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--- |
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language: |
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- pt |
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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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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Sussurrar |
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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: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: pt |
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split: test |
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args: pt |
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metrics: |
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- name: Wer |
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type: wer |
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value: 26.260504201680675 |
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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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# Sussurrar |
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4367 |
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- Wer: 26.2605 |
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## Model description |
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The model is fine-tuned for ASR in Portuguese. We decided to train in Portuguese because it is a very common language, yet does not have many resources in terms of NLP. |
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## Intended uses & limitations |
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The model is used for Automatic Speach Recognition. It is fine-tuned in the Portuguese language. |
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## Training and evaluation data |
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Trained and evaluated on the Common Voice 11 Portuguese data. |
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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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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| 0.4076 | 0.1 | 200 | 0.5182 | 32.4930 | |
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| 0.3462 | 0.2 | 400 | 0.4912 | 29.0266 | |
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| 0.3283 | 0.3 | 600 | 0.4671 | 27.0308 | |
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| 0.3579 | 0.4 | 800 | 0.4662 | 26.6457 | |
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| 0.2766 | 0.5 | 1000 | 0.4639 | 26.7157 | |
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| 0.2147 | 1.03 | 1200 | 0.4470 | 26.7857 | |
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| 0.1877 | 1.13 | 1400 | 0.4382 | 26.4006 | |
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| 0.192 | 1.23 | 1600 | 0.4430 | 26.3655 | |
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| 0.1894 | 1.33 | 1800 | 0.4349 | 26.4006 | |
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| 0.1725 | 1.43 | 2000 | 0.4367 | 26.2605 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.1.dev0 |
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- Tokenizers 0.13.2 |
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