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:
- f6e6ff9703f58f1c51d9a756e76e546a07dd7be21128b34581d40d07d125986d
- Size of remote file:
- 5.24 kB
- SHA256:
- 93155cbf2f42b5bdca94ac9032a686f40fdb90d96687ebcb16b99fee1458679c
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