Instructions to use rossevine/Model_ALL_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rossevine/Model_ALL_Wav2Vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_ALL_Wav2Vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rossevine/Model_ALL_Wav2Vec2") model = AutoModelForCTC.from_pretrained("rossevine/Model_ALL_Wav2Vec2") - 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 sha256:16d2579745c27edc5bffd6e15d337503a31648e0d08488c5ce754faa30e6e9c0
|
| 3 |
size 1262028973
|
runs/Aug29_14-53-38_hpc-Aquarium2/events.out.tfevents.1693295744.hpc-Aquarium2.5353.0
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:f1764deb9eb4ccae9b574f6b28df28d9835eb10ce86c7c6c9081930552281d3a
|
| 3 |
+
size 29095
|