Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use cleandata/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cleandata/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cleandata/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cleandata/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("cleandata/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51263cfd5e8c1401f4554379001d2e275aade3a6ddca99348b761572f25b2667
- Size of remote file:
- 4.09 kB
- SHA256:
- ab30cbd9d323c709e127e7e8f1f421752efd75dabe2d82f2c943b20859cbd7ec
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