Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Serbian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sagicc/whisper-medium-sr-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/whisper-medium-sr-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sagicc/whisper-medium-sr-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sagicc/whisper-medium-sr-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sagicc/whisper-medium-sr-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 827e701c12b2bb105ab6259e9b2a29dfb4e5f1a5c5e6cbc4fabb4ca8158d74cb
- Size of remote file:
- 4.28 kB
- SHA256:
- 6384f696e4c007ff84d9dd0cff58aa1d4f4eaeebfc9eeb42d7be393fe62e93b6
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