<!-- 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_finetuned_on_grid
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3047
- Wer: 0.7993
- Cer: 0.5300
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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- 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: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
6.8952 | 1.43 | 500 | 0.4457 | 0.7814 | 0.2382 |
0.8573 | 2.86 | 1000 | 0.3487 | 0.7846 | 0.4142 |
0.6617 | 4.29 | 1500 | 0.3471 | 0.7759 | 0.4080 |
0.5865 | 5.71 | 2000 | 0.3279 | 0.8131 | 0.5024 |
0.5855 | 7.14 | 2500 | 0.3384 | 0.7841 | 0.4356 |
0.554 | 8.57 | 3000 | 0.3230 | 0.7949 | 0.4626 |
0.5527 | 10.0 | 3500 | 0.3244 | 0.7706 | 0.4572 |
0.5224 | 11.43 | 4000 | 0.3197 | 0.7878 | 0.4891 |
0.5112 | 12.86 | 4500 | 0.3180 | 0.7939 | 0.4989 |
0.5037 | 14.29 | 5000 | 0.3234 | 0.7650 | 0.4987 |
0.5115 | 15.71 | 5500 | 0.3153 | 0.7712 | 0.4899 |
0.498 | 17.14 | 6000 | 0.3168 | 0.7649 | 0.4728 |
0.5031 | 18.57 | 6500 | 0.3138 | 0.7619 | 0.5011 |
0.4936 | 20.0 | 7000 | 0.3157 | 0.7575 | 0.5112 |
0.4935 | 21.43 | 7500 | 0.3140 | 0.7583 | 0.4985 |
0.495 | 22.86 | 8000 | 0.3080 | 0.7672 | 0.5257 |
0.4916 | 24.29 | 8500 | 0.3075 | 0.7782 | 0.5509 |
0.4897 | 25.71 | 9000 | 0.3053 | 0.7858 | 0.5241 |
0.4845 | 27.14 | 9500 | 0.3042 | 0.8001 | 0.5380 |
0.4894 | 28.57 | 10000 | 0.3038 | 0.8018 | 0.5462 |
0.4823 | 30.0 | 10500 | 0.3047 | 0.7993 | 0.5300 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3