Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Serbian
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Sagicc/whisper-base-sr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagicc/whisper-base-sr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sagicc/whisper-base-sr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Sagicc/whisper-base-sr") model = AutoModelForSpeechSeq2Seq.from_pretrained("Sagicc/whisper-base-sr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64deab26d752bd2b90a4c8a40e1f32d7f6e366c3875570a59b5303b225e672ad
- Size of remote file:
- 4.41 kB
- SHA256:
- 2e454dcfd58e9b714413a8ebdf8e1dbd974b51af6f0cbcd9556e1f90007c1999
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