Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
trnslation
Generated from Trainer
Instructions to use Subhaka/whisper-small-Sinhala-Fine_Tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subhaka/whisper-small-Sinhala-Fine_Tune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Subhaka/whisper-small-Sinhala-Fine_Tune")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Subhaka/whisper-small-Sinhala-Fine_Tune") model = AutoModelForSpeechSeq2Seq.from_pretrained("Subhaka/whisper-small-Sinhala-Fine_Tune") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fa93ac3089298b4c0eafee80a704fdec899e5ed2559634c54499993c8e9f51a
- Size of remote file:
- 967 MB
- SHA256:
- 709479647e90507527dbf5a60952a8c16c8586a21fdc12c759e8801bc8d74e64
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