Instructions to use ThomasFG/0-0-135 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasFG/0-0-135 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ThomasFG/0-0-135")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ThomasFG/0-0-135") model = AutoModelForSpeechSeq2Seq.from_pretrained("ThomasFG/0-0-135", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d93221da2e36e5d7e2645741d65d6755952d3005aa893742dd23e4c1b1f4eb07
- Size of remote file:
- 4.73 kB
- SHA256:
- a416f82d1dff46a561df18ce0b58d626abb5fd10c367a2b452621a38d162bf3a
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