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:
- 19ae4cae02209fc1433f3cdab607b7f94f5483d1c04d71d3f2a00a3577635e7e
- Size of remote file:
- 967 MB
- SHA256:
- 7ea382ac9cc9dffd657f81db9f1b645881fa3cde909cbfb8d6f994bdfcc256df
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