wav2vec2-xls-r-300m-gn-cv8-4
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: 1.5805
- Wer: 0.7545
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
- 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: 100
- training_steps: 13000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 9.2216 | 16.65 | 300 | 3.2771 | 1.0 |
| 3.1804 | 33.32 | 600 | 2.2869 | 1.0 |
| 1.5856 | 49.97 | 900 | 0.9573 | 0.8772 |
| 1.0299 | 66.65 | 1200 | 0.9044 | 0.8082 |
| 0.8916 | 83.32 | 1500 | 0.9478 | 0.8056 |
| 0.8451 | 99.97 | 1800 | 0.8814 | 0.8107 |
| 0.7649 | 116.65 | 2100 | 0.9897 | 0.7826 |
| 0.7185 | 133.32 | 2400 | 0.9988 | 0.7621 |
| 0.6595 | 149.97 | 2700 | 1.0607 | 0.7749 |
| 0.6211 | 166.65 | 3000 | 1.1826 | 0.7877 |
| 0.59 | 183.32 | 3300 | 1.1060 | 0.7826 |
| 0.5383 | 199.97 | 3600 | 1.1826 | 0.7852 |
| 0.5205 | 216.65 | 3900 | 1.2148 | 0.8261 |
| 0.4786 | 233.32 | 4200 | 1.2710 | 0.7928 |
| 0.4482 | 249.97 | 4500 | 1.1943 | 0.7980 |
| 0.4149 | 266.65 | 4800 | 1.2449 | 0.8031 |
| 0.3904 | 283.32 | 5100 | 1.3100 | 0.7928 |
| 0.3619 | 299.97 | 5400 | 1.3125 | 0.7596 |
| 0.3496 | 316.65 | 5700 | 1.3699 | 0.7877 |
| 0.3277 | 333.32 | 6000 | 1.4344 | 0.8031 |
| 0.2958 | 349.97 | 6300 | 1.4093 | 0.7980 |
| 0.2883 | 366.65 | 6600 | 1.3296 | 0.7570 |
| 0.2598 | 383.32 | 6900 | 1.4026 | 0.7980 |
| 0.2564 | 399.97 | 7200 | 1.4847 | 0.8031 |
| 0.2408 | 416.65 | 7500 | 1.4896 | 0.8107 |
| 0.2266 | 433.32 | 7800 | 1.4232 | 0.7698 |
| 0.224 | 449.97 | 8100 | 1.5560 | 0.7903 |
| 0.2038 | 466.65 | 8400 | 1.5355 | 0.7724 |
| 0.1948 | 483.32 | 8700 | 1.4624 | 0.7621 |
| 0.1995 | 499.97 | 9000 | 1.5808 | 0.7724 |
| 0.1864 | 516.65 | 9300 | 1.5653 | 0.7698 |
| 0.18 | 533.32 | 9600 | 1.4868 | 0.7494 |
| 0.1689 | 549.97 | 9900 | 1.5379 | 0.7749 |
| 0.1624 | 566.65 | 10200 | 1.5936 | 0.7749 |
| 0.1537 | 583.32 | 10500 | 1.6436 | 0.7801 |
| 0.1455 | 599.97 | 10800 | 1.6401 | 0.7673 |
| 0.1437 | 616.65 | 11100 | 1.6069 | 0.7673 |
| 0.1452 | 633.32 | 11400 | 1.6041 | 0.7519 |
| 0.139 | 649.97 | 11700 | 1.5758 | 0.7545 |
| 0.1299 | 666.65 | 12000 | 1.5559 | 0.7545 |
| 0.127 | 683.32 | 12300 | 1.5776 | 0.7596 |
| 0.1264 | 699.97 | 12600 | 1.5790 | 0.7519 |
| 0.1209 | 716.65 | 12900 | 1.5805 | 0.7545 |
Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0