Is there a complete Speech2Text example?
Hi!I am currently trying to train aSpeech2TextModelfrom scratch but I can’t seem to find a complete example on how to do this.I’ve started to go through it by myself but this turns out to be a trial & error kind of thing. For example, I don’t know how to create aSpeech2TextTokenizer. I got myspm_filebut how exactly should thevocab_filelook like? Do I generate this from theSentencePieceProcesor? Why can’t I set the vocab file created bysentencpieceand so on…Is there a comprehensive guide I overlooked?Update:I was able to proceed a bit further but only via debugging the code and some trial & error.I am able to start the training now but I am not sure if I am using all the right pieces here. I don’t know if I need to use theTraineror theSeq2SeqTrainer. My biggest problem is thedata_collatoras I have no clue what it’s supposed to return.Right now I am returning the following:@dataclass
class Speech2TextCollator:

    def __init__(self, processor: Speech2TextProcessor):
        self.processor = processor

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        inputs = [torch.Tensor(f["inputs"]) for f in features]
        targets = [torch.Tensor(f["targets"]) for f in features]
        # Create batches
        inputs_batch = pad_sequence(inputs, batch_first=True)
        targets_batch = pad_sequence(targets, batch_first=True).long()
        attention_mask = pad_sequence([f["attention_mask"] for f in features], batch_first=True).long()
        return dict(
            input_features=inputs_batch,
            decoder_input_ids=targets_batch,
            attention_mask=attention_mask,
            labels=targets_batch
        )Depending on whether I setlabel_smoothing_factor=1for theTrainingArgumentsI get either aKeyError: 'logits'orKeyError: 'loss'.Can somebody help me out here?