Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use shtif/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shtif/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="shtif/whisper-tiny-en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("shtif/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("shtif/whisper-tiny-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd48bfd6e2ff0aeecd9a21a2a426f4a99e04becd65e60842abf376aa4b3f2828
- Size of remote file:
- 151 MB
- SHA256:
- ed3a2d5742fd27c44454bc0740ef83ff10533f6141462c4cc9c55150e9e1fd7a
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