Instructions to use kosamit/whisper-tiny-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kosamit/whisper-tiny-ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kosamit/whisper-tiny-ja")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kosamit/whisper-tiny-ja") model = AutoModelForSpeechSeq2Seq.from_pretrained("kosamit/whisper-tiny-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kosamit/whisper-tiny-ja: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/kosamit/whisper-tiny-ja/resolve/main/training_args.bin
- Command line
-
hf download hf://kosamit/whisper-tiny-ja/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kosamit/whisper-tiny-ja/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 29fcdefc1787d31d9ed15239f3998952c13bba599924715c226f9252de48d0cf
- Size of remote file:
- 4.66 kB
- SHA256:
- 11cc3bd8b48e8378359f45b1f3b289016aea9184d3129db796da65c58934b7d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.