Instructions to use alakxender/whisper-large-v3-cv17-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/whisper-large-v3-cv17-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-large-v3-cv17-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alakxender/whisper-large-v3-cv17-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("alakxender/whisper-large-v3-cv17-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3703c4a0e9c7a5fd5ceaceb82bb1208d9ccb7f61f336dc1483437e7ff95d0b1
- Size of remote file:
- 4.99 GB
- SHA256:
- 81cc9f4ca8fe9ffde671a5ae57a131635a861a7005b87ea70c7c4f8f4b0476a8
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