Instructions to use hf-internal-testing/tiny-random-MCTCTForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MCTCTForCTC 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-MCTCTForCTC")# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-MCTCTForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bc77ec34c48f426555b4e95a105919ad93ad293b4a20498de747a266e240edf
- Size of remote file:
- 23.3 MB
- SHA256:
- 02718450804e1b1441a465cf7c7d17420bcfe69e2c8880c3ac45129f51c8ad39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.