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README.md
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---
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license: apache-2.0
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base_model: facebook/wav2vec2-xls-r-300m
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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: Check_Model_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
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type: common_voice
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config: id
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split: test
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args: id
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metrics:
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- name: Wer
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type: wer
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value: 0.2728883087823979
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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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# Check_Model_2
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3499
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- Wer: 0.2729
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- Cer: 0.0673
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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: 0.0003
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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: 500
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- num_epochs: 30
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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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| 3.8708 | 3.23 | 400 | 0.7345 | 0.7259 | 0.2034 |
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| 0.4247 | 6.45 | 800 | 0.4128 | 0.4268 | 0.1102 |
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| 0.2047 | 9.68 | 1200 | 0.3726 | 0.3795 | 0.0930 |
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| 0.1422 | 12.9 | 1600 | 0.3690 | 0.3514 | 0.0884 |
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| 0.1139 | 16.13 | 2000 | 0.3811 | 0.3160 | 0.0794 |
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| 0.089 | 19.35 | 2400 | 0.3650 | 0.2895 | 0.0731 |
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| 0.0709 | 22.58 | 2800 | 0.3629 | 0.2944 | 0.0727 |
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| 0.0594 | 25.81 | 3200 | 0.3538 | 0.2779 | 0.0692 |
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| 0.0478 | 29.03 | 3600 | 0.3499 | 0.2729 | 0.0673 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu117
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- Datasets 1.18.3
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- Tokenizers 0.13.3
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