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makhataei
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Whisper-Small-Ctejarat

Automatic Speech Recognition
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Safetensors
Persian
whisper
fa-asr
Generated from Trainer
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End of training

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  1. README.md +53 -53
  2. model.safetensors +1 -1
  3. runs/Mar24_06-25-05_Software-AI/events.out.tfevents.1711248906.Software-AI.917997.0 +2 -2
README.md CHANGED
@@ -20,8 +20,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [makhataei/Whisper-Small-Ctejarat](https://huggingface.co/makhataei/Whisper-Small-Ctejarat) on the Ctejarat dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0096
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- - Wer: 14.7297
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  ## Model description
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@@ -40,7 +40,7 @@ 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: 1e-06
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -55,56 +55,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:------:|:----:|:---------------:|:-------:|
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- | 0.0042 | 9.88 | 100 | 0.0098 | 15.0170 |
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- | 0.0011 | 19.75 | 200 | 0.0096 | 14.6775 |
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- | 0.0004 | 29.63 | 300 | 0.0096 | 14.7558 |
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- | 0.0002 | 39.51 | 400 | 0.0096 | 14.7819 |
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- | 0.0001 | 49.38 | 500 | 0.0096 | 14.7558 |
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- | 0.0001 | 59.26 | 600 | 0.0095 | 14.7819 |
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- | 0.0001 | 69.14 | 700 | 0.0095 | 14.8080 |
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- | 0.0001 | 79.01 | 800 | 0.0095 | 14.8342 |
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- | 0.0001 | 88.89 | 900 | 0.0095 | 14.8864 |
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- | 0.0001 | 98.77 | 1000 | 0.0095 | 14.9125 |
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- | 0.0 | 108.64 | 1100 | 0.0095 | 14.9647 |
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- | 0.0 | 118.52 | 1200 | 0.0095 | 15.0170 |
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- | 0.0 | 128.4 | 1300 | 0.0095 | 15.0431 |
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- | 0.0 | 138.27 | 1400 | 0.0095 | 15.0431 |
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- | 0.0 | 148.15 | 1500 | 0.0095 | 15.0431 |
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- | 0.0 | 158.02 | 1600 | 0.0095 | 15.0431 |
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- | 0.0 | 167.9 | 1700 | 0.0095 | 15.0431 |
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- | 0.0 | 177.78 | 1800 | 0.0095 | 15.0692 |
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- | 0.0 | 187.65 | 1900 | 0.0095 | 15.0692 |
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- | 0.0 | 197.53 | 2000 | 0.0095 | 15.0953 |
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- | 0.0 | 207.41 | 2100 | 0.0095 | 15.0953 |
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- | 0.0 | 217.28 | 2200 | 0.0095 | 15.1214 |
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- | 0.0 | 227.16 | 2300 | 0.0095 | 15.1214 |
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- | 0.0 | 237.04 | 2400 | 0.0095 | 15.1214 |
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- | 0.0 | 246.91 | 2500 | 0.0095 | 15.1214 |
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- | 0.0 | 256.79 | 2600 | 0.0095 | 15.1476 |
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- | 0.0 | 266.67 | 2700 | 0.0095 | 15.1476 |
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- | 0.0 | 276.54 | 2800 | 0.0095 | 15.1737 |
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- | 0.0 | 286.42 | 2900 | 0.0095 | 15.1737 |
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- | 0.0 | 296.3 | 3000 | 0.0095 | 15.1998 |
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- | 0.0 | 306.17 | 3100 | 0.0096 | 14.9386 |
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- | 0.0 | 316.05 | 3200 | 0.0096 | 14.9386 |
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- | 0.0 | 325.93 | 3300 | 0.0096 | 14.9386 |
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- | 0.0 | 335.8 | 3400 | 0.0096 | 14.9386 |
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- | 0.0 | 345.68 | 3500 | 0.0096 | 14.9386 |
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- | 0.0 | 355.56 | 3600 | 0.0096 | 14.9386 |
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- | 0.0 | 365.43 | 3700 | 0.0096 | 14.9386 |
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- | 0.0 | 375.31 | 3800 | 0.0096 | 14.5469 |
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- | 0.0 | 385.19 | 3900 | 0.0096 | 14.5730 |
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- | 0.0 | 395.06 | 4000 | 0.0096 | 14.5730 |
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- | 0.0 | 404.94 | 4100 | 0.0096 | 14.5991 |
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- | 0.0 | 414.81 | 4200 | 0.0096 | 14.5991 |
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- | 0.0 | 424.69 | 4300 | 0.0096 | 14.7036 |
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- | 0.0 | 434.57 | 4400 | 0.0096 | 14.7297 |
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- | 0.0 | 444.44 | 4500 | 0.0096 | 14.7297 |
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- | 0.0 | 454.32 | 4600 | 0.0096 | 14.7297 |
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- | 0.0 | 464.2 | 4700 | 0.0096 | 14.7297 |
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- | 0.0 | 474.07 | 4800 | 0.0096 | 14.7297 |
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- | 0.0 | 483.95 | 4900 | 0.0096 | 14.7297 |
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- | 0.0 | 493.83 | 5000 | 0.0096 | 14.7297 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [makhataei/Whisper-Small-Ctejarat](https://huggingface.co/makhataei/Whisper-Small-Ctejarat) on the Ctejarat dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0031
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+ - Wer: 13.3353
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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: 1e-07
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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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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:------:|:----:|:---------------:|:-------:|
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+ | 0.0032 | 9.52 | 100 | 0.0031 | 13.1328 |
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+ | 0.0027 | 19.05 | 200 | 0.0031 | 13.0171 |
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+ | 0.002 | 28.57 | 300 | 0.0031 | 13.0171 |
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+ | 0.0015 | 38.1 | 400 | 0.0031 | 13.0749 |
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+ | 0.0005 | 47.62 | 500 | 0.0031 | 13.3642 |
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+ | 0.0003 | 57.14 | 600 | 0.0031 | 13.3353 |
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+ | 0.0002 | 66.67 | 700 | 0.0031 | 13.3353 |
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+ | 0.0002 | 76.19 | 800 | 0.0031 | 13.3353 |
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+ | 0.0001 | 85.71 | 900 | 0.0031 | 13.3063 |
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+ | 0.0001 | 95.24 | 1000 | 0.0031 | 13.3063 |
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+ | 0.0001 | 104.76 | 1100 | 0.0031 | 13.3063 |
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+ | 0.0001 | 114.29 | 1200 | 0.0031 | 13.3931 |
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+ | 0.0001 | 123.81 | 1300 | 0.0031 | 13.3931 |
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+ | 0.0001 | 133.33 | 1400 | 0.0031 | 13.3642 |
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+ | 0.0001 | 142.86 | 1500 | 0.0031 | 13.3931 |
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+ | 0.0001 | 152.38 | 1600 | 0.0031 | 13.3931 |
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+ | 0.0001 | 161.9 | 1700 | 0.0031 | 13.3642 |
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+ | 0.0 | 171.43 | 1800 | 0.0031 | 13.3642 |
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+ | 0.0001 | 180.95 | 1900 | 0.0031 | 13.3642 |
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+ | 0.0 | 190.48 | 2000 | 0.0031 | 13.3642 |
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+ | 0.0 | 200.0 | 2100 | 0.0031 | 13.3642 |
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+ | 0.0 | 209.52 | 2200 | 0.0031 | 13.3642 |
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+ | 0.0 | 219.05 | 2300 | 0.0031 | 13.3642 |
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+ | 0.0 | 228.57 | 2400 | 0.0031 | 13.3642 |
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+ | 0.0 | 238.1 | 2500 | 0.0031 | 13.3353 |
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+ | 0.0 | 247.62 | 2600 | 0.0031 | 13.3353 |
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+ | 0.0 | 257.14 | 2700 | 0.0031 | 13.3353 |
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+ | 0.0 | 266.67 | 2800 | 0.0031 | 13.3353 |
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+ | 0.0 | 276.19 | 2900 | 0.0031 | 13.3353 |
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+ | 0.0 | 285.71 | 3000 | 0.0031 | 13.3353 |
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+ | 0.0 | 295.24 | 3100 | 0.0031 | 13.3353 |
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+ | 0.0 | 304.76 | 3200 | 0.0031 | 13.3353 |
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+ | 0.0 | 314.29 | 3300 | 0.0031 | 13.3642 |
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+ | 0.0 | 323.81 | 3400 | 0.0031 | 13.3642 |
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+ | 0.0 | 333.33 | 3500 | 0.0031 | 13.3642 |
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+ | 0.0 | 342.86 | 3600 | 0.0031 | 13.3642 |
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+ | 0.0 | 352.38 | 3700 | 0.0031 | 13.3353 |
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+ | 0.0 | 361.9 | 3800 | 0.0031 | 13.3353 |
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+ | 0.0 | 371.43 | 3900 | 0.0031 | 13.3353 |
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+ | 0.0 | 380.95 | 4000 | 0.0031 | 13.3353 |
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+ | 0.0 | 390.48 | 4100 | 0.0031 | 13.3353 |
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+ | 0.0 | 400.0 | 4200 | 0.0031 | 13.3353 |
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+ | 0.0 | 409.52 | 4300 | 0.0031 | 13.3353 |
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+ | 0.0 | 419.05 | 4400 | 0.0031 | 13.3353 |
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+ | 0.0 | 428.57 | 4500 | 0.0031 | 13.3353 |
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+ | 0.0 | 438.1 | 4600 | 0.0031 | 13.3353 |
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+ | 0.0 | 447.62 | 4700 | 0.0031 | 13.3353 |
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+ | 0.0 | 457.14 | 4800 | 0.0031 | 13.3353 |
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+ | 0.0 | 466.67 | 4900 | 0.0031 | 13.3353 |
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+ | 0.0 | 476.19 | 5000 | 0.0031 | 13.3353 |
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  ### Framework versions
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