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Add torchcodec in docstrings/tests for datasets 4.0 (#39156)

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* fix dataset run_object_detection

* bump version

* keep same dataset actually

* torchcodec in docstrings and testing utils

* torchcodec in dockerfiles and requirements

* remove duplicate

* add torchocodec to all the remaining docker files

* fix tests

* support torchcodec in audio classification and ASR

* [commit to revert] build ci-dev images

* [commit to revert] trigger circleci

* [commit to revert] build ci-dev images

* fix

* fix modeling_hubert

* backward compatible run_object_detection

* revert ci trigger commits

* fix mono conversion and support torch tensor as input

* revert map_to_array docs + fix it

* revert mono

* nit in docstring

* style

* fix modular

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Quentin Lhoest
2025-07-08 17:06:12 +02:00
committed by GitHub
parent 1255480fd2
commit 1ecd52e50a
78 changed files with 448 additions and 350 deletions
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9
docs/source/en/model_doc/speech_to_text_2.md
View File

@@ -61,19 +61,16 @@ predicted token ids.
- Step-by-step Speech Translation
```python
>>> import torch
>>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
>>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> def map_to_array(example):
... example["speech"] = example["audio"]["array"]
... return example
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

6
docs/source/en/model_doc/wav2vec2.md
View File

@@ -172,9 +172,9 @@ Otherwise, [`~Wav2Vec2ProcessorWithLM.batch_decode`] performance will be slower
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> def map_to_array(batch):
... batch["speech"] = batch["audio"]["array"]
... return batch
>>> def map_to_array(example):
... example["speech"] = example["audio"]["array"]
... return example
>>> # prepare speech data for batch inference