Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Swedish
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use SebLih/whisper-SV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SebLih/whisper-SV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SebLih/whisper-SV")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SebLih/whisper-SV") model = AutoModelForSpeechSeq2Seq.from_pretrained("SebLih/whisper-SV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SebLih/whisper-SV: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/SebLih/whisper-SV/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SebLih/whisper-SV/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SebLih/whisper-SV/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- db75476c9800ef1ae8cc0cbbef37297b33ff70f1f179ce040908870cee24821c
- Size of remote file:
- 967 MB
- SHA256:
- b9050d12fd58c2573ef6b226b6146ee7751521be2f161f8e9844756140c43bfb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.