Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mjavadf/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjavadf/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mjavadf/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mjavadf/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("mjavadf/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfba4cc35d67fa669f0adecfb9553db413134ad9e6833e8a9007df6c741113fa
- Size of remote file:
- 5.18 kB
- SHA256:
- 9c62b1212718445e77ed88dbf05133e2eb07409d5c6f9d456fc38f9b9f509a84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.