Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small") - Notebooks
- Google Colab
- Kaggle
Commit History
Correct long-form generation config parameters 'max_initial_timestamp_index' and 'prev_sot_token_id'. (#37) 998cb1a verified
add special tokens for fast (#33) e34e8ae
add timestamp tokens (#31) b93f5c7
Adding `safetensors` variant of this model (#28) 4b5b137
Adding `safetensors` variant of this model (#30) 1450e63
Update generation config with word-level alignment heads (#29) 15291b7
update cv13 zero shot results dc81969
Update README with zero-shot cv13 results 3d64a9b
Update README.md f674449
Update generation_config.json to suppress task tokens (#23) e0bba93
Update the pad token (#19) 358bfe9
Add Flax weights 2d276f0
sanchit-gandhi commited on