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README.md
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language:
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- pl
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library_name: peft
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tags:
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- generated_from_trainer
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model-index:
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- name: Whisper Base Polish PEFT - s22678 prod
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results:
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- Tokenizers 0.15.1
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---
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language:
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- pl
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: openai/whisper-base
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datasets:
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- mozilla-foundation/common_voice_17_0
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model-index:
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- name: Whisper Base Polish PEFT - s22678 prod
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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: Common Voice 17.0
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type: mozilla-foundation/common_voice_17_0
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split: test
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metrics:
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- type: wer
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value: 42.070773263433814
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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 Base Polish PEFT - s22678 prod
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This model is a fine-tuned version of [openai/openai/whisper-base](https://huggingface.co/openai/openai/whisper-base) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5546
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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: 0.001
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- train_batch_size: 52
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- eval_batch_size: 64
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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: 50
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- num_epochs: 10
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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 |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.3265 | 1.0 | 400 | 0.4049 |
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| 0.2506 | 2.01 | 800 | 0.3920 |
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| 0.185 | 3.01 | 1200 | 0.3868 |
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| 0.1506 | 4.01 | 1600 | 0.3859 |
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| 0.1217 | 5.01 | 2000 | 0.3856 |
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| 0.0931 | 6.02 | 2400 | 0.3922 |
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| 0.0698 | 7.02 | 2800 | 0.3999 |
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| 0.0549 | 8.02 | 3200 | 0.4077 |
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| 0.0477 | 9.02 | 3600 | 0.4121 |
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### Framework versions
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- PEFT 0.11.2.dev0
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- Transformers 4.36.0
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- Pytorch 2.1.0
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- Datasets 2.15.0
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- Tokenizers 0.15.1
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