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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b150e0632eaac6042e310bbb5f0ddce05c96920bd13e914c207464aa5d3dfecf
- Size of remote file:
- 967 MB
- SHA256:
- 31bb26b83d11c8ca4fa229008588912c7d8583be734331776c9e8bae1185df9d
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