Instructions to use Vkt/model-dataaugmentationpipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vkt/model-dataaugmentationpipe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vkt/model-dataaugmentationpipe")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vkt/model-dataaugmentationpipe") model = AutoModelForCTC.from_pretrained("Vkt/model-dataaugmentationpipe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 6300
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1262092573
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b6b9b9ad5b0aff6b4cb6b0c0a4479be5c84fea92791d79e5d647c0667df22d7b
|
| 3 |
size 1262092573
|
runs/Jul04_14-48-31_dsbrwavvec2-0/events.out.tfevents.1656948057.dsbrwavvec2-0.282365.7
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:ec97a2506418cd6ad57d6b548e4fcea88a093d1ba4a4996fc7e5cd25c2ef5611
|
| 3 |
+
size 14780
|