Instructions to use hf-tiny-model-private/tiny-random-SEWForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SEWForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-SEWForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-SEWForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-SEWForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59541a7944f9a00c430578d70172fe5be30c8503bc50da317b8a0dac4ffe53b3
- Size of remote file:
- 208 kB
- SHA256:
- a54c328ba7c10599886800c9cd53e3f754e1d517d185e7a62d4d71e0b1edeb09
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.