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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
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metrics:
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- wer
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model-index:
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- name: wav2vec2-common_voice-tr-demo
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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
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type: common_voice
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config: tr
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split: test
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args: tr
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metrics:
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- name: Wer
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type: wer
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value: 0.49443366356858337
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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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# wav2vec2-common_voice-tr-demo
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5314
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- Wer: 0.4944
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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: 0.0002
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- train_batch_size: 32
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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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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- num_epochs: 20.0
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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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| No log | 1.83 | 100 | 4.1084 | 1.0 |
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| No log | 3.67 | 200 | 3.1519 | 1.0 |
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| No log | 5.5 | 300 | 1.9348 | 0.9799 |
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| No log | 7.34 | 400 | 0.7185 | 0.7490 |
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| 3.6165 | 9.17 | 500 | 0.6041 | 0.6368 |
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| 3.6165 | 11.01 | 600 | 0.5610 | 0.5771 |
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| 3.6165 | 12.84 | 700 | 0.5292 | 0.5398 |
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| 3.6165 | 14.68 | 800 | 0.5242 | 0.5083 |
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| 3.6165 | 16.51 | 900 | 0.5443 | 0.5037 |
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| 0.1894 | 18.35 | 1000 | 0.5314 | 0.4944 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1
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- Datasets 2.13.1
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- Tokenizers 0.13.2
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