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Model_G_2

Automatic Speech Recognition
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PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Eval Results
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@@ -3,11 +3,26 @@ license: apache-2.0
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  base_model: facebook/wav2vec2-large-xlsr-53
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  tags:
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  - generated_from_trainer
 
 
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  metrics:
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  - wer
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  model-index:
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  - name: Model_G_2
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -15,11 +30,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # Model_G_2
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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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7332
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- - Wer: 1.0098
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- - Cer: 0.7490
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  ## Model description
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@@ -51,19 +66,35 @@ The following hyperparameters were used during training:
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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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- | 3.5212 | 2.57 | 400 | 0.7741 | 1.0236 | 0.7838 |
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- | 0.3158 | 5.14 | 800 | 0.6119 | 1.0085 | 0.7600 |
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- | 0.1522 | 7.72 | 1200 | 0.6402 | 1.0215 | 0.7521 |
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- | 0.102 | 10.29 | 1600 | 0.6226 | 1.0134 | 0.7540 |
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- | 0.0752 | 12.86 | 2000 | 0.6474 | 1.0365 | 0.7501 |
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- | 0.0627 | 15.43 | 2400 | 0.6617 | 1.0169 | 0.7503 |
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- | 0.0535 | 18.01 | 2800 | 0.6818 | 1.0116 | 0.7495 |
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- | 0.0432 | 20.58 | 3200 | 0.7056 | 1.0125 | 0.7536 |
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- | 0.0383 | 23.15 | 3600 | 0.6953 | 1.0096 | 0.7448 |
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- | 0.0347 | 25.72 | 4000 | 0.7217 | 1.0202 | 0.7457 |
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- | 0.0301 | 28.3 | 4400 | 0.7332 | 1.0098 | 0.7490 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  base_model: facebook/wav2vec2-large-xlsr-53
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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: Model_G_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: 0.9848965131456274
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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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  # Model_G_2
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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: 0.0374
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+ - Wer: 0.9849
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+ - Cer: 0.7098
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  ## Model description
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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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+ | 3.6149 | 1.07 | 400 | 0.4672 | 1.0114 | 0.7588 |
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+ | 0.4341 | 2.15 | 800 | 0.2008 | 0.9972 | 0.7369 |
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+ | 0.2665 | 3.22 | 1200 | 0.1283 | 0.9986 | 0.7180 |
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+ | 0.2114 | 4.3 | 1600 | 0.1016 | 0.9995 | 0.7135 |
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+ | 0.1768 | 5.37 | 2000 | 0.0774 | 0.9950 | 0.7208 |
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+ | 0.1531 | 6.44 | 2400 | 0.0682 | 0.9933 | 0.7137 |
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+ | 0.1352 | 7.52 | 2800 | 0.0690 | 0.9883 | 0.7022 |
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+ | 0.1252 | 8.59 | 3200 | 0.0656 | 0.9925 | 0.7091 |
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+ | 0.1144 | 9.66 | 3600 | 0.0521 | 0.9888 | 0.7124 |
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+ | 0.0986 | 10.74 | 4000 | 0.0527 | 0.9915 | 0.7067 |
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+ | 0.0875 | 11.81 | 4400 | 0.0531 | 0.9902 | 0.7057 |
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+ | 0.0883 | 12.89 | 4800 | 0.0488 | 0.9888 | 0.7136 |
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+ | 0.0812 | 13.96 | 5200 | 0.0461 | 0.9884 | 0.7122 |
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+ | 0.0721 | 15.03 | 5600 | 0.0474 | 0.9884 | 0.7128 |
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+ | 0.0681 | 16.11 | 6000 | 0.0469 | 0.9869 | 0.7243 |
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+ | 0.0671 | 17.18 | 6400 | 0.0450 | 0.9878 | 0.7086 |
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+ | 0.0613 | 18.26 | 6800 | 0.0492 | 0.9852 | 0.7171 |
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+ | 0.0573 | 19.33 | 7200 | 0.0435 | 0.9852 | 0.7209 |
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+ | 0.0531 | 20.4 | 7600 | 0.0389 | 0.9908 | 0.7071 |
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+ | 0.0493 | 21.48 | 8000 | 0.0423 | 0.9871 | 0.7166 |
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+ | 0.0477 | 22.55 | 8400 | 0.0416 | 0.9843 | 0.7127 |
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+ | 0.0441 | 23.62 | 8800 | 0.0372 | 0.9864 | 0.7075 |
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+ | 0.0412 | 24.7 | 9200 | 0.0408 | 0.9857 | 0.7118 |
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+ | 0.0392 | 25.77 | 9600 | 0.0407 | 0.9851 | 0.7152 |
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+ | 0.0359 | 26.85 | 10000 | 0.0383 | 0.9861 | 0.7086 |
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+ | 0.0347 | 27.92 | 10400 | 0.0373 | 0.9852 | 0.7066 |
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+ | 0.0327 | 28.99 | 10800 | 0.0374 | 0.9849 | 0.7098 |
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  ### Framework versions