Instructions to use dmmagdal/whisper-medium-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmmagdal/whisper-medium-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmmagdal/whisper-medium-onnx")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmmagdal/whisper-medium-onnx") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmmagdal/whisper-medium-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f597baf6a62a8333d91aba2e76d6f2eeb71a2a337cc91d9380a8a1e9ccd263f9
- Size of remote file:
- 2.04 GB
- SHA256:
- 6ffe0ecc91d3c04c6d9c3933f7598171815e16b70b82825bbfa6a64121d1c606
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