whisper base
Whisper base is a pre-trained Transformer encoder-decoder model for automatic speech recognition and speech translation. It was trained on 680k hours of labelled speech data using large-scale weak supervision by OpenAI. The model is available under the Apache-2.0 license and uses the safetensors format. It is a multilingual model, supporting many languages as listed on its model card. Reported metrics include a test WER of 5.008769117619326 on LibriSpeech clean, 12.84936273212057 on LibriSpeech other, and 131 on Common Voice 11.0 Hindi. The model card states it can generalise to many datasets and domains without fine-tuning. It comes in five configurations of varying model sizes, and this base checkpoint is one of them.
At a glance
- Parameters
- 0.07B
- Architecture
- whisper
- License
- apache-2.0
- Disk space
- 0.3 GB
- Software
- Transformers
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Add my hardwareBest for
- Automatic speech recognition for multilingual audio.
- Speech translation between languages.
- Transcription of English audio.
- Transcription of non-English audio in supported languages.
- General-purpose speech recognition without fine-tuning.
Tradeoffs
- Model card content is partly written by Hugging Face and copied from the original model card.
- No performance guarantees or testing claims beyond the reported metrics are provided.
- May not perform equally well on all datasets or domains.
- Reported WER for Hindi on Common Voice 11.0 is 131.
- No specific hardware requirements or compatibility details are provided.
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-base")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
>>> 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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