Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use LeonM78Code/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeonM78Code/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="LeonM78Code/whisper-small-dv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("LeonM78Code/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("LeonM78Code/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LeonM78Code/whisper-small-dv: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/LeonM78Code/whisper-small-dv/resolve/main/training_args.bin
- Command line
-
hf download hf://LeonM78Code/whisper-small-dv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/LeonM78Code/whisper-small-dv/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- d33f6ae79a9651a1f67b96e3357fabfef8d5bea368e569d7311c83c97cfcb93d
- Size of remote file:
- 5.5 kB
- SHA256:
- 036e13859874852646f226ecb051e998e847dade0297fd78368df460c8423da3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.