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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: whisper-tiny-te
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results:
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license: apache-2.0
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tags:
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- generated_from_trainer
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base_model: openai/whisper-tiny
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model-index:
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- name: whisper-tiny-te
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results:
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