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# Processors

Multimodal models require a preprocessor capable of handling inputs that combine more than one modality. Depending on the input modality, a processor needs to convert text into an array of tensors, images into pixel values, and audio into an array of tensors with the correct sampling rate.

For example, [PaliGemma](./model_doc/paligemma) is a vision-language model that uses the [SigLIP](./model_doc/siglip) image processor and the [Llama](./model_doc/llama) tokenizer. A [`ProcessorMixin`] class wraps both of these preprocessor types, providing a single and unified processor class for a multimodal model.

Call [`~ProcessorMixin.from_pretrained`] to load a processor. Pass the input type to the processor to generate the expected model inputs, input ids and pixel values.

```py
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
import requests

processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")

prompt = "answer en Where is the cat standing?"
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs
```

This guide describes the processor class and how to preprocess multimodal inputs.

## Processor classes

All processors inherit from the [`ProcessorMixin`] class which provides methods like [`~ProcessorMixin.from_pretrained`], [`~ProcessorMixin.save_pretrained`], and [`~ProcessorMixin.push_to_hub`] for loading, saving, and sharing processors to the Hub.

There are two ways to load a processor, with an [`AutoProcessor`] and with a model-specific processor class.

<hfoptions id="processor-class">
<hfoption id="AutoProcessor">

The [AutoClass](./model_doc/auto) API provides a simple interface to load processors without directly specifying the specific model class it belongs to.

Use [`~AutoProcessor.from_pretrained`] to load a processor.

```py
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
```

</hfoption>
<hfoption id="model-specific processor">

Processors are also associated with a specific pretrained multimodal model class. You can load a processor directly from the model class with [`~ProcessorMixin.from_pretrained`].

```py
from transformers import WhisperProcessor

processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
```

You could also separately load the two preprocessor types, [`WhisperTokenizerFast`] and [`WhisperFeatureExtractor`].

```py
from transformers import WhisperTokenizerFast, WhisperFeatureExtractor, WhisperProcessor

tokenizer = WhisperTokenizerFast.from_pretrained("openai/whisper-tiny")
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")
processor = WhisperProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
```

</hfoption>
</hfoptions>

## Preprocess

Processors preprocess multimodal inputs into the expected Transformers format. There are a couple combinations of input modalities that a processor can handle such as text and audio or text and image.

Automatic speech recognition (ASR) tasks require a processor that can handle text and audio inputs. Load a dataset and take a look at the `audio` and `text` columns (you can remove the other columns which aren't needed).

```py
from datasets import load_dataset

dataset = load_dataset("lj_speech", split="train")
dataset = dataset.map(remove_columns=["file", "id", "normalized_text"])
dataset[0]["audio"]
{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
         7.3242188e-04,  2.1362305e-04,  6.1035156e-05], dtype=float32),
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
 'sampling_rate': 22050}

dataset[0]["text"]
'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
```

Remember to resample the sampling rate to match the pretrained models required sampling rate.

```py
from datasets import Audio

dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
```

Load a processor and pass the audio `array` and `text` columns to it.

```py
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("openai/whisper-tiny")

def prepare_dataset(example):
    audio = example["audio"]
    example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
    return example
```

Apply the `prepare_dataset` function to preprocess the dataset. The processor returns `input_features` for the `audio` column and `labels` for the text column.

```py
prepare_dataset(dataset[0])
```
