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:
- 2ba6e8a1622970b3ff300a1507a3519e51aa1cfe8b0bb1de5836098d31a5a2c1
- Size of remote file:
- 151 MB
- SHA256:
- 56d1603336dad361385fb0f5f8dd396ed06283acaa0ad72f18ec0db81729f6eb
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