fix: [whisper] don't overwrite GenerationConfig's return_timestamps when return_timestamps is not passed to generate function

[hrl]

What does this PR do?

When WhisperGenerationMixin.generate called without return_timestamps, GenerationConfig's return_timestamps will be overwritten to None.

It's okay if we can control generate's arguments. But in some particular case, for example, in Trainer.evaluation_loop, then in Seq2SeqTrainer.prediction_step, there is no way to pass gen_kwargs to generate function. (#19777)
Which makes it impossible to generate with timestamps to calculate generative metrics when training.

This PR fixes this case by initializing return_timestamps to GenerationConfig's value.

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@patrickvonplaten @sanchit-gandhi

[amyeroberts]

Possibly cc @gante as it relates to generation

[amyeroberts]

cc @kamilakesbi

[kamilakesbi]

Hi @hrl,

Thanks for working on this! it LGTM :)
@amyeroberts we should be able to merge

[amyeroberts]

Thanks for handling this!

Could you add a test?

I'd just like to confirm this behaviour is consistent with the rest of the generation API cc @gante

[kamilakesbi]

@hrl did you have time to iterate on this ? thank you!

[hrl]

@hrl did you have time to iterate on this ? thank you!

@kamilakesbi conflicts resolved.

Could you add a test?
I'd just like to confirm this behaviour is consistent with the rest of the generation API cc @gante

@amyeroberts I'm not familiar with the rest of the generation API. Should I write a test for this?

The behavior will be different only when GenerationConfig.return_timestamps == True and return_timestamps is not passed to generate.

[amyeroberts]

@amyeroberts I'm not familiar with the rest of the generation API. Should I write a test for this?

Oh, no worries, this was a question for @gante, our generate king and who knows all the ins-and-outs best :)

[amyeroberts]

Gentle ping @gante

[gante]

Apologies for the delayed review, and thank you for fixing 🙏

[gante]

(@amyeroberts @kamilakesbi the root cause for the issue behind this PR is that Whisper's generate breaks a few assumptions present in the global generate, such as how the generation config and the generate kwargs interact with each other. Some of our tools, like Trainer, are built with the general case in mind -- I will try to nudge Whisper towards the general case whenever I see the opportunity 🤗 )