whisper-medium-sb-lug-eng

README.md

@@ -1,199 +1,90 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+base_model: openai/whisper-medium
+tags:
+- generated_from_trainer
+datasets:
+- generator
+model-index:
+- name: whisper-medium-sb-lug-eng
+  results: []
 ---
 
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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+<!-- 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-medium-sb-lug-eng
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1181
+- Wer Lug: 0.458
+- Wer Eng: 0.023
+- Wer Mean: 0.24
+- Cer Lug: 0.273
+- Cer Eng: 0.011
+- Cer Mean: 0.142
+
+## 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: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 12000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer Lug | Wer Eng | Wer Mean | Cer Lug | Cer Eng | Cer Mean |
+|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:--------:|:-------:|:-------:|:--------:|
+| 0.6895        | 0.0417 | 500   | 0.2672          | 0.398   | 0.031   | 0.214    | 0.084   | 0.016   | 0.05     |
+| 0.5359        | 0.0833 | 1000  | 0.1889          | 0.594   | 0.027   | 0.311    | 0.287   | 0.012   | 0.149    |
+| 0.4759        | 0.125  | 1500  | 0.1722          | 0.216   | 0.027   | 0.121    | 0.12    | 0.011   | 0.066    |
+| 0.4283        | 0.1667 | 2000  | 0.1595          | 0.224   | 0.024   | 0.124    | 0.062   | 0.012   | 0.037    |
+| 0.3989        | 0.2083 | 2500  | 0.1502          | 0.193   | 0.026   | 0.109    | 0.062   | 0.011   | 0.037    |
+| 0.3676        | 0.25   | 3000  | 0.1501          | 0.166   | 0.028   | 0.097    | 0.049   | 0.013   | 0.031    |
+| 0.3475        | 0.2917 | 3500  | 0.1424          | 0.184   | 0.028   | 0.106    | 0.066   | 0.013   | 0.04     |
+| 0.3469        | 0.3333 | 4000  | 0.1388          | 0.246   | 0.029   | 0.138    | 0.111   | 0.013   | 0.062    |
+| 0.3115        | 0.375  | 4500  | 0.1355          | 0.916   | 0.029   | 0.473    | 0.541   | 0.013   | 0.277    |
+| 0.2969        | 0.4167 | 5000  | 0.1343          | 0.304   | 0.028   | 0.166    | 0.137   | 0.009   | 0.073    |
+| 0.2438        | 1.0383 | 5500  | 0.1246          | 0.191   | 0.027   | 0.109    | 0.081   | 0.011   | 0.046    |
+| 0.237         | 1.08   | 6000  | 0.1293          | 0.193   | 0.026   | 0.109    | 0.081   | 0.01    | 0.045    |
+| 0.2192        | 1.1217 | 6500  | 0.1314          | 0.199   | 0.022   | 0.111    | 0.09    | 0.008   | 0.049    |
+| 0.2404        | 1.1633 | 7000  | 0.1308          | 0.136   | 0.028   | 0.082    | 0.049   | 0.01    | 0.03     |
+| 0.2185        | 1.205  | 7500  | 0.1283          | 0.179   | 0.019   | 0.099    | 0.068   | 0.007   | 0.038    |
+| 0.22          | 1.2467 | 8000  | 0.1235          | 0.272   | 0.025   | 0.149    | 0.164   | 0.01    | 0.087    |
+| 0.2252        | 1.2883 | 8500  | 0.1263          | 0.243   | 0.023   | 0.133    | 0.108   | 0.009   | 0.059    |
+| 0.2121        | 1.33   | 9000  | 0.1238          | 0.549   | 0.037   | 0.293    | 0.27    | 0.019   | 0.145    |
+| 0.2106        | 1.3717 | 9500  | 0.1213          | 0.23    | 0.024   | 0.127    | 0.112   | 0.009   | 0.06     |
+| 0.2095        | 1.4133 | 10000 | 0.1201          | 0.305   | 0.027   | 0.166    | 0.169   | 0.013   | 0.091    |
+| 0.1517        | 2.035  | 10500 | 0.1206          | 0.376   | 0.026   | 0.201    | 0.212   | 0.011   | 0.111    |
+| 0.1639        | 2.0767 | 11000 | 0.1200          | 0.415   | 0.022   | 0.219    | 0.262   | 0.01    | 0.136    |
+| 0.1561        | 2.1183 | 11500 | 0.1185          | 0.497   | 0.023   | 0.26     | 0.301   | 0.011   | 0.156    |
+| 0.1634        | 2.16   | 12000 | 0.1181          | 0.458   | 0.023   | 0.24     | 0.273   | 0.011   | 0.142    |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.2.0
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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