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:
- 4aab429d770180817e6f212bfdb2da615afa8aa1e01f195a05617fe70e9cd313
- Size of remote file:
- 3.1 GB
- SHA256:
- 89d76af87c627c8a530615d241fa48d9349e7cd99fb14a2d28c7b767fce70718
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.