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
Add pad token to tokenizer
#9
by sanchit-gandhi - opened
Whisper large is missing the pad token, which is otherwise added in the tiny-medium models (e.g. https://huggingface.co/openai/whisper-medium/blob/main/special_tokens_map.json#L125). This PR adds the pad token for the large checkpoint.
Thanks
ArthurZ changed pull request status to merged