End of training

README.md

@@ -0,0 +1,113 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: whisper-small-Kurdish-Sorani-10
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# whisper-small-Kurdish-Sorani-10
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1166
+- Wer Ortho: 14.4007
+- Wer: 13.1989
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer Ortho | Wer     |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:-------:|
+| 0.2207        | 0.0992 | 1000  | 0.2857          | 48.3163   | 44.5546 |
+| 0.1701        | 0.1984 | 2000  | 0.2396          | 41.9819   | 38.2653 |
+| 0.1551        | 0.2976 | 3000  | 0.2099          | 37.3690   | 33.9086 |
+| 0.1213        | 0.3968 | 4000  | 0.1918          | 34.8926   | 31.6996 |
+| 0.1205        | 0.4960 | 5000  | 0.1757          | 32.6973   | 29.4823 |
+| 0.1126        | 0.5952 | 6000  | 0.1654          | 31.8523   | 28.7945 |
+| 0.1229        | 0.6944 | 7000  | 0.1520          | 29.2376   | 26.4927 |
+| 0.0966        | 0.7937 | 8000  | 0.1459          | 28.1116   | 25.5538 |
+| 0.0805        | 0.8929 | 9000  | 0.1345          | 26.0589   | 23.7183 |
+| 0.0829        | 0.9921 | 10000 | 0.1290          | 25.4676   | 23.3069 |
+| 0.0503        | 1.0913 | 11000 | 0.1261          | 24.1885   | 21.9946 |
+| 0.0363        | 1.1905 | 12000 | 0.1212          | 23.0877   | 21.0642 |
+| 0.0562        | 1.2897 | 13000 | 0.1177          | 22.5090   | 20.7266 |
+| 0.0382        | 1.3889 | 14000 | 0.1152          | 21.6053   | 19.8785 |
+| 0.0457        | 1.4881 | 15000 | 0.1143          | 21.0224   | 19.4502 |
+| 0.0394        | 1.5873 | 16000 | 0.1072          | 20.3892   | 18.8130 |
+| 0.0427        | 1.6865 | 17000 | 0.1066          | 19.8482   | 18.2814 |
+| 0.03          | 1.7857 | 18000 | 0.1033          | 19.0619   | 17.5957 |
+| 0.0311        | 1.8849 | 19000 | 0.1018          | 18.7390   | 17.2391 |
+| 0.0308        | 1.9841 | 20000 | 0.1004          | 18.8753   | 17.3172 |
+| 0.0297        | 2.0833 | 21000 | 0.1034          | 18.1309   | 16.7623 |
+| 0.0158        | 2.1825 | 22000 | 0.1052          | 18.5042   | 17.1463 |
+| 0.0157        | 2.2817 | 23000 | 0.1039          | 17.8290   | 16.4374 |
+| 0.0367        | 2.3810 | 24000 | 0.1022          | 18.0953   | 16.8129 |
+| 0.0144        | 2.4802 | 25000 | 0.1041          | 17.3551   | 16.0724 |
+| 0.01          | 2.5794 | 26000 | 0.1051          | 17.3132   | 15.9880 |
+| 0.0116        | 2.6786 | 27000 | 0.1046          | 16.8561   | 15.4711 |
+| 0.0149        | 2.7778 | 28000 | 0.1011          | 16.9861   | 15.5914 |
+| 0.02          | 2.8770 | 29000 | 0.1008          | 16.4367   | 15.1357 |
+| 0.0122        | 2.9762 | 30000 | 0.1002          | 16.1914   | 14.9352 |
+| 0.004         | 3.0754 | 31000 | 0.1057          | 15.6861   | 14.3403 |
+| 0.0055        | 3.1746 | 32000 | 0.1067          | 15.7783   | 14.4795 |
+| 0.0045        | 3.2738 | 33000 | 0.1089          | 15.7133   | 14.3761 |
+| 0.0084        | 3.3730 | 34000 | 0.1072          | 15.7196   | 14.4500 |
+| 0.0046        | 3.4722 | 35000 | 0.1087          | 15.7825   | 14.4837 |
+| 0.0032        | 3.5714 | 36000 | 0.1094          | 15.3757   | 14.1567 |
+| 0.0085        | 3.6706 | 37000 | 0.1071          | 15.4303   | 14.1989 |
+| 0.0064        | 3.7698 | 38000 | 0.1106          | 15.2688   | 14.0280 |
+| 0.0037        | 3.8690 | 39000 | 0.1086          | 14.9836   | 13.7263 |
+| 0.0123        | 3.9683 | 40000 | 0.1109          | 14.7886   | 13.5639 |
+| 0.0021        | 4.0675 | 41000 | 0.1135          | 14.7362   | 13.4900 |
+| 0.0017        | 4.1667 | 42000 | 0.1142          | 14.5685   | 13.3402 |
+| 0.0019        | 4.2659 | 43000 | 0.1144          | 14.6964   | 13.4141 |
+| 0.0013        | 4.3651 | 44000 | 0.1156          | 14.6796   | 13.4225 |
+| 0.0051        | 4.4643 | 45000 | 0.1155          | 14.5769   | 13.3381 |
+| 0.001         | 4.5635 | 46000 | 0.1162          | 14.4846   | 13.2727 |
+| 0.0008        | 4.6627 | 47000 | 0.1170          | 14.5119   | 13.3086 |
+| 0.0045        | 4.7619 | 48000 | 0.1149          | 14.6083   | 13.4098 |
+| 0.0012        | 4.8611 | 49000 | 0.1164          | 14.3609   | 13.1672 |
+| 0.0007        | 4.9603 | 50000 | 0.1166          | 14.4007   | 13.1989 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu118
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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