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 pytorch_model.bin from kosamit/whisper-tiny-ja: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/kosamit/whisper-tiny-ja/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kosamit/whisper-tiny-ja/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kosamit/whisper-tiny-ja/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 94b8a90e375ef9880a80e777b380bbd430e03677b51e591a34c834086e1c5720
- Size of remote file:
- 151 MB
- SHA256:
- c9e0d0e038748d519fe00a4351e3bc5a60ec172715fbda255183378d986ed5f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.