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wav2vec2-large-xls-r-300m-dutch-baseline
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.5107
- Wer: 0.2674
- Cer: 0.0863
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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- 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 | Cer |
---|---|---|---|---|---|
3.655 | 1.31 | 400 | 0.9337 | 0.7332 | 0.2534 |
0.42 | 2.61 | 800 | 0.5018 | 0.4115 | 0.1374 |
0.2267 | 3.92 | 1200 | 0.4776 | 0.3791 | 0.1259 |
0.1624 | 5.23 | 1600 | 0.4807 | 0.3590 | 0.1208 |
0.135 | 6.54 | 2000 | 0.4899 | 0.3417 | 0.1121 |
0.1179 | 7.84 | 2400 | 0.5096 | 0.3445 | 0.1133 |
0.1035 | 9.15 | 2800 | 0.4563 | 0.3455 | 0.1129 |
0.092 | 10.46 | 3200 | 0.5061 | 0.3382 | 0.1127 |
0.0804 | 11.76 | 3600 | 0.4969 | 0.3285 | 0.1088 |
0.0748 | 13.07 | 4000 | 0.5274 | 0.3380 | 0.1114 |
0.0669 | 14.38 | 4400 | 0.5201 | 0.3115 | 0.1028 |
0.0588 | 15.69 | 4800 | 0.5238 | 0.3212 | 0.1054 |
0.0561 | 16.99 | 5200 | 0.5273 | 0.3185 | 0.1044 |
0.0513 | 18.3 | 5600 | 0.5577 | 0.3032 | 0.1010 |
0.0476 | 19.61 | 6000 | 0.5298 | 0.3050 | 0.1008 |
0.0408 | 20.91 | 6400 | 0.5725 | 0.2982 | 0.0984 |
0.0376 | 22.22 | 6800 | 0.5605 | 0.2953 | 0.0966 |
0.0339 | 23.53 | 7200 | 0.5419 | 0.2865 | 0.0938 |
0.0315 | 24.84 | 7600 | 0.5530 | 0.2782 | 0.0915 |
0.0286 | 26.14 | 8000 | 0.5354 | 0.2788 | 0.0917 |
0.0259 | 27.45 | 8400 | 0.5245 | 0.2715 | 0.0878 |
0.0231 | 28.76 | 8800 | 0.5107 | 0.2674 | 0.0863 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu102
- Datasets 2.7.1
- Tokenizers 0.13.2