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README.md
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---
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language:
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- dv
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: Whisper Small Dv - Juan Carlos Pineros HF Class
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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 13
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type: mozilla-foundation/common_voice_13_0
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config: dv
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split: test
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args: dv
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metrics:
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- name: Wer
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type: wer
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value: 11.119031887888166
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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 Small Dv - Juan Carlos Pineros HF Class
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2937
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- Wer Ortho: 56.7101
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- Wer: 11.1190
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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: 16
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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: constant_with_warmup
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- lr_scheduler_warmup_steps: 50
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- training_steps: 4000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
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| 0.1203 | 1.63 | 500 | 0.1687 | 62.7551 | 13.3724 |
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| 0.0464 | 3.26 | 1000 | 0.1757 | 58.8899 | 12.0997 |
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| 0.0327 | 4.89 | 1500 | 0.1931 | 59.0919 | 11.8510 |
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| 0.0118 | 6.51 | 2000 | 0.2349 | 58.2492 | 11.4042 |
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| 0.007 | 8.14 | 2500 | 0.2606 | 57.7408 | 11.5259 |
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| 0.0056 | 9.77 | 3000 | 0.2759 | 57.4413 | 11.0564 |
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| 0.0038 | 11.4 | 3500 | 0.2785 | 57.2185 | 10.9956 |
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| 0.0039 | 13.03 | 4000 | 0.2937 | 56.7101 | 11.1190 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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