Instructions to use aadel4/Wav2vec_Classroom_WSP_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/Wav2vec_Classroom_WSP_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_WSP_FT")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT") model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,6 +6,7 @@ base_model:
|
|
| 6 |
pipeline_tag: automatic-speech-recognition
|
| 7 |
tags:
|
| 8 |
- wav2vec2
|
|
|
|
| 9 |
---
|
| 10 |
## Model Card: Wav2vec_Classroom_WSP_FT
|
| 11 |
|
|
|
|
| 6 |
pipeline_tag: automatic-speech-recognition
|
| 7 |
tags:
|
| 8 |
- wav2vec2
|
| 9 |
+
library_name: transformers
|
| 10 |
---
|
| 11 |
## Model Card: Wav2vec_Classroom_WSP_FT
|
| 12 |
|