Instructions to use RecCode/whisper_tuning_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecCode/whisper_tuning_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RecCode/whisper_tuning_2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RecCode/whisper_tuning_2") model = AutoModelForSpeechSeq2Seq.from_pretrained("RecCode/whisper_tuning_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 656766629f254caf7114206d93fa7438431c4cbcccb12cd5001bca66c22991c3
- Size of remote file:
- 967 MB
- SHA256:
- 76f0eaf7f9c7789ea169e77d72ab3146b06cb168f82f1c6a9b36f7bfc6e6d21f
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