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End of training

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  1. README.md +17 -16
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -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: 0.23381058715355313
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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-base](https://huggingface.co/facebook/wav2vec2-base) on the vivos dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3492
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- - Wer: 0.2338
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  ## Model description
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@@ -52,31 +52,32 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 128
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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
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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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- | 8.4226 | 2.0548 | 150 | 4.9423 | 1.0 |
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- | 3.59 | 4.1096 | 300 | 3.6898 | 1.0 |
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- | 3.4271 | 6.1644 | 450 | 3.5183 | 1.0 |
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- | 2.6948 | 8.2192 | 600 | 1.2770 | 0.8026 |
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- | 0.7372 | 10.2740 | 750 | 0.5197 | 0.3625 |
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- | 0.4012 | 12.3288 | 900 | 0.4108 | 0.2911 |
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- | 0.2974 | 14.3836 | 1050 | 0.3732 | 0.2604 |
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- | 0.2737 | 16.4384 | 1200 | 0.3550 | 0.2393 |
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- | 0.2108 | 18.4932 | 1350 | 0.3565 | 0.2434 |
 
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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: 0.239169022417987
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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-base](https://huggingface.co/facebook/wav2vec2-base) on the vivos dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3501
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+ - Wer: 0.2392
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 8e-05
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  - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_steps: 500
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  - num_epochs: 20
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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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+ | 8.7262 | 2.0 | 146 | 5.1186 | 1.0 |
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+ | 3.6815 | 4.0 | 292 | 3.6847 | 1.0 |
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+ | 3.4316 | 6.0 | 438 | 3.5415 | 1.0 |
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+ | 2.8102 | 8.0 | 584 | 1.5866 | 0.9160 |
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+ | 0.8818 | 10.0 | 730 | 0.5903 | 0.4066 |
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+ | 0.4305 | 12.0 | 876 | 0.4283 | 0.3104 |
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+ | 0.3067 | 14.0 | 1022 | 0.3793 | 0.2762 |
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+ | 0.2819 | 16.0 | 1168 | 0.3620 | 0.2496 |
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+ | 0.2235 | 18.0 | 1314 | 0.3507 | 0.2405 |
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+ | 0.211 | 20.0 | 1460 | 0.3501 | 0.2392 |
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  ### Framework versions
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