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update model card README.md
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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: whisper-large-es-cv11-2
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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: es
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split: validation[:1000]
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args: es
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metrics:
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- name: Wer
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type: wer
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value: 3.7010962486171173
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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-es-cv11-2
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) 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: 0.1320
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- Wer: 3.7011
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- Cer: 1.0555
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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-06
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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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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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: 2000
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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 | Cer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
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| 0.1837 | 0.32 | 1000 | 0.1669 | 4.2442 | 1.2488 |
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| 0.1343 | 0.64 | 2000 | 0.1444 | 4.0833 | 1.2084 |
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| 0.1312 | 0.96 | 3000 | 0.1362 | 3.9324 | 1.1933 |
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| 0.1206 | 1.28 | 4000 | 0.1333 | 3.8520 | 1.1748 |
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| 0.1143 | 1.6 | 5000 | 0.1321 | 3.6508 | 1.0572 |
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| 0.1202 | 1.92 | 6000 | 0.1291 | 3.8017 | 1.1311 |
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| 0.0856 | 2.24 | 7000 | 0.1325 | 3.7011 | 1.0841 |
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| 0.1005 | 2.56 | 8000 | 0.1320 | 3.7011 | 1.0555 |
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### Framework versions
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- Transformers 4.26.0.dev0
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- Pytorch 1.13.1+cu117
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- Datasets 2.7.1.dev0
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- Tokenizers 0.13.2
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