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--- |
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library_name: transformers |
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language: |
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- qve |
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license: apache-2.0 |
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base_model: openai/whisper-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cportoca/Quechua_dataset |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large Ja-Qve - cportoca |
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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: Quechua_dataset |
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type: cportoca/Quechua_dataset |
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args: 'config: Qve, split: train/test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 14.02572952620019 |
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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 Large Ja-Qve - cportoca |
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Quechua_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1918 |
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- Wer: 14.0257 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 8000 |
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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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| 0.2039 | 1.3550 | 1000 | 0.2614 | 25.1961 | |
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| 0.0757 | 2.7100 | 2000 | 0.1891 | 33.3856 | |
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| 0.0211 | 4.0650 | 3000 | 0.1846 | 16.0966 | |
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| 0.0112 | 5.4201 | 4000 | 0.1876 | 15.8770 | |
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| 0.0027 | 6.7751 | 5000 | 0.1875 | 14.1826 | |
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| 0.0006 | 8.1301 | 6000 | 0.1888 | 14.2140 | |
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| 0.0002 | 9.4851 | 7000 | 0.1897 | 13.9316 | |
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| 0.0001 | 10.8401 | 8000 | 0.1918 | 14.0257 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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