Automatic Speech Recognition
Transformers
PyTorch
Serbian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sagicc/whisper-medium-sr-fleurs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/whisper-medium-sr-fleurs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sagicc/whisper-medium-sr-fleurs")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sagicc/whisper-medium-sr-fleurs") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sagicc/whisper-medium-sr-fleurs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8e939ec776ea54124393dd9c7b5ceb49cef2bcc7fbd7ac3ffb963084b840757
- Size of remote file:
- 3.06 GB
- SHA256:
- 42f71e11b5bdbd71c5f8822dd34dca211947e5e82de82c801e196b2b011a82b2
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