Instructions to use ThomasFG/10-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/10-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/10-10")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/10-10") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/10-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88f5635bfa455136767d1a53fa3829639136e68c08ff37cb9b4326d11dcfeea4
- Size of remote file:
- 4.73 kB
- SHA256:
- b3236c1c12f349e66782b3f12119ca820e97c49d0910bf15eede260771f22e6d
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