Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Urdu
wav2vec2
robust-speech-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use kingabzpro/wav2vec2-urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/wav2vec2-urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-urdu")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kingabzpro/wav2vec2-urdu") model = AutoModelForCTC.from_pretrained("kingabzpro/wav2vec2-urdu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e8a75b9da9f974b3930e148853f42f88e3eb4e2b1742e5c9a92b3067ab7ebc7
- Size of remote file:
- 3.06 kB
- SHA256:
- 429d20773f1936554d7099492557f1952ffdc11915717f43c5634b2b25d3b82b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.