Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use TheRains/yt-special-batch4-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheRains/yt-special-batch4-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TheRains/yt-special-batch4-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("TheRains/yt-special-batch4-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("TheRains/yt-special-batch4-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from TheRains/yt-special-batch4-tiny: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/TheRains/yt-special-batch4-tiny/resolve/main/training_args.bin
- Command line
-
hf download hf://TheRains/yt-special-batch4-tiny/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/TheRains/yt-special-batch4-tiny/resolve/main/training_args.bin
4.16 kB
- Xet hash:
- 2056e960c09bfef5c10774c01517756a794cedb987d4b89e32326dc6747bf8be
- Size of remote file:
- 4.16 kB
- SHA256:
- cade79458db7c5b84dde3d0cda1d35042ff797db11122a18f48f73ad87a2eae6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.