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--- |
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license: apache-2.0 |
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base_model: openai/whisper-base |
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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: Breeze DSW Telugu - base |
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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 te_in |
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type: google/fleurs |
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config: te_in |
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split: test |
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args: te_in |
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metrics: |
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- name: Wer |
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type: wer |
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value: 37.45436058603319 |
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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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# Breeze DSW Telugu - base |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs te_in dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3372 |
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- Wer: 37.4544 |
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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: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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: 2000 |
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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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| 0.2937 | 2.03 | 200 | 0.3237 | 42.5614 | |
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| 0.1611 | 5.02 | 400 | 0.2756 | 38.9148 | |
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| 0.0889 | 8.01 | 600 | 0.2930 | 38.1106 | |
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| 0.0456 | 11.0 | 800 | 0.3372 | 37.4544 | |
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| 0.0229 | 13.03 | 1000 | 0.3982 | 37.9258 | |
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| 0.0103 | 16.02 | 1200 | 0.4473 | 38.2678 | |
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| 0.0042 | 19.02 | 1400 | 0.4836 | 37.8980 | |
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| 0.0025 | 22.01 | 1600 | 0.5083 | 37.7317 | |
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| 0.002 | 24.04 | 1800 | 0.5220 | 37.8010 | |
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| 0.0018 | 27.03 | 2000 | 0.5269 | 37.9027 | |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.2.dev0 |
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- Tokenizers 0.15.0 |
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