Instructions to use BrunoHays/whisper-large-v3-O2-fp16-gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BrunoHays/whisper-large-v3-O2-fp16-gpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BrunoHays/whisper-large-v3-O2-fp16-gpu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("BrunoHays/whisper-large-v3-O2-fp16-gpu") model = AutoModelForSpeechSeq2Seq.from_pretrained("BrunoHays/whisper-large-v3-O2-fp16-gpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a881362e7fba34a664d6d4b580e7d3c3373714d80835a55dbf99984dda24656
- Size of remote file:
- 1.95 GB
- SHA256:
- 166d1ad62d6a4c3622b37a2101a0f88c456e580c92cab8117ffaed3f9a51152f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.