Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bladevb/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bladevb/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bladevb/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bladevb/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("bladevb/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b348e62778f3fdc78e360e0836c4c719e94a2917621e4cba95994d60b5ffcee
- Size of remote file:
- 967 MB
- SHA256:
- 5d651a94a7ecef4055a9eb36433a06647346fef730d589c6d507b050df949e09
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