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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5eedea4395cf8ce9546354deeab34f12130c6f9258b8f4305b80a6bd8413b59
- Size of remote file:
- 967 MB
- SHA256:
- a8ee371130dac0a2c5f55e6cd89e65f48044ff6f4ac97cfd46f8b6d3c55604c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.