRecognitionPipeline, Pipeline, GenerationConfig

audio_path = 'xxx.wav'

generate_kwargs = {'temperature': 1, 'max_length': 448, 'return_dict_in_generate': True, 'output_scores': True}

pipe = pipeline(
    model="openai/whisper-small",
    chunk_length_s=10,
    framework="pt",
    batch_size=1
)

print(pipe(audio_path, return_timestamps=True, generate_kwargs=generate_kwargs))
```


### Expected behavior

I expect to get the text result accompanied with the timestamps and the prediction scores
cc @ArthurZucker <|||||>Hey! Thanks for reporting. This is normal as the `pipeline` does not support returning the usual `dictionary`. 
We should probably prevent this behaviour (raise an error when `return_dict_in_generate` is set in the pipeline) cc @Narsil this is a duplicate of another issue but I can't find it! 
edit: #21185
<|||||>Best recommendation in the mean time is to define a custom pipeline, where you process the inputs before feeding them to `super.preprocess`! <|||||>> Best recommendation in the mean time is to define a custom pipeline, where you process the inputs before feeding them to `super.preprocess`!

Thanks for your reply, I now understand the issue.

However, I am not sure how to preprocess the input to achieve this. 
I can see the output and the dictionary still contains the tokens (inside the ModelOutput):

```
{'tokens': ModelOutput([('sequences', tensor([[50258, 50342, 50358, 50364,  1044,   291,   337,  1976,     0, 50864,
         50257]])), ('scores', (tensor([[2.3064,   -inf,   -inf,  ..., 2.8053, 2.7866, 3.3406]]), tensor([[3.7724,   -inf,   -inf,  ..., 3.1328, 3.6590, 3.8489]]), tensor([[    -inf,     -inf,     -inf,  ...,  -7.8979,  -7.7944, -11.4352]]), tensor([[-5.0041,    -inf,    -inf,  ..., -5.5928, -5.6329, -6.7607]]), tensor([[16.9060,    -inf,    -inf,  ...,    -inf,    -inf,    -inf]]), tensor([[ 4.7684,    -inf,    -inf,  ..., -4.7718, -4.7031, -6.6440]]), tensor([[ 3.5967,    -inf,    -inf,  ..., -0.2559, -0.4887, -1.7837]]), tensor([[  1.7885,     -inf,     -inf,  ...,  -8.9040,  -8.4750, -12.0667]]), tensor([[    -inf,     -inf,     -inf,  ..., -15.8636, -15.3132, -18.1436]]), tensor([[   -inf,    -inf,    -inf,  ..., 13.3971, 12.9880, 10.2999]])))]), 'stride': (160000, 0, 26667)}
```

and where it fails is when it tries to execute `outputs["tokens"].numpy()`. Would you mean maybe post process the output?

<|||||>Hi @panagiotidi , thanks for raising this issue. 

Yes, in this case as the error is being raise in the `postprocess` method, this is the one you'd need to adapt. Generally for custom workflows, it's probably easier to start with lower-level API such as `AutoModel` to define your steps and then move to something like a custom pipeline. 

If all that you want to do automatic speech recognition with the audio input, removing `return_dict_in_generate` from the `generate_kwargs` will work i.e.:

```python
from pathlib import Path
from transformers import pipeline, AutomaticSpeechRecognitionPipeline, Pipeline, GenerationConfig

audio_path = 'xxx.wav'

generate_kwargs = {'temperature': 1, 'max_length': 448, 'output_scores': True}

pipe = pipeline(
    model="openai/whisper-small",
    chunk_length_s=10,
    framework="pt",
    batch_size=1
)

print(pipe(audio_path, return_timestamps=True, generate_kwargs=generate_kwargs))
```<|||||>I am actually trying to implement the `--logprob_threshold` from the original paper of whisper as I would like to be able to experiment with it when transcribing. There is a relevant discussion [here](https://github.com/openai/whisper/discussions/654#discussioncomment-4510801), but as you said too, in order to implement in a pipeline, a custom implementation of post process is needed on the output results.

Will you maybe include in later versions?<|||||>@panagiotidi I don't know of any plans to add this at the moment. As this is a specific generation case, it's not something that's likely to be included into a pipeline. 

If I've understood `--logprob_threshold`, then the desire is to stop generation if the average logprob is below a certain threshold. In this case, a custom [`Constraint` class](https://huggingface.co/docs/transformers/v4.27.1/en/internal/generation_utils#transformers.Constraint) could be implemented and passed in to the `generate_kwargs`. Questions about an implementation of this is probably best placed in the [forums](https://discuss.huggingface.co/). 

As mentioned above, when applying custom code, it is easier to work from the `AutoModel` level first e.g. [adapting the examples in the docs](https://huggingface.co/docs/transformers/v4.27.1/en/model_doc/whisper#transformers.WhisperForConditionalGeneration.forward.example).<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.