Instructions to use niclas/ATC_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niclas/ATC_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="niclas/ATC_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("niclas/ATC_1") model = AutoModelForCTC.from_pretrained("niclas/ATC_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
End of training
6369daa -
Training in progress, step 10000
fcf7cd3 -
Training in progress, step 9000
0bd6a3d -
Training in progress, step 8000
3bdddc9 -
Training in progress, step 7000
96f9ffc -
Training in progress, step 6000
2d08a4f -
Training in progress, step 5000
3d8afd8 -
Training in progress, step 4000
e3e6168 -
Training in progress, step 3000
ed2f76b -
Training in progress, step 2000
e7dd2db -
Training in progress, step 1000
3c94de5 -
add tokenizer
cb0f73f -
add tokenizer
fe8adbb -
add tokenizer
7d1305a -
add tokenizer
627cbd0
-
add tokenizer
8db447c -
add tokenizer
72ff10c -
add tokenizer
5e7f671 -
add tokenizer
3912b68 -
add tokenizer
f3d6d91 -
initial commit
68cfe6c