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:
- 9c0a9a3dd29ed6618531d61697d9ffd36650ba538846d844659b5e17c895d93e
- Size of remote file:
- 967 MB
- SHA256:
- 33a1506cc9aab67374ed0c61d36c38d7457cb50b538e55509f82a13d82a2350d
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