Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Serbian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sagicc/whisper-large-sr-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/whisper-large-sr-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sagicc/whisper-large-sr-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sagicc/whisper-large-sr-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sagicc/whisper-large-sr-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ed6eb2b4acb2dadc9da3495ff0f65ce99e8f538b906f8510c66df1e0a4f99e2
- Size of remote file:
- 4.41 kB
- SHA256:
- 33400c358e7c6843492d13af22e135037049dce3a29b9000fb253e2ed8b59861
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