Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add missing merge to tokenizer (#50)
Verified signature- Add missing merge to tokenizer (85d6fba6e0e7321eae54169e6ac106f3f702f32f)4ef9b41
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add special tokens for fast (#39)
- add special tokens for fast (2cd9782cda0637170ca1709e4fefe1a5223961f6)20d6ad5
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Upload tokenizer.json (#28)
- Upload tokenizer.json (d749371bfe914cc1a3a9e6bee38a38825a3c697b)240e349
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Changes for fast tokenizer (#20)
- Update tokens (4080f4e993d5ae739ccdf4359347a24ea63e5ca0) - Add tokenizer.json (906330ca580f0d119131905cc24dc97534acc616) Co-authored-by: Jonatan Kłosko <jonatanklosko@users.noreply.huggingface.co>477a286