Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Winmodel/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Winmodel/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Winmodel/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Winmodel/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("Winmodel/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5230f613ad261f104fc374eadfa44c8c286d99eab454352d1430f20fe3a69cf2
- Size of remote file:
- 967 MB
- SHA256:
- 20bc2fc59504c31a497547d4baaf72e08d95ab31a33c6130e32c65ee6d5b16e0
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