Instructions to use RafatK/second2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RafatK/second2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RafatK/second2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RafatK/second2") model = AutoModelForSpeechSeq2Seq.from_pretrained("RafatK/second2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d31cf76a6b3ff9c3dd1093e2df383a15729b531821dc343b17abf3b8b34a3984
- Size of remote file:
- 5.84 kB
- SHA256:
- b81d14287daae9f77ad6062bd20a30f1cec7ab6a7e54937d3a802dc14253cfc2
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