Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Kinyarwanda
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Kleber/output_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kleber/output_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kleber/output_dir")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kleber/output_dir") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kleber/output_dir") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bad8df05812a26b9554fdf611d679224e78a189f289b02a122fed807d576cb4e
- Size of remote file:
- 967 MB
- SHA256:
- b06f8b9709dff65b02d496ec1744aa1ebd37262666f9e608136ea04c8f60885f
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