Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Czech
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-cs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-cs")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-cs") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-cs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89a15bd63459a364ed1f91c29b4d9f7229909a7ce8f1d2b6ef48de8462139f0e
- Size of remote file:
- 5.24 kB
- SHA256:
- 9248bf68a17e7e4172688d3dffc8d1be16f7b6c49956b1e3de4edb5757e69ae7
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