whisper large
Whisper large is a pre-trained Transformer-based encoder-decoder model for automatic speech recognition and speech translation, developed by OpenAI. It was trained on 680k hours of labeled speech data using large-scale weak supervision and is released under the Apache-2.0 license. The official model card notes that the large-v2 variant is recommended in place of this original large model, as it was trained for 2.5x more epochs with regularization and surpasses its performance without architecture changes.
At a glance
- Parameters
- 1.54B
- Architecture
- whisper
- License
- apache-2.0
- Disk space
- 5.7 GB
- Software
- Transformers
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Add my hardwareBest for
- Automatic speech recognition in the same language as the input audio
- Speech translation from one language to another
- Multilingual transcription across many supported languages
- Zero-shot generalization to new datasets and domains without fine-tuning
- English speech recognition on datasets such as LibriSpeech (clean and other splits)
Tradeoffs
- The model card recommends using the large-v2 model instead of this original large model
- No performance or hardware requirements are provided in the supplied data
- No details are given about audio length, real-time processing, or streaming
- No information is provided about computational cost or resource needs
- The model card does not describe specific failure modes, biases, or robustness limitations beyond noting its general capabilities
Ways to run it
Transformers · See official guide
Repository-specific example documented in the publisher model card; confirm dependencies and platform in the source.
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
>>> model.config.forced_decoder_ids = None
>>> # 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)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
>>> 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.']Explore its uses
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