Whisper

This model was released on 2022-12-06 and added to Hugging Face Transformers on 2022-10-05.

Whisper

Whisper is a 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 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.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="automatic-speech-recognition",
    model="openai/whisper-large-v3-turbo",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")

# pip install datasets
import torch
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",
    dtype=torch.float16,
    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, dtype=torch.float16)

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

Notes

• Whisper relies 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