Instructions to use rossevine/Model_G_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_G_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_G_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_G_Wav2Vec2") model = AutoModelForCTC.from_pretrained("rossevine/Model_G_Wav2Vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2000
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1262050737
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:526e60f7a3384742e5c1116599017795ec911a17672126fe2f98910d83fb8e98
|
| 3 |
size 1262050737
|
runs/Aug15_20-57-13_hpc-Aquarium2/events.out.tfevents.1692107860.hpc-Aquarium2.8251.0
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:edc98b2a1ad175bc03dddee999cb4744148620b11ddf19c4544f9ee5d7bc7acb
|
| 3 |
+
size 6809
|