Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use ptah23/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ptah23/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ptah23/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ptah23/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("ptah23/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a4c30ff25d61d218689ab1c1783e247caa34071d8794c3f57fffc3ee92b6cb7
- Size of remote file:
- 967 MB
- SHA256:
- 917dd5870472dad8794ac5c007057fcac99d0d06d9fde1621ea98227ee16d27b
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