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:
- 891135fbe806837214e77278fe3e61cb382781d527aeb96d42298d3bafafeb60
- Size of remote file:
- 1.27 GB
- SHA256:
- bdcd0dba60a912a712ec33f72dbcf036bd5096dedf5bf310184d31c242bacce0
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