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--- |
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base_model: openai/whisper-medium |
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datasets: |
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- google/fleurs |
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language: |
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- hi |
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license: apache-2.0 |
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metrics: |
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- wer |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Whisper Medium hindi -megha sharma |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Google Fleurs |
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type: google/fleurs |
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config: hi_in |
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split: None |
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args: 'config: hi, split: test' |
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metrics: |
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- type: wer |
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value: 18.176493557204218 |
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name: Wer |
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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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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Medium hindi -megha sharma |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Google Fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3120 |
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- Wer: 18.1765 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 250 |
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- training_steps: 5000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:----:|:---------------:|:-------:| |
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| 0.2166 | 0.8475 | 250 | 0.2327 | 26.1128 | |
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| 0.1217 | 1.6949 | 500 | 0.1955 | 21.5053 | |
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| 0.0578 | 2.5424 | 750 | 0.2025 | 20.7536 | |
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| 0.0271 | 3.3898 | 1000 | 0.2230 | 20.5096 | |
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| 0.0134 | 4.2373 | 1250 | 0.2463 | 20.3046 | |
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| 0.0105 | 5.0847 | 1500 | 0.2463 | 19.7970 | |
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| 0.0064 | 5.9322 | 1750 | 0.2636 | 19.2796 | |
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| 0.0048 | 6.7797 | 2000 | 0.2678 | 19.5920 | |
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| 0.0034 | 7.6271 | 2250 | 0.2765 | 19.2991 | |
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| 0.0021 | 8.4746 | 2500 | 0.2710 | 18.5084 | |
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| 0.0006 | 9.3220 | 2750 | 0.2879 | 19.2015 | |
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| 0.0001 | 10.1695 | 3000 | 0.2895 | 18.4303 | |
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| 0.0003 | 11.0169 | 3250 | 0.2930 | 18.3815 | |
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| 0.0005 | 11.8644 | 3500 | 0.3032 | 18.5963 | |
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| 0.0001 | 12.7119 | 3750 | 0.3003 | 18.4889 | |
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| 0.0001 | 13.5593 | 4000 | 0.3054 | 18.4010 | |
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| 0.0001 | 14.4068 | 4250 | 0.3085 | 18.2058 | |
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| 0.0 | 15.2542 | 4500 | 0.3104 | 18.1472 | |
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| 0.0 | 16.1017 | 4750 | 0.3116 | 18.1863 | |
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| 0.0 | 16.9492 | 5000 | 0.3120 | 18.1765 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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