Instructions to use PrajwalS/wav2vec2_train_large_on_untrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PrajwalS/wav2vec2_train_large_on_untrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="PrajwalS/wav2vec2_train_large_on_untrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("PrajwalS/wav2vec2_train_large_on_untrained") model = AutoModelForCTC.from_pretrained("PrajwalS/wav2vec2_train_large_on_untrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Training in progress, step 31500
6bf4b1c -
Training in progress, step 28500
25b8a67 -
Training in progress, step 25500
042cf10 -
Training in progress, step 23000
ee42722 -
Training in progress, step 20000
92cdb1b -
Training in progress, step 17500
f30bd8b
-
Training in progress, step 13000
a901c42 -
Training in progress, step 9500
02e6e87 -
Training in progress, step 5500
3393eb6 -
Training in progress, step 3000
b8e649d -
Training in progress, step 500
0619bd9 -
add tokenizer
cfc4ce7 -
initial commit
d4a92da