Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Tatar
whisper
whisper-event
Eval Results (legacy)
Instructions to use 501Good/whisper-tiny-tt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 501Good/whisper-tiny-tt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="501Good/whisper-tiny-tt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("501Good/whisper-tiny-tt") model = AutoModelForSpeechSeq2Seq.from_pretrained("501Good/whisper-tiny-tt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c1baaa6db0a9ad11580b08900fdb473c79666620497a0c93b8cb14d62811683
- Size of remote file:
- 151 MB
- SHA256:
- 81e54952c912e5589612cf83dd2b6291cbc85c04e1d4ebff9c584898217072bc
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