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mirfan899
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kids_phoneme_sm_model

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Inference Endpoints
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update model card README.md

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@@ -14,8 +14,8 @@ 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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6936
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- - Cer: 0.2531
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  ## Model description
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@@ -34,7 +34,7 @@ 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: 4e-05
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  - train_batch_size: 2
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  - eval_batch_size: 8
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  - seed: 42
@@ -44,48 +44,48 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Cer |
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- |:-------------:|:-----:|:-----:|:---------------:|:------:|
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- | 3.2437 | 0.74 | 500 | 4.1235 | 1.0 |
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- | 2.8562 | 1.48 | 1000 | 3.5824 | 1.0 |
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- | 2.7606 | 2.22 | 1500 | 3.2239 | 1.0 |
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- | 2.0885 | 2.96 | 2000 | 1.1613 | 0.8147 |
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- | 1.0295 | 3.7 | 2500 | 0.7703 | 0.5125 |
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- | 0.796 | 4.44 | 3000 | 0.6539 | 0.4420 |
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- | 0.6484 | 5.19 | 3500 | 0.6259 | 0.3937 |
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- | 0.6099 | 5.93 | 4000 | 0.5749 | 0.3887 |
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- | 0.5772 | 6.67 | 4500 | 0.6031 | 0.3637 |
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- | 0.5158 | 7.41 | 5000 | 0.5978 | 0.3518 |
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- | 0.4923 | 8.15 | 5500 | 0.5621 | 0.3364 |
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- | 0.4679 | 8.89 | 6000 | 0.5371 | 0.3396 |
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- | 0.4385 | 9.63 | 6500 | 0.5804 | 0.3213 |
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- | 0.4818 | 10.37 | 7000 | 0.5469 | 0.3223 |
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- | 0.3797 | 11.11 | 7500 | 0.5789 | 0.3118 |
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- | 0.3669 | 11.85 | 8000 | 0.5733 | 0.2986 |
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- | 0.3777 | 12.59 | 8500 | 0.6053 | 0.3004 |
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- | 0.3613 | 13.33 | 9000 | 0.6061 | 0.2895 |
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- | 0.3454 | 14.07 | 9500 | 0.6072 | 0.2740 |
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- | 0.3532 | 14.81 | 10000 | 0.6119 | 0.2872 |
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- | 0.3087 | 15.56 | 10500 | 0.6020 | 0.2849 |
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- | 0.3277 | 16.3 | 11000 | 0.6397 | 0.2745 |
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- | 0.2978 | 17.04 | 11500 | 0.6216 | 0.2745 |
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- | 0.2939 | 17.78 | 12000 | 0.6377 | 0.2690 |
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- | 0.2675 | 18.52 | 12500 | 0.6752 | 0.2681 |
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- | 0.2873 | 19.26 | 13000 | 0.6677 | 0.2767 |
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- | 0.2779 | 20.0 | 13500 | 0.6748 | 0.2717 |
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- | 0.28 | 20.74 | 14000 | 0.6771 | 0.2645 |
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- | 0.2688 | 21.48 | 14500 | 0.6618 | 0.2604 |
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- | 0.2234 | 22.22 | 15000 | 0.6791 | 0.2613 |
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- | 0.2464 | 22.96 | 15500 | 0.6665 | 0.2626 |
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- | 0.2254 | 23.7 | 16000 | 0.7028 | 0.2572 |
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- | 0.2132 | 24.44 | 16500 | 0.6985 | 0.2567 |
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- | 0.2424 | 25.19 | 17000 | 0.6731 | 0.2590 |
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- | 0.2447 | 25.93 | 17500 | 0.6780 | 0.2544 |
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- | 0.2209 | 26.67 | 18000 | 0.6729 | 0.2567 |
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- | 0.2102 | 27.41 | 18500 | 0.6844 | 0.2563 |
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- | 0.2185 | 28.15 | 19000 | 0.6922 | 0.2585 |
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- | 0.2294 | 28.89 | 19500 | 0.6940 | 0.2563 |
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- | 0.2208 | 29.63 | 20000 | 0.6936 | 0.2531 |
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  ### Framework versions
 
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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: 15.8911
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+ - Cer: 1.0
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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: 4e-59
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  - train_batch_size: 2
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  - eval_batch_size: 8
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  - seed: 42
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Cer |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---:|
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+ | 6.5005 | 0.74 | 500 | 15.8911 | 1.0 |
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+ | 6.3865 | 1.48 | 1000 | 15.8911 | 1.0 |
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+ | 6.3822 | 2.22 | 1500 | 15.8911 | 1.0 |
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+ | 6.4251 | 2.96 | 2000 | 15.8911 | 1.0 |
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+ | 6.3836 | 3.7 | 2500 | 15.8911 | 1.0 |
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+ | 6.4552 | 4.44 | 3000 | 15.8911 | 1.0 |
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+ | 6.4287 | 5.19 | 3500 | 15.8911 | 1.0 |
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+ | 6.4304 | 5.93 | 4000 | 15.8911 | 1.0 |
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+ | 6.375 | 6.67 | 4500 | 15.8911 | 1.0 |
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+ | 6.5434 | 7.41 | 5000 | 15.8911 | 1.0 |
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+ | 6.319 | 8.15 | 5500 | 15.8911 | 1.0 |
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+ | 6.4693 | 8.89 | 6000 | 15.8911 | 1.0 |
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+ | 6.4585 | 9.63 | 6500 | 15.8911 | 1.0 |
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+ | 6.4795 | 10.37 | 7000 | 15.8911 | 1.0 |
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+ | 6.393 | 11.11 | 7500 | 15.8911 | 1.0 |
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+ | 6.3489 | 11.85 | 8000 | 15.8911 | 1.0 |
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+ | 6.4511 | 12.59 | 8500 | 15.8911 | 1.0 |
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+ | 6.3378 | 13.33 | 9000 | 15.8911 | 1.0 |
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+ | 6.5094 | 14.07 | 9500 | 15.8911 | 1.0 |
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+ | 6.3643 | 14.81 | 10000 | 15.8911 | 1.0 |
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+ | 6.5079 | 15.56 | 10500 | 15.8911 | 1.0 |
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+ | 6.4272 | 16.3 | 11000 | 15.8911 | 1.0 |
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+ | 6.3937 | 17.04 | 11500 | 15.8911 | 1.0 |
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+ | 6.3704 | 17.78 | 12000 | 15.8911 | 1.0 |
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+ | 6.3797 | 18.52 | 12500 | 15.8911 | 1.0 |
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+ | 6.5126 | 19.26 | 13000 | 15.8911 | 1.0 |
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+ | 6.4111 | 20.0 | 13500 | 15.8911 | 1.0 |
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+ | 6.3557 | 20.74 | 14000 | 15.8911 | 1.0 |
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+ | 6.5705 | 21.48 | 14500 | 15.8911 | 1.0 |
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+ | 6.3207 | 22.22 | 15000 | 15.8911 | 1.0 |
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+ | 6.4664 | 22.96 | 15500 | 15.8911 | 1.0 |
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+ | 6.4401 | 23.7 | 16000 | 15.8911 | 1.0 |
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+ | 6.4136 | 24.44 | 16500 | 15.8911 | 1.0 |
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+ | 6.5345 | 25.19 | 17000 | 15.8911 | 1.0 |
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+ | 6.4097 | 25.93 | 17500 | 15.8911 | 1.0 |
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+ | 6.2736 | 26.67 | 18000 | 15.8911 | 1.0 |
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+ | 6.5474 | 27.41 | 18500 | 15.8911 | 1.0 |
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+ | 6.3997 | 28.15 | 19000 | 15.8911 | 1.0 |
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+ | 6.3997 | 28.89 | 19500 | 15.8911 | 1.0 |
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+ | 6.5538 | 29.63 | 20000 | 15.8911 | 1.0 |
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  ### Framework versions