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:
- 7fd8a4e6c3ecd951cb1740fa8440358805359189cfcedd58b43969223ffb03fb
- Size of remote file:
- 967 MB
- SHA256:
- 01106fbba787efdd3e1741f0107e6ad60b006e00a16e0e91853c0924ad647cd6
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