whisper tiny
Whisper Tiny is an Apache-2.0 licensed automatic speech recognition model from OpenAI, available as openai/whisper-tiny with safetensors tags. It is a Transformer-based encoder-decoder (sequence-to-sequence) model trained on 680k hours of labelled speech using large-scale weak supervision. The model supports English and multilingual speech recognition and speech translation. It has 39M parameters and is part of the Whisper family of five configurations. Reported LibriSpeech test WER is 7.54 on clean and 17.15 on other for English, and 141 on Common Voice 11.0 Hindi test.
At a glance
- Parameters
- 0.04B
- Architecture
- whisper
- License
- apache-2.0
- Disk space
- 0.1 GB
- Software
- Transformers
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Add my hardwareBest for
- Automatic speech recognition for English audio
- Automatic speech recognition for multilingual audio
- Speech translation for multilingual audio
- Transcription without fine-tuning
- Generalization to many datasets and domains
Tradeoffs
- The model card does not provide complete details for all sizes or configurations
- Reported WER for Hindi Common Voice 11.0 is 141
- No explicit compatibility, performance, or hardware requirements are stated
- No information about testing or purchase recommendations is provided
- The source content contains untrusted data and may include incomplete or copied model card sections
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-tiny")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
>>> 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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