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--- |
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license: apache-2.0 |
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base_model: openai/whisper-medium |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-medium-pt-3000h-ct2 |
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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: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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default |
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type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.11007210455159983 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# whisper-medium-pt-3000h-ct2 |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. |
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It was converted to the CTranslate2 format. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9306 |
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- Wer: 0.1101 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 10000 |
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- num_epochs: 10.0 |
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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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| 0.4423 | 0.2 | 20000 | 0.4723 | 0.1633 | |
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| 0.4963 | 0.39 | 40000 | 0.4921 | 0.1547 | |
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| 0.3853 | 0.59 | 60000 | 0.5099 | 0.1470 | |
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| 0.37 | 0.79 | 80000 | 0.4753 | 0.1439 | |
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| 0.3615 | 0.98 | 100000 | 0.5074 | 0.1386 | |
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| 0.2394 | 1.18 | 120000 | 0.4858 | 0.1341 | |
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| 0.227 | 1.38 | 140000 | 0.5758 | 0.1323 | |
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| 0.2461 | 1.57 | 160000 | 0.5067 | 0.1322 | |
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| 0.2078 | 1.77 | 180000 | 0.5087 | 0.1291 | |
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| 0.2138 | 1.97 | 200000 | 0.5201 | 0.1273 | |
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| 0.1188 | 2.16 | 220000 | 0.6359 | 0.1265 | |
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| 0.1009 | 2.36 | 240000 | 0.6229 | 0.1253 | |
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| 0.1394 | 2.56 | 260000 | 0.5734 | 0.1231 | |
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| 0.1383 | 2.75 | 280000 | 0.5914 | 0.1213 | |
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| 0.1332 | 2.95 | 300000 | 0.6174 | 0.1212 | |
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| 0.0634 | 3.15 | 320000 | 0.6461 | 0.1190 | |
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| 0.0667 | 3.34 | 340000 | 0.6330 | 0.1211 | |
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| 0.0546 | 3.54 | 360000 | 0.6927 | 0.1190 | |
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| 0.1029 | 3.74 | 380000 | 0.6777 | 0.1184 | |
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| 0.0664 | 3.93 | 400000 | 0.6367 | 0.1161 | |
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| 0.0665 | 4.13 | 420000 | 0.7467 | 0.1171 | |
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| 0.0695 | 4.33 | 440000 | 0.7332 | 0.1164 | |
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| 0.0708 | 4.52 | 460000 | 0.7141 | 0.1171 | |
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| 0.0695 | 4.72 | 480000 | 0.6869 | 0.1169 | |
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| 0.0758 | 4.92 | 500000 | 0.7360 | 0.1153 | |
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| 0.061 | 5.11 | 520000 | 0.7594 | 0.1161 | |
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| 0.0804 | 5.31 | 540000 | 0.7640 | 0.1158 | |
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| 0.0963 | 5.51 | 560000 | 0.7848 | 0.1157 | |
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| 0.0815 | 5.7 | 580000 | 0.7635 | 0.1145 | |
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| 0.0794 | 5.9 | 600000 | 0.7566 | 0.1134 | |
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| 0.0907 | 6.1 | 620000 | 0.8152 | 0.1147 | |
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| 0.0664 | 6.29 | 640000 | 0.8405 | 0.1123 | |
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| 0.0654 | 6.49 | 660000 | 0.8278 | 0.1119 | |
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| 0.0652 | 6.69 | 680000 | 0.8267 | 0.1134 | |
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| 0.1043 | 6.88 | 700000 | 0.8254 | 0.1122 | |
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| 0.0383 | 7.08 | 720000 | 0.8719 | 0.1122 | |
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| 0.0461 | 7.28 | 740000 | 0.8640 | 0.1130 | |
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| 0.0791 | 7.47 | 760000 | 0.8990 | 0.1122 | |
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| 0.0587 | 7.67 | 780000 | 0.9107 | 0.1122 | |
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| 0.0578 | 7.87 | 800000 | 0.9060 | 0.1124 | |
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| 0.0218 | 8.06 | 820000 | 0.8845 | 0.1111 | |
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| 0.0125 | 8.26 | 840000 | 0.9072 | 0.1112 | |
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| 0.0172 | 8.46 | 860000 | 0.8899 | 0.1107 | |
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| 0.0204 | 8.65 | 880000 | 0.9149 | 0.1108 | |
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| 0.0145 | 8.85 | 900000 | 0.9097 | 0.1103 | |
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| 0.0146 | 9.05 | 920000 | 0.9084 | 0.1107 | |
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| 0.0166 | 9.24 | 940000 | 0.9053 | 0.1103 | |
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| 0.0177 | 9.44 | 960000 | 0.9193 | 0.1100 | |
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| 0.0157 | 9.64 | 980000 | 0.9212 | 0.1101 | |
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| 0.0096 | 9.83 | 1000000 | 0.9313 | 0.1103 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.1.dev0 |
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- Tokenizers 0.15.0 |
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