Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use cvnberk/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cvnberk/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cvnberk/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("cvnberk/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5df1781aea3220f0cd30df0709798a23a878160d21e695d0f607c9c034da89ec
- Size of remote file:
- 4.09 kB
- SHA256:
- 8effc66bcb109023beaea42c420666b7a02c1ed0591a4415aeed7254fcfc1c36
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