Instructions to use Kleber/output_dir_run_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kleber/output_dir_run_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kleber/output_dir_run_2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kleber/output_dir_run_2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kleber/output_dir_run_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f50c95f3ba81d51f7ca102697a8e810bf836107472a60eae73c70caf126bb58f
- Size of remote file:
- 3.57 kB
- SHA256:
- 8ba9a265d79015c82b2b8f6edd14e270d82f0b0b389192e94b039b9064d71380
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