Instructions to use RecCode/whisper_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecCode/whisper_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RecCode/whisper_final")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RecCode/whisper_final") model = AutoModelForSpeechSeq2Seq.from_pretrained("RecCode/whisper_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 161438153432befbcc4bb6c9fea27ad6b289c64147a30b150d7be6840a713066
- Size of remote file:
- 4.86 kB
- SHA256:
- 8a39717c91bba69ba73400de27801658def45f7179577a65a1b352bcb6d2137b
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