Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bunduli/whisper-small-dv-second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunduli/whisper-small-dv-second with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bunduli/whisper-small-dv-second")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("bunduli/whisper-small-dv-second") model = AutoModelForMultimodalLM.from_pretrained("bunduli/whisper-small-dv-second") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11584a6b3835846fe49a8d6058f46e28a362d430b73cb3fb02fcacd5825c7c5d
- Size of remote file:
- 5.43 kB
- SHA256:
- 42e6c7256d072347764cd1d960163ff298391a66938d83619b8ef86cef5ab63c
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