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
ONNX implementation
#17
by kirankumaram - opened
Can anyone suggest how to use the exported whisper-large model in ONXX version for transcription or translation?
You can use it with ORT pipeline: https://github.com/huggingface/optimum/pull/420#issue-1406136285
Or ONNX runtime: https://huggingface.co/docs/transformers/serialization#exporting-a-model-to-onnx (here you'll need to modify the template code snippet to pass the appropriate inputs to the ONNX model)