Instructions to use EducativeCS2023/whisper-en-tiny-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EducativeCS2023/whisper-en-tiny-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EducativeCS2023/whisper-en-tiny-trained")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EducativeCS2023/whisper-en-tiny-trained") model = AutoModelForSpeechSeq2Seq.from_pretrained("EducativeCS2023/whisper-en-tiny-trained") - 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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version https://git-lfs.github.com/spec/v1
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oid sha256:216084642f9ab56754cd8e5b1b8f04983ab711c6111922490377a694d0deeab2
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size 151061672
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