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:
- 296653374821cc8a6d9a024edbbc167eee3a24bb27bf835649954ade7db304b1
- Size of remote file:
- 967 MB
- SHA256:
- 9ec0baf86c87008ca4bc667ed23998f44ac0e91ab5ff5d853960de00b9e6204b
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