Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add missing merge to tokenizer (#100)
Verified signature- Add missing merge to tokenizer (e1127ece7b25bf859ddb883688724073110441d4)ae46427
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add timestamp tokens (#64)
- add timestamp tokens (5353231917b627fc88cd2370177e4a1e1a6414b3)0007ffc
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Upload tokenizer (#26)
- Upload tokenizer (33482313ea52a0bc9ee1303ac23d3f2d36a90932)5c6a0f3