Instructions to use casual/whisper_tiny_til2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use casual/whisper_tiny_til2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="casual/whisper_tiny_til2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("casual/whisper_tiny_til2") model = AutoModelForSpeechSeq2Seq.from_pretrained("casual/whisper_tiny_til2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from casual/whisper_tiny_til2: direct link, hf CLI and curl.
- Browser
- Download file 4.67 kB
-
https://huggingface.co/casual/whisper_tiny_til2/resolve/main/training_args.bin
- Command line
-
hf download hf://casual/whisper_tiny_til2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/casual/whisper_tiny_til2/resolve/main/training_args.bin
4.67 kB
- Xet hash:
- d8559d18fd2a55eefa9f5aa3c1a8270f111abc9fa0fae240ba0fd41a381daa80
- Size of remote file:
- 4.67 kB
- SHA256:
- 1a3fcc030c82067fe1fdc27db500c98ed49932339650d9f576e21dbcb34b0731
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.