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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 Kannada - 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 kn_in |
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type: google/fleurs |
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config: kn_in |
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split: test |
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args: kn_in |
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metrics: |
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- name: Wer |
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type: wer |
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value: 30.612702366127024 |
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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 Kannada - 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 kn_in dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2258 |
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- Wer: 30.6127 |
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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: 1000 |
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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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| 0.7196 | 1.03 | 100 | 0.5166 | 55.2130 | |
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| 0.2769 | 2.06 | 200 | 0.2532 | 36.1594 | |
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| 0.1896 | 4.02 | 300 | 0.2167 | 32.7298 | |
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| 0.1384 | 5.04 | 400 | 0.2037 | 31.8356 | |
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| 0.1099 | 7.0 | 500 | 0.2030 | 31.0560 | |
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| 0.0707 | 8.03 | 600 | 0.2153 | 31.2453 | |
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| 0.052 | 9.06 | 700 | 0.2258 | 30.6127 | |
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| 0.0375 | 11.02 | 800 | 0.2413 | 31.2204 | |
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| 0.0256 | 12.05 | 900 | 0.2507 | 31.0635 | |
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| 0.0245 | 14.01 | 1000 | 0.2549 | 31.1059 | |
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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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