Instructions to use RecCode/4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecCode/4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RecCode/4")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RecCode/4") model = AutoModelForSpeechSeq2Seq.from_pretrained("RecCode/4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae367ecc9a60fdd0a5f00aa8d744cca6353c49872956712d253ff1f2aae21e8d
- Size of remote file:
- 5.05 kB
- SHA256:
- d598ea4f651ab11ebf1f4b3f747f7e0d79408fab6e3df7695203eea4af93a48f
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