Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use marma/whisper-tiny-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marma/whisper-tiny-sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="marma/whisper-tiny-sv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("marma/whisper-tiny-sv") model = AutoModelForSpeechSeq2Seq.from_pretrained("marma/whisper-tiny-sv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c498ff6350c9fdab376aec93135094d94fffc7e13a53f600df85530bad47375a
- Size of remote file:
- 3.63 kB
- SHA256:
- 80d18f0ed77ef225e95473ba705c7eefc8e4a35c34200a4036ffb4934e2e1d6f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.