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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large") - Notebooks
- Google Colab
- Kaggle
Commit History
Correct long-form generation config parameters 'max_initial_timestamp_index' and 'prev_sot_token_id'. (#48) c24901c verified
Correct forced decoder ids (#44) d5522fe
add special tokens for fast (#39) 20d6ad5
add timestamp tokens (#37) f870a1a
Adding `safetensors` variant of this model (#36) bc7ea18
Update generation config with word-level alignment heads (#35) 58d3008
Update README.md e80f01d
Update generation_config.json to suppress task tokens (#31) 27b5bb3
Update config.json to suppress task tokens (#30) 04f04cb
Update the pad token (#29) 6fdb54a
Add Flax weights 3383020
sanchit-gandhi commited on