Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Urdu
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use kingabzpro/wav2vec2-60-urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/wav2vec2-60-urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-60-urdu")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kingabzpro/wav2vec2-60-urdu") model = AutoModelForCTC.from_pretrained("kingabzpro/wav2vec2-60-urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kingabzpro/wav2vec2-60-urdu: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/kingabzpro/wav2vec2-60-urdu/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kingabzpro/wav2vec2-60-urdu/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kingabzpro/wav2vec2-60-urdu/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 01d8459ea06c5ba3b803c93379323f209ce4230300036f0ae5600d2b6b211e21
- Size of remote file:
- 378 MB
- SHA256:
- 29eaac4d88af1cc5d998eccae82a8c96402205a7a22bc55f2494521f707a6a29
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