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wav2vec2-common_voice-tr-demo-dist
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR dataset. It achieves the following results on the evaluation set:
- Loss: 0.3934
- Wer: 0.3305
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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.5459 | 0.23 | 100 | 3.6773 | 1.0 |
| 3.2247 | 0.46 | 200 | 3.1491 | 0.9999 |
| 2.3457 | 0.69 | 300 | 2.4236 | 1.0041 |
| 0.9149 | 0.92 | 400 | 0.9471 | 0.7684 |
| 0.6622 | 1.15 | 500 | 0.7518 | 0.6863 |
| 0.7205 | 1.38 | 600 | 0.6387 | 0.6402 |
| 0.6978 | 1.61 | 700 | 0.5611 | 0.5739 |
| 0.5317 | 1.84 | 800 | 0.5061 | 0.5418 |
| 0.5222 | 2.07 | 900 | 0.4839 | 0.5344 |
| 0.4467 | 2.3 | 1000 | 0.5060 | 0.5339 |
| 0.3196 | 2.53 | 1100 | 0.4619 | 0.5213 |
| 0.276 | 2.76 | 1200 | 0.4595 | 0.5020 |
| 0.3569 | 2.99 | 1300 | 0.4339 | 0.4901 |
| 0.2236 | 3.22 | 1400 | 0.4602 | 0.4887 |
| 0.293 | 3.45 | 1500 | 0.4376 | 0.4639 |
| 0.1677 | 3.68 | 1600 | 0.4371 | 0.4605 |
| 0.1838 | 3.91 | 1700 | 0.4116 | 0.4589 |
| 0.1225 | 4.14 | 1800 | 0.4144 | 0.4495 |
| 0.2301 | 4.37 | 1900 | 0.4250 | 0.4567 |
| 0.1931 | 4.6 | 2000 | 0.4081 | 0.4470 |
| 0.1427 | 4.83 | 2100 | 0.4295 | 0.4482 |
| 0.361 | 5.06 | 2200 | 0.4374 | 0.4445 |
| 0.3272 | 5.29 | 2300 | 0.4088 | 0.4258 |
| 0.3686 | 5.52 | 2400 | 0.4087 | 0.4258 |
| 0.3087 | 5.75 | 2500 | 0.4100 | 0.4371 |
| 0.4637 | 5.98 | 2600 | 0.4038 | 0.4219 |
| 0.1485 | 6.21 | 2700 | 0.4361 | 0.4197 |
| 0.1341 | 6.44 | 2800 | 0.4217 | 0.4132 |
| 0.1185 | 6.67 | 2900 | 0.4244 | 0.4097 |
| 0.1588 | 6.9 | 3000 | 0.4212 | 0.4181 |
| 0.0697 | 7.13 | 3100 | 0.3981 | 0.4073 |
| 0.0491 | 7.36 | 3200 | 0.3992 | 0.4010 |
| 0.088 | 7.59 | 3300 | 0.4206 | 0.4022 |
| 0.0731 | 7.82 | 3400 | 0.3998 | 0.3841 |
| 0.2767 | 8.05 | 3500 | 0.4195 | 0.3829 |
| 0.1725 | 8.28 | 3600 | 0.4167 | 0.3946 |
| 0.1242 | 8.51 | 3700 | 0.4177 | 0.3821 |
| 0.1133 | 8.74 | 3800 | 0.3993 | 0.3802 |
| 0.1952 | 8.97 | 3900 | 0.4132 | 0.3904 |
| 0.1399 | 9.2 | 4000 | 0.4010 | 0.3795 |
| 0.047 | 9.43 | 4100 | 0.4128 | 0.3703 |
| 0.049 | 9.66 | 4200 | 0.4319 | 0.3670 |
| 0.0994 | 9.89 | 4300 | 0.4118 | 0.3631 |
| 0.1209 | 10.11 | 4400 | 0.4296 | 0.3722 |
| 0.0484 | 10.34 | 4500 | 0.4130 | 0.3615 |
| 0.2065 | 10.57 | 4600 | 0.3958 | 0.3668 |
| 0.133 | 10.8 | 4700 | 0.4102 | 0.3679 |
| 0.0622 | 11.03 | 4800 | 0.4137 | 0.3585 |
| 0.0999 | 11.26 | 4900 | 0.4042 | 0.3583 |
| 0.0346 | 11.49 | 5000 | 0.4183 | 0.3573 |
| 0.072 | 11.72 | 5100 | 0.4060 | 0.3530 |
| 0.0365 | 11.95 | 5200 | 0.3968 | 0.3483 |
| 0.0615 | 12.18 | 5300 | 0.3958 | 0.3485 |
| 0.1067 | 12.41 | 5400 | 0.3987 | 0.3453 |
| 0.0253 | 12.64 | 5500 | 0.4182 | 0.3405 |
| 0.0636 | 12.87 | 5600 | 0.4199 | 0.3458 |
| 0.0506 | 13.1 | 5700 | 0.4056 | 0.3412 |
| 0.0944 | 13.33 | 5800 | 0.4061 | 0.3381 |
| 0.1187 | 13.56 | 5900 | 0.4113 | 0.3381 |
| 0.0237 | 13.79 | 6000 | 0.3973 | 0.3343 |
| 0.0166 | 14.02 | 6100 | 0.4001 | 0.3357 |
| 0.1189 | 14.25 | 6200 | 0.3931 | 0.3315 |
| 0.0375 | 14.48 | 6300 | 0.3944 | 0.3329 |
| 0.0537 | 14.71 | 6400 | 0.3953 | 0.3308 |
| 0.045 | 14.94 | 6500 | 0.3933 | 0.3303 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 1.13.3
- Tokenizers 0.11.6
