Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
German
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-de")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-de") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 347d6dc75e7edbc97bda1a9f337326f22972328425cf0006e3bc23c1ea0f1647
- Size of remote file:
- 5.24 kB
- SHA256:
- 412c5f9527e5708d63748be5e51a9595a39d1ee2349055820b81a467abcebbf3
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