Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Marathi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use simran14/mr-val-g3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simran14/mr-val-g3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="simran14/mr-val-g3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("simran14/mr-val-g3") model = AutoModelForSpeechSeq2Seq.from_pretrained("simran14/mr-val-g3") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [simran14/mr-val-f](https://huggingface.co/simran14/mr-val-f) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0009
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- Wer: 0.
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## Model description
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metrics:
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- name: Wer
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type: wer
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value: 0.090463
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [simran14/mr-val-f](https://huggingface.co/simran14/mr-val-f) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0009
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- Wer: 0.090463
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## Model description
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