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update model card README.md

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@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 0.689873417721519
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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 audiofolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1224
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- - Wer: 0.6899
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  ## Model description
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@@ -61,7 +61,7 @@ The following hyperparameters were used during training:
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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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@@ -72,6 +72,18 @@ The following hyperparameters were used during training:
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  | 1.7606 | 15.58 | 600 | 1.1942 | 0.8532 |
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  | 1.0549 | 20.78 | 800 | 1.1132 | 0.7788 |
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  | 0.7553 | 25.97 | 1000 | 1.1224 | 0.6899 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 0.6465189873417722
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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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  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7535
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+ - Wer: 0.6465
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  ## Model description
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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: 90
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  ### Training results
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  | 1.7606 | 15.58 | 600 | 1.1942 | 0.8532 |
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  | 1.0549 | 20.78 | 800 | 1.1132 | 0.7788 |
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  | 0.7553 | 25.97 | 1000 | 1.1224 | 0.6899 |
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+ | 0.6639 | 31.51 | 1200 | 1.2641 | 0.7082 |
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+ | 0.5344 | 36.7 | 1400 | 1.3247 | 0.6835 |
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+ | 0.4527 | 41.9 | 1600 | 1.3915 | 0.7022 |
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+ | 0.3839 | 47.09 | 1800 | 1.4051 | 0.6791 |
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+ | 0.3065 | 52.29 | 2000 | 1.3899 | 0.6706 |
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+ | 0.2714 | 57.48 | 2200 | 1.5455 | 0.6573 |
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+ | 0.2437 | 62.68 | 2400 | 1.6798 | 0.6601 |
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+ | 0.2103 | 67.87 | 2600 | 1.7406 | 0.6674 |
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+ | 0.1899 | 73.06 | 2800 | 1.7625 | 0.6522 |
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+ | 0.1841 | 78.26 | 3000 | 1.7443 | 0.6535 |
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+ | 0.1544 | 83.45 | 3200 | 1.7405 | 0.6465 |
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+ | 0.1461 | 88.65 | 3400 | 1.7535 | 0.6465 |
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  ### Framework versions