Instructions to use readerbench/whisper-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/whisper-ro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="readerbench/whisper-ro")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("readerbench/whisper-ro") model = AutoModelForSpeechSeq2Seq.from_pretrained("readerbench/whisper-ro") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:947bf6924441a6f0be211f9e2dad79625160e54c6a57cd4a84a591b8c830c5a1
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size 966995080
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