Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Slovenian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use mikr/whisper-medium-sl-cv11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikr/whisper-medium-sl-cv11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mikr/whisper-medium-sl-cv11")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mikr/whisper-medium-sl-cv11") model = AutoModelForSpeechSeq2Seq.from_pretrained("mikr/whisper-medium-sl-cv11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1996cf728a0852c19cf6872bc01ad445b351e4dd3ff193481b1593fe8295e75a
- Size of remote file:
- 1.53 GB
- SHA256:
- 08cc2ec011c811ff7d043246d20df358e7a2ca734ce96c3bca905e4a7bf8844b
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