Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use igoeldc/whisper-tiny-en-US with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igoeldc/whisper-tiny-en-US with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="igoeldc/whisper-tiny-en-US")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("igoeldc/whisper-tiny-en-US") model = AutoModelForSpeechSeq2Seq.from_pretrained("igoeldc/whisper-tiny-en-US", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0eb456c6d8f59b55642d070b5dd7a4cccd8c8cd857bc80be8ec7e2f120bd34c7
- Size of remote file:
- 151 MB
- SHA256:
- f3159d125f9c78fe0e695b9fa0303458fd9bb4dc19abe97f6ecfff69fde73f48
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