Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Marco-Cheung/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marco-Cheung/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Marco-Cheung/whisper-tiny-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Marco-Cheung/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("Marco-Cheung/whisper-tiny-en") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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oid sha256:2ec649bc6802fc398616d40d7917b0e2b6f41a3a4d1663c5f17b462a21e8027c
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size 151061728
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