Instructions to use Dev372/HarshDev-whisper-tiny-English_4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dev372/HarshDev-whisper-tiny-English_4000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dev372/HarshDev-whisper-tiny-English_4000")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Dev372/HarshDev-whisper-tiny-English_4000") model = AutoModelForSpeechSeq2Seq.from_pretrained("Dev372/HarshDev-whisper-tiny-English_4000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f055d51cb9c20882656bf530ce2a8b5738e2ec6ef90a0c256154471cb6fe3927
- Size of remote file:
- 151 MB
- SHA256:
- b42e4a3e1f1ab91ddd308f78e43afff0b17c0d13ad57ccfcf3d0c1b78b89462e
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