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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f73d0d361ca45f62b9a2d8e9678c292152d914df05763eb407f84a0e8edb42d
- Size of remote file:
- 5.24 kB
- SHA256:
- 7213d67e418fe4a6db6a1d50dceaf8d4508134810568120bb9835a3030c5c350
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