Instructions to use Talha/URDU-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Talha/URDU-ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Talha/URDU-ASR")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Talha/URDU-ASR") model = AutoModelForCTC.from_pretrained("Talha/URDU-ASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.62e7e0e
-
Update special_tokens_map.json
2afe3a8 -
Update added_tokens.json
1b89feb -
Update tokenizer_config.json
d9993dc -
Update alphabet.json
cc38734 -
URDU-ASR-LM
e346839 -
Update README.md
3d41d8d -
add tokenizer
b104e87 -
update model card README.md
ad70e2a -
End of training
917b370
-
Training in progress, step 500
c1e0d45 -
initial commit
8cc3cf2