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
whisper-small-dv / runs /Dec26_16-23-09_win-MS-7E02 /events.out.tfevents.1703582596.win-MS-7E02.13808.0
- Xet hash:
- 160690baaba39717a01f21bb7948a36bd47aefe869fb3168a98eabe4551d4682
- Size of remote file:
- 9.99 kB
- SHA256:
- f90e91c4c74015b86551abb3ec76bc7f35f513d122eec68bebf171e82cd066b8
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