Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use parambharat/whisper-tiny-te with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use parambharat/whisper-tiny-te with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="parambharat/whisper-tiny-te")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("parambharat/whisper-tiny-te") model = AutoModelForSpeechSeq2Seq.from_pretrained("parambharat/whisper-tiny-te") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5358860f295142f1c5c0d8fc10d06a6324693867db312e6a3b3ca64a56243be3
- Size of remote file:
- 3.64 kB
- SHA256:
- e2685a82a6026b1eb14825241281aa45688d34c5ffabf1c26fed2349c57ba002
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