[sw00]

What does this PR do?

Add the missing @property annotation on SpeechT5Config which causes failures on AutomaticSpeechRecognitionPipeline's preprocessing steps if chunk_length_s is specified.

Specifically, the following line of code will fail since getattr will resolve to a method instead of the value:

align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1)

This stacktrace is generated on this error:

Traceback (most recent call last):
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/litserve/loops/simple_loops.py", line 88, in run_single_loop
    y = _inject_context(
        ^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/litserve/loops/base.py", line 48, in _inject_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/src/omniserve/api.py", line 19, in predict
    return self.model.predict(audio_data)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/src/omniserve/model.py", line 63, in predict
    output = self.pipeline(input)
             ^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/automatic_speech_recognition.py", line 275, in __call__
    return super().__call__(inputs, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1459, in __call__
    return next(
           ^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py", line 126, in __next__
    item = next(self.iterator)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py", line 271, in __next__
    processed = self.infer(next(self.iterator), **self.params)
                           ^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 734, in __next__
    data = self._next_data()
           ^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 790, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/fetch.py", line 33, in fetch
    data.append(next(self.dataset_iter))
                ^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py", line 188, in __next__
    processed = next(self.subiterator)
                ^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/sett/src/model-cloud/servers/omniserve/.venv/lib/python3.12/site-packages/transformers/pipelines/automatic_speech_recognition.py", line 450, in preprocess
    chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
                          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~
TypeError: unsupported operand type(s) for /: 'float' and 'method'

Fixes # (issue)

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• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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@Rocketknight1 @eustlb

[Rocketknight1]

Yep, this is an obvious bug. Thank you for the fix!

[Rocketknight1]

Not sure why CircleCI is failing - @ydshieh can you force-merge this? It's a one-line fix that shouldn't cause problems.

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: speecht5

[ArthurZucker]

Thanks for the PR, and sorry it didn't make it in! This branch is based on v4, and v5 is very different, so rather than rebasing we'd love to invite you to open a new PR against main using our latest tools and good practices:

• Transformers tenets
• mlinter and the modeling rules it enforces (make typing)
• Modular transformers
• Contributing guide

Closing this one in the meantime, thanks again!