Instructions to use hf-internal-testing/tiny-random-SEWForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SEWForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-SEWForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SEWForCTC") model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-SEWForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba3db98f2bcc38de1d1e6bd9a8a37d41aca8f5902a94ea9fcb4adebb7cc7fc1b
- Size of remote file:
- 225 kB
- SHA256:
- 114f07a12142ce9f616617aa5a4087d0cd06316fa2c3a98e084b8bcdfb159951
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.