Instructions to use RecCode/Project-Whisper_Fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecCode/Project-Whisper_Fine_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RecCode/Project-Whisper_Fine_tuning")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RecCode/Project-Whisper_Fine_tuning") model = AutoModelForSpeechSeq2Seq.from_pretrained("RecCode/Project-Whisper_Fine_tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b376275c412d0a781579a2d521ea0efec35da7b5dfa78a2da4244bda1797711
- Size of remote file:
- 217 MB
- SHA256:
- 5cfec4dab2ad328c0ed01d70ceb1feff6b6da6fd1ee152b6f822ef82de10626b
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