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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4313078f11dfe0175211f77b3584b2b29d57255a031beeee1eb1f8b81a5cab0e
- Size of remote file:
- 4.92 kB
- SHA256:
- 54ccd018f6a3789c121b286c9e3c9251538edf6520372fc6dd3085b863cc01d8
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