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wav2vec2-base-vios-commonvoice-1
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8913
- Wer: 0.3621
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.4706 | 0.55 | 500 | 3.4725 | 1.0 |
| 3.202 | 1.1 | 1000 | 2.7555 | 1.0008 |
| 1.0507 | 1.66 | 1500 | 1.0481 | 0.6196 |
| 0.7325 | 2.21 | 2000 | 0.8120 | 0.4958 |
| 0.599 | 2.76 | 2500 | 0.7035 | 0.4447 |
| 0.5224 | 3.31 | 3000 | 0.6761 | 0.4078 |
| 0.4844 | 3.86 | 3500 | 0.6688 | 0.4011 |
| 0.4234 | 4.42 | 4000 | 0.6080 | 0.3729 |
| 0.4237 | 4.97 | 4500 | 0.5953 | 0.3556 |
| 0.3986 | 5.52 | 5000 | 0.6054 | 0.3478 |
| 0.3554 | 6.07 | 5500 | 0.6193 | 0.3479 |
| 0.3446 | 6.62 | 6000 | 0.5809 | 0.3302 |
| 0.3104 | 7.17 | 6500 | 0.5713 | 0.3283 |
| 0.3166 | 7.73 | 7000 | 0.5593 | 0.3133 |
| 0.2938 | 8.28 | 7500 | 0.5645 | 0.3081 |
| 0.3061 | 8.83 | 8000 | 0.5508 | 0.3020 |
| 0.2986 | 9.38 | 8500 | 0.5462 | 0.3024 |
| 0.2939 | 9.93 | 9000 | 0.5544 | 0.3028 |
| 0.2633 | 10.49 | 9500 | 0.5496 | 0.3024 |
| 0.2683 | 11.04 | 10000 | 0.5439 | 0.2946 |
| 0.2714 | 11.59 | 10500 | 0.5524 | 0.2947 |
| 0.2354 | 12.14 | 11000 | 0.5267 | 0.2918 |
| 0.2488 | 12.69 | 11500 | 0.5728 | 0.2938 |
| 0.2479 | 13.25 | 12000 | 0.5802 | 0.2951 |
| 0.245 | 13.8 | 12500 | 0.5571 | 0.2890 |
| 0.2422 | 14.35 | 13000 | 0.5531 | 0.2871 |
| 0.2369 | 14.9 | 13500 | 0.5453 | 0.2860 |
| 0.2345 | 15.45 | 14000 | 0.5452 | 0.2847 |
| 0.2507 | 16.0 | 14500 | 0.5536 | 0.2884 |
| 0.2454 | 16.56 | 15000 | 0.5577 | 0.2871 |
| 0.2729 | 17.11 | 15500 | 0.6019 | 0.2931 |
| 0.2743 | 17.66 | 16000 | 0.5619 | 0.2905 |
| 0.3031 | 18.21 | 16500 | 0.6401 | 0.3006 |
| 0.315 | 18.76 | 17000 | 0.6044 | 0.2990 |
| 0.4025 | 19.32 | 17500 | 0.6739 | 0.3304 |
| 0.4915 | 19.87 | 18000 | 0.7267 | 0.3472 |
| 0.5539 | 20.42 | 18500 | 0.8078 | 0.3483 |
| 0.7138 | 20.97 | 19000 | 0.9362 | 0.3765 |
| 0.5766 | 21.52 | 19500 | 0.7921 | 0.3392 |
| 0.688 | 22.08 | 20000 | 0.8833 | 0.3693 |
| 0.6964 | 22.63 | 20500 | 0.9137 | 0.3469 |
| 0.7389 | 23.18 | 21000 | 0.9379 | 0.3460 |
| 0.7851 | 23.73 | 21500 | 1.0438 | 0.3653 |
| 0.7619 | 24.28 | 22000 | 0.9313 | 0.3873 |
| 0.7175 | 24.83 | 22500 | 0.8668 | 0.3789 |
| 0.6842 | 25.39 | 23000 | 0.8243 | 0.3761 |
| 0.6941 | 25.94 | 23500 | 0.8557 | 0.3804 |
| 0.7167 | 26.49 | 24000 | 0.8618 | 0.3875 |
| 0.721 | 27.04 | 24500 | 0.8686 | 0.3764 |
| 0.6949 | 27.59 | 25000 | 0.8773 | 0.3690 |
| 0.727 | 28.15 | 25500 | 0.8769 | 0.3666 |
| 0.7363 | 28.7 | 26000 | 0.8867 | 0.3634 |
| 0.7157 | 29.25 | 26500 | 0.8895 | 0.3626 |
| 0.7385 | 29.8 | 27000 | 0.8913 | 0.3621 |
Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
