Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Kibalama/whisper-tiny-en-US with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/whisper-tiny-en-US with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kibalama/whisper-tiny-en-US")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kibalama/whisper-tiny-en-US") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kibalama/whisper-tiny-en-US") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 300edcb00e5617512799fd7e6863b073e7a9099c29c30657272df7d8bd46d741
- Size of remote file:
- 5.5 kB
- SHA256:
- 32c0a4c0dd790b17e6c1172e9261ac463d7db157640ca2a96a62c10a0cdf4b94
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