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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: 18.02030456852792 |
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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: 0.4333 |
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- Wer: 18.0203 |
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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: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 1000 |
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- training_steps: 25000 |
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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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| 0.0669 | 3.3898 | 1000 | 0.2086 | 20.9684 | |
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| 0.0115 | 6.7797 | 2000 | 0.2637 | 19.7579 | |
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| 0.0034 | 10.1695 | 3000 | 0.3012 | 19.6408 | |
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| 0.0026 | 13.5593 | 4000 | 0.3179 | 19.2893 | |
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| 0.0014 | 16.9492 | 5000 | 0.3242 | 18.7817 | |
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| 0.0024 | 20.3390 | 6000 | 0.3348 | 19.1624 | |
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| 0.0024 | 23.7288 | 7000 | 0.3421 | 19.7774 | |
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| 0.0006 | 27.1186 | 8000 | 0.3511 | 18.6939 | |
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| 0.0008 | 30.5085 | 9000 | 0.3632 | 18.8989 | |
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| 0.0007 | 33.8983 | 10000 | 0.3600 | 18.7622 | |
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| 0.0006 | 37.2881 | 11000 | 0.3470 | 18.4791 | |
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| 0.0002 | 40.6780 | 12000 | 0.3548 | 18.2936 | |
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| 0.0001 | 44.0678 | 13000 | 0.3711 | 18.0594 | |
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| 0.0006 | 47.4576 | 14000 | 0.3733 | 18.2839 | |
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| 0.0003 | 50.8475 | 15000 | 0.3766 | 18.1667 | |
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| 0.0 | 54.2373 | 16000 | 0.3745 | 18.0203 | |
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| 0.0 | 57.6271 | 17000 | 0.3914 | 17.8739 | |
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| 0.0 | 61.0169 | 18000 | 0.4003 | 17.9032 | |
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| 0.0 | 64.4068 | 19000 | 0.4081 | 17.8641 | |
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| 0.0 | 67.7966 | 20000 | 0.4153 | 17.8544 | |
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| 0.0 | 71.1864 | 21000 | 0.4219 | 17.8544 | |
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| 0.0 | 74.5763 | 22000 | 0.4281 | 18.0105 | |
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| 0.0 | 77.9661 | 23000 | 0.4333 | 18.0203 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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