Instructions to use minjibi/test1000v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minjibi/test1000v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="minjibi/test1000v2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("minjibi/test1000v2") model = AutoModelForCTC.from_pretrained("minjibi/test1000v2") - Notebooks
- Google Colab
- Kaggle
test1000v2
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1262173425
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c2fe25cc57706f9eda7e36c0c99d8a5ec3834b5a11f7b0fa6856d5d4f69f440
|
| 3 |
size 1262173425
|
runs/Oct05_18-00-06_5e0e97ad45ef/events.out.tfevents.1664992869.5e0e97ad45ef.65.0
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:a76985d5730da8288dc920003c479c23147359e9b2158e7a07b645232a16cc2e
|
| 3 |
+
size 8486
|