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
whisper large v3 turbo
#160
by deepdml - opened
https://huggingface.co/deepdml/whisper-large-v3-turbo
New released model from OpenAI: "the turbo model is an optimized version of large-v3 that offers faster transcription speed with a minimal degradation in accuracy."
You can use deepdml/whisper-large-v3-turbo to get speedup ~5x inference!
On Colab GPU T4
| Model | Time (s) | Relative speed |
|---|---|---|
| openai/whisper-large-v3 | 10.36 | 1x |
| deepdml/whisper-large-v3-turbo | 2.11 | ~5x |
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