Instructions to use rossevine/Model_G_P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_P with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_P")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_P") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_P") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1262028973
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ba951c057ae78e0a6259a96ead51e6670a40b34946a9f3e5a76a62fff08917e8
|
| 3 |
size 1262028973
|
runs/Aug31_02-27-13_hpc-Aquarium2/events.out.tfevents.1693423656.hpc-Aquarium2.66543.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c4ec3e0d866d059a5942df324617d8a0addad98077dd043cd9cec424884839e1
|
| 3 |
+
size 15493
|