Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use sjdata/whisper-tiny-minds14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjdata/whisper-tiny-minds14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sjdata/whisper-tiny-minds14")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sjdata/whisper-tiny-minds14") model = AutoModelForSpeechSeq2Seq.from_pretrained("sjdata/whisper-tiny-minds14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90ae4ca5fed5a144d476879efeb1c779d399b4d5d1b946b1df5de8b5e73fef71
- Size of remote file:
- 151 MB
- SHA256:
- 67a5f9b275857e733caed4441666a3ec79d66a75347ae3ae44ec134a8e98d076
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