Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Ukrainian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use gencgeray/whisper-tiny-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gencgeray/whisper-tiny-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gencgeray/whisper-tiny-uk")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("gencgeray/whisper-tiny-uk") model = AutoModelForSpeechSeq2Seq.from_pretrained("gencgeray/whisper-tiny-uk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gencgeray/whisper-tiny-uk: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/gencgeray/whisper-tiny-uk/resolve/main/training_args.bin
- Command line
-
hf download hf://gencgeray/whisper-tiny-uk/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gencgeray/whisper-tiny-uk/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 4d345c1799dce8b31e36258112d3aca382895584d1bcaa4f1488006dae8e62b3
- Size of remote file:
- 5.43 kB
- SHA256:
- 88061278a0806db594cdbdbeb4b4fc331930b5be97a26031949ab2c4226cacd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.