Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Solid work — testing for mobile deployment
#238
by 3morixd - opened
We're always scanning HuggingFace for models that could work on mobile. This one caught our attention.
At Dispatch AI (FZE, Sharjah UAE), we test models on a 40-phone farm (Samsung S20 FE, Snapdragon 865). If this fits the mobile deployment criteria, we'll quantize and benchmark it.
Appreciate the open release. Open weights move the whole field forward.
— Dispatch AI (FZE), Sharjah UAE