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wav2vec-base-All
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0545
- Wer: 0.8861
- Cer: 0.5014
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: 8
- 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: 1000
- num_epochs: 120
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| No log | 3.33 | 500 | 4.0654 | 1.0 | 0.9823 |
| No log | 6.67 | 1000 | 3.4532 | 1.0 | 0.9823 |
| No log | 10.0 | 1500 | 3.0707 | 0.9992 | 0.9781 |
| No log | 13.33 | 2000 | 2.7335 | 1.0017 | 0.9027 |
| No log | 16.67 | 2500 | 2.5896 | 1.0690 | 0.7302 |
| No log | 20.0 | 3000 | 2.3315 | 1.0690 | 0.6677 |
| No log | 23.33 | 3500 | 2.2217 | 1.0150 | 0.5966 |
| No log | 26.67 | 4000 | 2.3802 | 1.0549 | 0.5948 |
| No log | 30.0 | 4500 | 2.2208 | 0.9975 | 0.5681 |
| 2.4224 | 33.33 | 5000 | 2.2687 | 0.9800 | 0.5537 |
| 2.4224 | 36.67 | 5500 | 2.3169 | 0.9476 | 0.5493 |
| 2.4224 | 40.0 | 6000 | 2.5196 | 0.9900 | 0.5509 |
| 2.4224 | 43.33 | 6500 | 2.4816 | 0.9501 | 0.5272 |
| 2.4224 | 46.67 | 7000 | 2.4894 | 0.9485 | 0.5276 |
| 2.4224 | 50.0 | 7500 | 2.4555 | 0.9418 | 0.5305 |
| 2.4224 | 53.33 | 8000 | 2.7326 | 0.9559 | 0.5255 |
| 2.4224 | 56.67 | 8500 | 2.5514 | 0.9227 | 0.5209 |
| 2.4224 | 60.0 | 9000 | 2.9135 | 0.9717 | 0.5455 |
| 2.4224 | 63.33 | 9500 | 3.0465 | 0.8346 | 0.5002 |
| 0.8569 | 66.67 | 10000 | 2.8177 | 0.9302 | 0.5216 |
| 0.8569 | 70.0 | 10500 | 2.9908 | 0.9310 | 0.5128 |
| 0.8569 | 73.33 | 11000 | 3.1752 | 0.9235 | 0.5284 |
| 0.8569 | 76.67 | 11500 | 2.7412 | 0.8886 | 0.5 |
| 0.8569 | 80.0 | 12000 | 2.7362 | 0.9127 | 0.5040 |
| 0.8569 | 83.33 | 12500 | 2.9636 | 0.9152 | 0.5093 |
| 0.8569 | 86.67 | 13000 | 3.0139 | 0.9011 | 0.5097 |
| 0.8569 | 90.0 | 13500 | 2.8325 | 0.8853 | 0.5032 |
| 0.8569 | 93.33 | 14000 | 3.0383 | 0.8845 | 0.5056 |
| 0.8569 | 96.67 | 14500 | 2.7931 | 0.8795 | 0.4965 |
| 0.3881 | 100.0 | 15000 | 2.8972 | 0.8928 | 0.5012 |
| 0.3881 | 103.33 | 15500 | 2.7780 | 0.8736 | 0.4947 |
| 0.3881 | 106.67 | 16000 | 3.1081 | 0.9036 | 0.5109 |
| 0.3881 | 110.0 | 16500 | 3.0078 | 0.8928 | 0.5032 |
| 0.3881 | 113.33 | 17000 | 3.0245 | 0.8886 | 0.5009 |
| 0.3881 | 116.67 | 17500 | 3.0739 | 0.8928 | 0.5065 |
| 0.3881 | 120.0 | 18000 | 3.0545 | 0.8861 | 0.5014 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
