Instructions to use Umong/w2v-bert-2.0-ben10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umong/w2v-bert-2.0-ben10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Umong/w2v-bert-2.0-ben10")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Umong/w2v-bert-2.0-ben10") model = AutoModelForCTC.from_pretrained("Umong/w2v-bert-2.0-ben10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 4100
Browse files- model.safetensors +1 -1
- training_args.bin +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2423089260
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:00978f56a41622ae9cf2c5f5abb038013c50eb920222565327eea1bf01a422da
|
| 3 |
size 2423089260
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4920
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a44cbdcfd9934dc6d17b9f56b5a82ea7da4a80cb0daebe0d5645d79cf8688680
|
| 3 |
size 4920
|