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
Create tokenizer.json
Browse files- tokenizer.json +5 -0
tokenizer.json
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": "<s>",
|
| 3 |
+
"eos_token": "</s>",
|
| 4 |
+
"unk_token": "<unk>"
|
| 5 |
+
}
|