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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v2") - Notebooks
- Google Colab
- Kaggle
Question: mobile deployment — has anyone tested this?
#124
by 3morixd - opened
We test models on 40 phones (Snapdragon 865, 8GB RAM) at Dispatch AI (FZE, UAE).
Question: has anyone benchmarked this on mobile? Specifically:
- Inference speed (tokens/sec)?
- Model size after Q4_K_M quantization?
- RAM usage after load?
Happy to share our phone farm results if there's interest.
- Dispatch AI (FZE), Sharjah UAE