# Is there a complete Speech2Text example?

**URL:** <https://discuss.huggingface.co/t/is-there-a-complete-speech2text-example/12234>\
**Category:** 🤗Transformers\
**Created:** [November 24, 2021, 9:36am UTC](https://discuss.huggingface.co/t/is-there-a-complete-speech2text-example/12234 "2021-11-24T09:36:46Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![sfalk](https://avatars.discourse-cdn.com/v4/letter/s/5e9695/32.png) [@sfalk](https://discuss.huggingface.co/u/sfalk)\
**Post date:** [November 24, 2021, 9:36am UTC](https://discuss.huggingface.co/t/is-there-a-complete-speech2text-example/12234/1 "2021-11-24T09:36:46Z")

</div>

Hi!

I am currently trying to train a `Speech2TextModel` from 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 a `Speech2TextTokenizer`. I got my `spm_file` but how exactly should the `vocab_file` look like? Do I generate this from the `SentencePieceProcesor`? Why can’t I set the vocab file created by `sentencpiece` and 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 the `Trainer` or the `Seq2SeqTrainer`. My biggest problem is the `data_collator` as I have no clue what it’s supposed to return.

Right now I am returning the following:

```auto
@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 set `label_smoothing_factor=1` for the `TrainingArguments` I get either a `KeyError: 'logits'` or `KeyError: 'loss'`.

Can somebody help me out here?