lilitket
/

wav2vec2-large-xls-r-300m-turkish-colab

@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3853
-- Wer: 0.3258
 ## Model description
@@ -37,29 +37,71 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 0.0003
-- train_batch_size: 4
 - eval_batch_size: 8
 - seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- num_epochs: 30
 - mixed_precision_training: Native AMP
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 3.8377        | 3.7   | 400  | 0.6728          | 0.6887 |
-| 0.4209        | 7.4   | 800  | 0.4591          | 0.5044 |
-| 0.1976        | 11.11 | 1200 | 0.4477          | 0.4294 |
-| 0.1338        | 14.81 | 1600 | 0.4364          | 0.4132 |
-| 0.1024        | 18.51 | 2000 | 0.4454          | 0.3741 |
-| 0.0785        | 22.22 | 2400 | 0.4093          | 0.3580 |
-| 0.0613        | 25.92 | 2800 | 0.3930          | 0.3391 |
-| 0.0495        | 29.63 | 3200 | 0.3853          | 0.3258 |
 ### Framework versions

 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.7126
+- Wer: 0.8198
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 0.0003
+- train_batch_size: 1
 - eval_batch_size: 8
 - seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
+- num_epochs: 120
 - mixed_precision_training: Native AMP
 ### Training results
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 6.7419        | 2.38   | 200   | 3.1913          | 1.0    |
+| 3.0446        | 4.76   | 400   | 2.3247          | 1.0    |
+| 1.3163        | 7.14   | 600   | 1.2629          | 0.9656 |
+| 0.6058        | 9.52   | 800   | 1.2203          | 0.9343 |
+| 0.3687        | 11.9   | 1000  | 1.2157          | 0.8849 |
+| 0.2644        | 14.29  | 1200  | 1.3693          | 0.8992 |
+| 0.2147        | 16.67  | 1400  | 1.3321          | 0.8623 |
+| 0.1962        | 19.05  | 1600  | 1.3476          | 0.8886 |
+| 0.1631        | 21.43  | 1800  | 1.3984          | 0.8755 |
+| 0.15          | 23.81  | 2000  | 1.4602          | 0.8798 |
+| 0.1311        | 26.19  | 2200  | 1.4727          | 0.8836 |
+| 0.1174        | 28.57  | 2400  | 1.5257          | 0.8805 |
+| 0.1155        | 30.95  | 2600  | 1.4697          | 0.9337 |
+| 0.1046        | 33.33  | 2800  | 1.6076          | 0.8667 |
+| 0.1063        | 35.71  | 3000  | 1.5012          | 0.8861 |
+| 0.0996        | 38.1   | 3200  | 1.6204          | 0.8605 |
+| 0.088         | 40.48  | 3400  | 1.4788          | 0.8586 |
+| 0.089         | 42.86  | 3600  | 1.5983          | 0.8648 |
+| 0.0805        | 45.24  | 3800  | 1.5045          | 0.8298 |
+| 0.0718        | 47.62  | 4000  | 1.6361          | 0.8611 |
+| 0.0718        | 50.0   | 4200  | 1.5088          | 0.8548 |
+| 0.0649        | 52.38  | 4400  | 1.5491          | 0.8554 |
+| 0.0685        | 54.76  | 4600  | 1.5939          | 0.8442 |
+| 0.0588        | 57.14  | 4800  | 1.6321          | 0.8536 |
+| 0.0591        | 59.52  | 5000  | 1.6468          | 0.8442 |
+| 0.0529        | 61.9   | 5200  | 1.6086          | 0.8661 |
+| 0.0482        | 64.29  | 5400  | 1.6622          | 0.8517 |
+| 0.0396        | 66.67  | 5600  | 1.6191          | 0.8436 |
+| 0.0463        | 69.05  | 5800  | 1.6231          | 0.8661 |
+| 0.0415        | 71.43  | 6000  | 1.6874          | 0.8511 |
+| 0.0383        | 73.81  | 6200  | 1.7054          | 0.8411 |
+| 0.0411        | 76.19  | 6400  | 1.7073          | 0.8486 |
+| 0.0346        | 78.57  | 6600  | 1.7137          | 0.8342 |
+| 0.0318        | 80.95  | 6800  | 1.6523          | 0.8329 |
+| 0.0299        | 83.33  | 7000  | 1.6893          | 0.8579 |
+| 0.029         | 85.71  | 7200  | 1.7162          | 0.8429 |
+| 0.025         | 88.1   | 7400  | 1.7589          | 0.8529 |
+| 0.025         | 90.48  | 7600  | 1.7581          | 0.8398 |
+| 0.0232        | 92.86  | 7800  | 1.8459          | 0.8442 |
+| 0.0215        | 95.24  | 8000  | 1.7942          | 0.8448 |
+| 0.0222        | 97.62  | 8200  | 1.6848          | 0.8442 |
+| 0.0179        | 100.0  | 8400  | 1.7223          | 0.8298 |
+| 0.0176        | 102.38 | 8600  | 1.7426          | 0.8404 |
+| 0.016         | 104.76 | 8800  | 1.7501          | 0.8411 |
+| 0.0153        | 107.14 | 9000  | 1.7185          | 0.8235 |
+| 0.0136        | 109.52 | 9200  | 1.7250          | 0.8292 |
+| 0.0117        | 111.9  | 9400  | 1.7159          | 0.8185 |
+| 0.0123        | 114.29 | 9600  | 1.7135          | 0.8248 |
+| 0.0121        | 116.67 | 9800  | 1.7189          | 0.8210 |
+| 0.0116        | 119.05 | 10000 | 1.7126          | 0.8198 |
 ### Framework versions