Instructions to use garnagar/whisper-tiny-libirClean-vs-commonNative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garnagar/whisper-tiny-libirClean-vs-commonNative with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="garnagar/whisper-tiny-libirClean-vs-commonNative")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("garnagar/whisper-tiny-libirClean-vs-commonNative") model = AutoModelForSpeechSeq2Seq.from_pretrained("garnagar/whisper-tiny-libirClean-vs-commonNative") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b785328e9e13ce45b62d6ad022f6418325146e516642a97c44d05043f7a7dbc
- Size of remote file:
- 3.57 kB
- SHA256:
- 5e47a9199fd291faf8d005ab0f897cfdc16125f76fae4e23eefba878930c4db6
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