Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Czech
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use mikr/whisper-large2-czech-cv11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikr/whisper-large2-czech-cv11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mikr/whisper-large2-czech-cv11")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mikr/whisper-large2-czech-cv11") model = AutoModelForMultimodalLM.from_pretrained("mikr/whisper-large2-czech-cv11") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 267ab1f118bc050f2affef379016e394397d9b0372b0e6ae0e5606fc53c0fd16
- Size of remote file:
- 3.52 kB
- SHA256:
- 30fad0ba01d92a7cd92d77de4fb5b37a5ef579abf697d6cd4358f84c5309db3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.