Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base") - Notebooks
- Google Colab
- Kaggle
Commit History
Correct long-form generation config parameters 'max_initial_timestamp_index' and 'prev_sot_token_id'. (#29) 8c1db9b verified
add special tokens for fast (#25) 013fe3b
add timestamp tokens (#23) 894f251
Adding `safetensors` variant of this model (#22) 55ccf2b
Update generation config with word-level alignment heads (#21) 4ced08d
Update README.md ba19fed
Update generation_config.json to suppress task tokens (#15) 89144d6
Update config.json to suppress task tokens (#14) 4147011
Update the pad token (#12) 4e31d12
Add Flax weights 4495b69
sanchit-gandhi commited on