Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
German
whisper
whisper-event
Eval Results (legacy)
Instructions to use bofenghuang/whisper-large-v2-cv11-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bofenghuang/whisper-large-v2-cv11-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-cv11-german")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-cv11-german") model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-large-v2-cv11-german", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2fe991a19ed4b162c179785bac4c8d2539cc2675d918d820ced7bafb48a59d0
- Size of remote file:
- 3.09 GB
- SHA256:
- e10d90936368fbc11d8e23cbed4ed795129ec728f911b2579e27f4af58c51c85
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