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--- |
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language: |
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- te |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-small-telugu |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: 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: te_in |
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split: test |
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metrics: |
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- name: Wer |
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type: wer |
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value: 39.67740444608772 |
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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-small-telugu |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset. |
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It achieves the following results on the evaluation set (google/flerus telugu test set): |
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- Loss: 0.3622 |
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- Wer: 39.6774 |
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[openai/whisper-small](https://huggingface.co/openai/whisper-small) has the following zero shot performance on google/fleurs test set: |
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- Wer: 117.91 |
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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: 8 |
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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: 500 |
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- training_steps: 10000 |
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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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| 0.2623 | 1.55 | 500 | 0.2733 | 65.9750 | |
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| 0.0859 | 3.1 | 1000 | 0.2045 | 39.7652 | |
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| 0.0538 | 4.64 | 1500 | 0.2220 | 42.3811 | |
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| 0.0265 | 6.19 | 2000 | 0.2526 | 42.3626 | |
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| 0.0179 | 7.74 | 2500 | 0.2754 | 42.1685 | |
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| 0.008 | 9.29 | 3000 | 0.2966 | 41.2257 | |
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| 0.0061 | 10.83 | 3500 | 0.2950 | 40.6202 | |
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| 0.0034 | 12.38 | 4000 | 0.3049 | 40.3198 | |
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| 0.004 | 13.93 | 4500 | 0.3106 | 40.5879 | |
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| 0.0018 | 15.48 | 5000 | 0.3199 | 40.1812 | |
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| 0.0016 | 17.03 | 5500 | 0.3346 | 39.8345 | |
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| 0.0006 | 18.57 | 6000 | 0.3337 | 40.2274 | |
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| 0.0003 | 20.12 | 6500 | 0.3396 | 40.2597 | |
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| 0.0005 | 21.67 | 7000 | 0.3465 | 40.1072 | |
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| 0.0002 | 23.22 | 7500 | 0.3485 | 39.7282 | |
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| 0.0002 | 24.77 | 8000 | 0.3519 | 39.7837 | |
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| 0.0001 | 26.32 | 8500 | 0.3567 | 39.7560 | |
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| 0.0001 | 27.86 | 9000 | 0.3614 | 39.8068 | |
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| 0.0 | 29.41 | 9500 | 0.3609 | 39.4925 | |
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| 0.0 | 30.96 | 10000 | 0.3622 | 39.6774 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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