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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
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