Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Urdu
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use stackintel/whisper-small-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stackintel/whisper-small-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stackintel/whisper-small-ur")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stackintel/whisper-small-ur") model = AutoModelForSpeechSeq2Seq.from_pretrained("stackintel/whisper-small-ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stackintel/whisper-small-ur: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/stackintel/whisper-small-ur/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stackintel/whisper-small-ur/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stackintel/whisper-small-ur/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- e062ad172216abe283a82bcd2a3c3a4501a6a1bd3999823f32f0682dbead6c74
- Size of remote file:
- 967 MB
- SHA256:
- f0fc1b0188915501fc1066b2932bcedbe557ab656231371b7ea5278a28d488d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.