Instructions to use aadel4/Wav2vec_Classroom_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/Wav2vec_Classroom_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_FT")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_FT") model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_FT") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,9 +1,11 @@
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
base_model:
|
| 4 |
-
- aadel4/Wav2vec_Classroom
|
| 5 |
- facebook/wav2vec2-large-robust
|
|
|
|
| 6 |
pipeline_tag: automatic-speech-recognition
|
|
|
|
|
|
|
| 7 |
library_name: transformers
|
| 8 |
---
|
| 9 |
## Model Card: Wav2vec_Classroom_FT
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
base_model:
|
|
|
|
| 4 |
- facebook/wav2vec2-large-robust
|
| 5 |
+
- aadel4/Wav2vec_Classroom
|
| 6 |
pipeline_tag: automatic-speech-recognition
|
| 7 |
+
tags:
|
| 8 |
+
- wav2vec2
|
| 9 |
library_name: transformers
|
| 10 |
---
|
| 11 |
## Model Card: Wav2vec_Classroom_FT
|