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*This model was published in HF papers on 2022-12-06 and contributed to Hugging Face Transformers on 2022-10-05.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

# Whisper

[Whisper](https://huggingface.co/papers/2212.04356) is an encoder-decoder (sequence-to-sequence) transformer pretrained on 680,000 hours of labeled audio data. This amount of pretraining data enables zero-shot performance on audio tasks in English and many other languages. The decoder allows Whisper to map the encoders learned speech representations to useful outputs, such as text, without additional fine-tuning. Whisper just works out of the box.

You can find all the original Whisper checkpoints under the [Whisper](https://huggingface.co/collections/openai/whisper-release-6501bba2cf999715fd953013) collection.

> [!TIP]
> Click on the Whisper models in the right sidebar for more examples of how to apply Whisper to different audio tasks.

The example below demonstrates how to automatically transcribe speech into text with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="automatic-speech-recognition",
    model="openai/whisper-large-v3-turbo",
    device=0
)
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
```

</hfoption>
<hfoption id="AutoModel">

```python
# pip install datasets
from datasets import load_dataset

from transformers import AutoProcessor, WhisperForConditionalGeneration


processor = AutoProcessor.from_pretrained(
    "openai/whisper-large-v3-turbo",
)
model = WhisperForConditionalGeneration.from_pretrained(
    "openai/whisper-large-v3-turbo",
    device_map="auto",
    attn_implementation="sdpa"
)

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = ds[0]["audio"]

input_features = processor(
    audio_sample["array"],
    sampling_rate=audio_sample["sampling_rate"],
    return_tensors="pt"
).input_features
input_features = input_features.to(model.device)

predicted_ids = model.generate(input_features, cache_implementation="static")
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
transcription[0]
```

</hfoption>
</hfoptions>

## Notes

- Whisper relies on a custom [`generate`] for inference, make sure to check the docs below.
- The [`WhisperProcessor`] can be used for preparing audio and decoding predicted ids back into text.

## WhisperConfig

[[autodoc]] WhisperConfig

## WhisperTokenizer

[[autodoc]] WhisperTokenizer
    - set_prefix_tokens
    - get_special_tokens_mask
    - save_vocabulary
    - batch_decode
    - decode
    - basic_normalize
    - normalize

## WhisperTokenizerFast

[[autodoc]] WhisperTokenizerFast
    - set_prefix_tokens
    - get_special_tokens_mask
    - save_vocabulary
    - batch_decode
    - decode
    - basic_normalize
    - normalize

## WhisperFeatureExtractor

[[autodoc]] WhisperFeatureExtractor
    - __call__

## WhisperProcessor

[[autodoc]] WhisperProcessor
    - __call__
    - from_pretrained
    - save_pretrained
    - batch_decode
    - decode

## WhisperModel

[[autodoc]] WhisperModel
    - forward
    - _mask_input_features

## WhisperForConditionalGeneration

[[autodoc]] WhisperForConditionalGeneration
    - forward
    - generate

## WhisperForCausalLM

[[autodoc]] WhisperForCausalLM
    - forward

## WhisperForAudioClassification

[[autodoc]] WhisperForAudioClassification
    - forward
