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---
tags:
- automatic-speech-recognition
- Lemswasabi/tuudle
- generated_from_trainer
datasets:
- tuudle
model-index:
- name: ''
  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. -->

# 

This model is a fine-tuned version of [Lemswasabi/letzspeak](https://huggingface.co/Lemswasabi/letzspeak) on the LEMSWASABI/TUUDLE - RTL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1058
- Wer: 0.1075

## 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: 7.5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.1484        | 0.89  | 500   | 3.0844          | 1.0    |
| 2.6539        | 1.77  | 1000  | 1.7272          | 0.9358 |
| 0.8732        | 2.66  | 1500  | 0.1975          | 0.1609 |
| 0.8075        | 3.55  | 2000  | 0.1483          | 0.1468 |
| 0.7358        | 4.43  | 2500  | 0.1331          | 0.1401 |
| 0.7079        | 5.32  | 3000  | 0.1273          | 0.1364 |
| 0.7032        | 6.21  | 3500  | 0.1133          | 0.1240 |
| 0.7129        | 7.09  | 4000  | 0.1124          | 0.1290 |
| 0.6771        | 7.98  | 4500  | 0.1121          | 0.1300 |
| 0.6859        | 8.86  | 5000  | 0.1095          | 0.1313 |
| 0.6496        | 9.75  | 5500  | 0.1091          | 0.1250 |
| 0.6431        | 10.64 | 6000  | 0.1102          | 0.1293 |
| 0.6422        | 11.52 | 6500  | 0.1107          | 0.1179 |
| 0.6334        | 12.41 | 7000  | 0.1049          | 0.1236 |
| 0.599         | 13.3  | 7500  | 0.1092          | 0.1152 |
| 0.6205        | 14.18 | 8000  | 0.1047          | 0.1219 |
| 0.5944        | 15.07 | 8500  | 0.1068          | 0.1203 |
| 0.6102        | 15.96 | 9000  | 0.1056          | 0.1159 |
| 0.5983        | 16.84 | 9500  | 0.1061          | 0.1152 |
| 0.5882        | 17.73 | 10000 | 0.1043          | 0.1135 |
| 0.5876        | 18.62 | 10500 | 0.1023          | 0.1159 |
| 0.5717        | 19.5  | 11000 | 0.1037          | 0.1233 |
| 0.5537        | 20.39 | 11500 | 0.1070          | 0.1192 |
| 0.5636        | 21.28 | 12000 | 0.1036          | 0.1169 |
| 0.5536        | 22.16 | 12500 | 0.1008          | 0.1182 |
| 0.5656        | 23.05 | 13000 | 0.1010          | 0.1172 |
| 0.5504        | 23.94 | 13500 | 0.1019          | 0.1105 |
| 0.5476        | 24.82 | 14000 | 0.1026          | 0.1166 |
| 0.5375        | 25.71 | 14500 | 0.1107          | 0.1189 |
| 0.5318        | 26.6  | 15000 | 0.1051          | 0.1142 |
| 0.5278        | 27.48 | 15500 | 0.1049          | 0.1166 |
| 0.5204        | 28.37 | 16000 | 0.1081          | 0.1182 |
| 0.512         | 29.26 | 16500 | 0.1062          | 0.1156 |
| 0.5082        | 30.14 | 17000 | 0.1045          | 0.1135 |
| 0.5193        | 31.03 | 17500 | 0.1091          | 0.1145 |
| 0.5129        | 31.91 | 18000 | 0.1040          | 0.1088 |
| 0.5126        | 32.8  | 18500 | 0.1085          | 0.1169 |
| 0.496         | 33.69 | 19000 | 0.1070          | 0.1166 |
| 0.5017        | 34.57 | 19500 | 0.1119          | 0.1162 |
| 0.4808        | 35.46 | 20000 | 0.1101          | 0.1139 |
| 0.4939        | 36.35 | 20500 | 0.1081          | 0.1125 |
| 0.4738        | 37.23 | 21000 | 0.1091          | 0.1098 |
| 0.4978        | 38.12 | 21500 | 0.1057          | 0.1092 |
| 0.4972        | 39.01 | 22000 | 0.1074          | 0.1105 |
| 0.4773        | 39.89 | 22500 | 0.1062          | 0.1108 |
| 0.4741        | 40.78 | 23000 | 0.1057          | 0.1085 |
| 0.4776        | 41.67 | 23500 | 0.1077          | 0.1085 |
| 0.4637        | 42.55 | 24000 | 0.1061          | 0.1095 |
| 0.4853        | 43.44 | 24500 | 0.1081          | 0.1075 |
| 0.4602        | 44.33 | 25000 | 0.1076          | 0.1085 |
| 0.4667        | 45.21 | 25500 | 0.1078          | 0.1078 |
| 0.4484        | 46.1  | 26000 | 0.1056          | 0.1082 |
| 0.4601        | 46.99 | 26500 | 0.1066          | 0.1078 |
| 0.4691        | 47.87 | 27000 | 0.1068          | 0.1085 |
| 0.4457        | 48.76 | 27500 | 0.1066          | 0.1078 |
| 0.475         | 49.65 | 28000 | 0.1060          | 0.1082 |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1