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---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-uralic-voxpopuli-v2-finnish
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. -->
# wav2vec2-large-uralic-voxpopuli-v2-finnish
This model is a fine-tuned version of [facebook/wav2vec2-large-uralic-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-large-uralic-voxpopuli-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0828
- 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.9421 | 0.17 | 500 | 0.8633 | 0.8870 |
| 0.572 | 0.33 | 1000 | 0.1650 | 0.1829 |
| 0.5149 | 0.5 | 1500 | 0.1416 | 0.1711 |
| 0.4884 | 0.66 | 2000 | 0.1265 | 0.1605 |
| 0.4729 | 0.83 | 2500 | 0.1205 | 0.1485 |
| 0.4723 | 1.0 | 3000 | 0.1108 | 0.1403 |
| 0.443 | 1.16 | 3500 | 0.1175 | 0.1439 |
| 0.4378 | 1.33 | 4000 | 0.1083 | 0.1482 |
| 0.4313 | 1.49 | 4500 | 0.1110 | 0.1398 |
| 0.4182 | 1.66 | 5000 | 0.1024 | 0.1418 |
| 0.3884 | 1.83 | 5500 | 0.1032 | 0.1395 |
| 0.4034 | 1.99 | 6000 | 0.0985 | 0.1318 |
| 0.3735 | 2.16 | 6500 | 0.1008 | 0.1355 |
| 0.4174 | 2.32 | 7000 | 0.0970 | 0.1361 |
| 0.3581 | 2.49 | 7500 | 0.0968 | 0.1297 |
| 0.3783 | 2.66 | 8000 | 0.0881 | 0.1284 |
| 0.3827 | 2.82 | 8500 | 0.0921 | 0.1352 |
| 0.3651 | 2.99 | 9000 | 0.0861 | 0.1298 |
| 0.3684 | 3.15 | 9500 | 0.0844 | 0.1270 |
| 0.3784 | 3.32 | 10000 | 0.0870 | 0.1248 |
| 0.356 | 3.48 | 10500 | 0.0828 | 0.1214 |
| 0.3524 | 3.65 | 11000 | 0.0878 | 0.1218 |
| 0.3879 | 3.82 | 11500 | 0.0874 | 0.1216 |
| 0.3521 | 3.98 | 12000 | 0.0860 | 0.1210 |
| 0.3527 | 4.15 | 12500 | 0.0818 | 0.1184 |
| 0.3529 | 4.31 | 13000 | 0.0787 | 0.1185 |
| 0.3114 | 4.48 | 13500 | 0.0852 | 0.1202 |
| 0.3495 | 4.65 | 14000 | 0.0807 | 0.1187 |
| 0.34 | 4.81 | 14500 | 0.0796 | 0.1162 |
| 0.3646 | 4.98 | 15000 | 0.0782 | 0.1149 |
| 0.3004 | 5.14 | 15500 | 0.0799 | 0.1142 |
| 0.3167 | 5.31 | 16000 | 0.0847 | 0.1123 |
| 0.3249 | 5.48 | 16500 | 0.0837 | 0.1171 |
| 0.3202 | 5.64 | 17000 | 0.0749 | 0.1109 |
| 0.3104 | 5.81 | 17500 | 0.0798 | 0.1093 |
| 0.3039 | 5.97 | 18000 | 0.0810 | 0.1132 |
| 0.3157 | 6.14 | 18500 | 0.0847 | 0.1156 |
| 0.3133 | 6.31 | 19000 | 0.0833 | 0.1140 |
| 0.3203 | 6.47 | 19500 | 0.0838 | 0.1113 |
| 0.3178 | 6.64 | 20000 | 0.0907 | 0.1141 |
| 0.3182 | 6.8 | 20500 | 0.0938 | 0.1143 |
| 0.3 | 6.97 | 21000 | 0.0854 | 0.1133 |
| 0.3151 | 7.14 | 21500 | 0.0859 | 0.1109 |
| 0.2963 | 7.3 | 22000 | 0.0832 | 0.1122 |
| 0.3099 | 7.47 | 22500 | 0.0865 | 0.1103 |
| 0.322 | 7.63 | 23000 | 0.0833 | 0.1105 |
| 0.3064 | 7.8 | 23500 | 0.0865 | 0.1078 |
| 0.2964 | 7.97 | 24000 | 0.0859 | 0.1096 |
| 0.2869 | 8.13 | 24500 | 0.0872 | 0.1100 |
| 0.315 | 8.3 | 25000 | 0.0869 | 0.1099 |
| 0.3003 | 8.46 | 25500 | 0.0878 | 0.1105 |
| 0.2947 | 8.63 | 26000 | 0.0884 | 0.1084 |
| 0.297 | 8.8 | 26500 | 0.0891 | 0.1102 |
| 0.3049 | 8.96 | 27000 | 0.0863 | 0.1081 |
| 0.2957 | 9.13 | 27500 | 0.0846 | 0.1083 |
| 0.2908 | 9.29 | 28000 | 0.0848 | 0.1059 |
| 0.2955 | 9.46 | 28500 | 0.0846 | 0.1085 |
| 0.2991 | 9.62 | 29000 | 0.0839 | 0.1081 |
| 0.3112 | 9.79 | 29500 | 0.0832 | 0.1071 |
| 0.29 | 9.96 | 30000 | 0.0828 | 0.1075 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.11.0
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