whisper.en

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. The original code repository can be found here.

Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card.

Model details

Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.

The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio.

Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the Hugging Face Hub. The checkpoints are summarised in the following table with links to the models on the Hub:

Usage

This checkpoint is an English-only model, meaning it can be used for English speech recognition. Multilingual speech recognition or speech translation is possible through use of a multilingual checkpoint.

To transcribe audio samples, the model has to be used alongside a WhisperProcessor.

The WhisperProcessor is used to:

1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

Transcription

>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-base.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base.en")

>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features

>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
[' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.']

>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']

The context tokens can be removed from the start of the transcription by setting skip_special_tokens=True.

Evaluation

This code snippet shows how to evaluate Whisper base.en on LibriSpeech test-clean:

>>> from datasets import load_dataset
>>> from transformers import WhisperForConditionalGeneration, WhisperProcessor
>>> import torch
>>> from evaluate import load

>>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")

>>> processor = WhisperProcessor.from_pretrained("openai/whisper-base.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base.en").to("cuda")

>>> def map_to_pred(batch):
>>>     audio = batch["audio"]
>>>     input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
>>>     batch["reference"] = processor.tokenizer._normalize(batch['text'])
>>>
>>>     with torch.no_grad():
>>>         predicted_ids = model.generate(input_features.to("cuda"))[0]
>>>     transcription = processor.decode(predicted_ids)
>>>     batch["prediction"] = processor.tokenizer._normalize(transcription)
>>>     return batch

>>> result = librispeech_test_clean.map(map_to_pred)

>>> wer = load("wer")
>>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
4.271408904897505

Long-Form Transcription

The Whisper model is intrinsically designed t