Instructions to use dball/whisper-medium-de-med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dball/whisper-medium-de-med with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dball/whisper-medium-de-med")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dball/whisper-medium-de-med") model = AutoModelForSpeechSeq2Seq.from_pretrained("dball/whisper-medium-de-med") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11f447f4f3765c4a12266522b7ce1ec5586a17da9bb710e48fc6e5375d7bac0a
- Size of remote file:
- 3.06 GB
- SHA256:
- 33fdf716da11618c135197aac7d086963a2f6ed7336b9b66dcccfaac5676d005
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