Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use jason1i/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jason1i/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jason1i/whisper-small-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jason1i/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("jason1i/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jason1i/whisper-small-dv: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/jason1i/whisper-small-dv/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jason1i/whisper-small-dv/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jason1i/whisper-small-dv/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- deb4302499af38a5e8170c6e138b56d5aa6fe1560be010a4c53d92df366b1424
- Size of remote file:
- 967 MB
- SHA256:
- 81231b63b45ac2f265731de005cf5c33299aea5238604a8397ab885446795dae
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