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README.md
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---
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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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- 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: openai/whisper-small
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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: common_voice_11_0
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config: ca
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split: test
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args: ca
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metrics:
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- name: Wer
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type: wer
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value: 8.569471791798646
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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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# openai/whisper-small
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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_11_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1980
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- Wer: 8.5695
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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: 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: 20000
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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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| 0.2128 | 0.1 | 2000 | 0.2644 | 13.0303 |
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| 0.1361 | 1.1 | 4000 | 0.2300 | 10.9568 |
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| 0.0658 | 2.1 | 6000 | 0.2376 | 11.2810 |
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| 0.102 | 3.09 | 8000 | 0.2156 | 9.8730 |
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| 0.0706 | 4.09 | 10000 | 0.2126 | 9.6179 |
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| 0.0428 | 5.09 | 12000 | 0.2178 | 9.3405 |
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| 0.0503 | 6.09 | 14000 | 0.2109 | 9.1356 |
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| 0.0778 | 7.08 | 16000 | 0.2058 | 9.2001 |
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| 0.0082 | 8.08 | 18000 | 0.2173 | 8.9941 |
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| 0.0994 | 9.08 | 20000 | 0.1980 | 8.5695 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.10.0+cu102
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- Datasets 2.7.1
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- Tokenizers 0.13.2
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