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nacielo
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wav2GPT2MusiSD3200

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
speech-encoder-decoder
Generated from Trainer
Inference Endpoints
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update model card README.md

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@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9236
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  - Rouge1: 28.7212
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  - Rouge2: 7.4616
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  - Rougel: 21.8892
@@ -40,28 +40,38 @@ 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: 1e-05
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  - train_batch_size: 4
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  - eval_batch_size: 4
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  - seed: 42
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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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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
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- | 1.2945 | 1.0 | 1361 | 1.1031 | 27.9697 | 6.7041 | 21.6712 | 21.6064 | 55.0 |
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- | 1.212 | 2.0 | 2722 | 1.0544 | 27.9697 | 6.7041 | 21.6712 | 21.6064 | 55.0 |
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- | 1.1771 | 3.0 | 4083 | 1.0171 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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- | 1.124 | 4.0 | 5444 | 0.9856 | 30.3958 | 7.6795 | 22.3852 | 22.4201 | 62.0 |
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- | 1.1177 | 5.0 | 6805 | 0.9591 | 30.2957 | 8.1085 | 22.6405 | 22.5926 | 50.0 |
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- | 1.1168 | 6.0 | 8166 | 0.9419 | 27.9697 | 6.7041 | 21.6712 | 21.6064 | 55.0 |
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- | 1.1277 | 7.0 | 9527 | 0.9304 | 27.9697 | 6.7041 | 21.6712 | 21.6064 | 55.0 |
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- | 1.1256 | 8.0 | 10888 | 0.9227 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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- | 1.1617 | 9.0 | 12249 | 0.9222 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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- | 1.2002 | 10.0 | 13610 | 0.9236 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8962
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  - Rouge1: 28.7212
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  - Rouge2: 7.4616
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  - Rougel: 21.8892
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-06
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  - train_batch_size: 4
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  - eval_batch_size: 4
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  - seed: 42
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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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+ - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
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+ | 1.0361 | 1.0 | 1361 | 0.9081 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.0015 | 2.0 | 2722 | 0.9021 | 28.1788 | 7.304 | 21.4695 | 21.4607 | 50.0 |
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+ | 1.0003 | 3.0 | 4083 | 0.8976 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 0.9761 | 4.0 | 5444 | 0.8914 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 0.9928 | 5.0 | 6805 | 0.8884 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 1.013 | 6.0 | 8166 | 0.8858 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 1.0476 | 7.0 | 9527 | 0.8852 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 1.0649 | 8.0 | 10888 | 0.8847 | 27.7311 | 6.574 | 21.4759 | 21.4402 | 57.0 |
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+ | 1.1224 | 9.0 | 12249 | 0.8888 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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+ | 1.1818 | 10.0 | 13610 | 0.8949 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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+ | 1.1832 | 11.0 | 14971 | 0.8981 | 30.1982 | 7.2766 | 22.0853 | 22.118 | 59.0 |
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+ | 1.1878 | 12.0 | 16332 | 0.8987 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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+ | 1.1833 | 13.0 | 17693 | 0.8983 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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+ | 1.1772 | 14.0 | 19054 | 0.8980 | 29.7298 | 7.2318 | 22.3598 | 22.3739 | 56.0 |
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+ | 1.1723 | 15.0 | 20415 | 0.8974 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.1778 | 16.0 | 21776 | 0.8972 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.1707 | 17.0 | 23137 | 0.8968 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.1767 | 18.0 | 24498 | 0.8964 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.17 | 19.0 | 25859 | 0.8962 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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+ | 1.1737 | 20.0 | 27220 | 0.8962 | 28.7212 | 7.4616 | 21.8892 | 21.8832 | 46.0 |
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  ### Framework versions