Instructions to use Kleber/output_dir_run_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kleber/output_dir_run_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kleber/output_dir_run_2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kleber/output_dir_run_2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kleber/output_dir_run_2") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:089b65a27c3d0d4ac304b7aedbc9a30b8f416fa4941b1cbbfdf588bb314ae12b
|
| 3 |
+
size 966995080
|