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microsoft-wavlm-fleurs-ur
This model is a fine-tuned version of microsoft/wavlm-large on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.7294
- Wer: 0.4026
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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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 |
|---|---|---|---|---|
| 3.911 | 0.35 | 100 | 3.7784 | 1.0 |
| 3.0833 | 0.71 | 200 | 3.0964 | 1.0 |
| 3.028 | 1.06 | 300 | 3.0377 | 1.0 |
| 2.5114 | 1.41 | 400 | 2.4941 | 0.9922 |
| 1.0583 | 1.77 | 500 | 1.0753 | 0.7579 |
| 0.715 | 2.12 | 600 | 0.8524 | 0.6410 |
| 0.6779 | 2.47 | 700 | 0.7711 | 0.6063 |
| 0.6123 | 2.83 | 800 | 0.7170 | 0.5706 |
| 0.8183 | 3.18 | 900 | 0.6897 | 0.5368 |
| 0.5195 | 3.53 | 1000 | 0.6586 | 0.5303 |
| 0.4774 | 3.89 | 1100 | 0.6306 | 0.5014 |
| 0.4242 | 4.24 | 1200 | 0.6138 | 0.4817 |
| 0.4549 | 4.59 | 1300 | 0.6027 | 0.4678 |
| 0.2576 | 4.95 | 1400 | 0.5878 | 0.4600 |
| 0.1578 | 5.3 | 1500 | 0.6144 | 0.4585 |
| 0.3556 | 5.65 | 1600 | 0.5884 | 0.4582 |
| 0.2427 | 6.01 | 1700 | 0.6071 | 0.4572 |
| 0.267 | 6.36 | 1800 | 0.6303 | 0.4514 |
| 0.2468 | 6.71 | 1900 | 0.6358 | 0.4495 |
| 0.159 | 7.07 | 2000 | 0.6242 | 0.4312 |
| 0.1527 | 7.42 | 2100 | 0.6372 | 0.4400 |
| 0.1401 | 7.77 | 2200 | 0.6252 | 0.4292 |
| 0.1211 | 8.13 | 2300 | 0.6358 | 0.4251 |
| 0.1022 | 8.48 | 2400 | 0.6529 | 0.4356 |
| 0.0818 | 8.83 | 2500 | 0.6773 | 0.4200 |
| 0.0918 | 9.19 | 2600 | 0.6879 | 0.4267 |
| 0.119 | 9.54 | 2700 | 0.6948 | 0.4254 |
| 0.1615 | 9.89 | 2800 | 0.6920 | 0.4259 |
| 0.0953 | 10.25 | 2900 | 0.7019 | 0.4218 |
| 0.1008 | 10.6 | 3000 | 0.6933 | 0.4133 |
| 0.0729 | 10.95 | 3100 | 0.6950 | 0.4164 |
| 0.0636 | 11.31 | 3200 | 0.7151 | 0.4121 |
| 0.0395 | 11.66 | 3300 | 0.7053 | 0.4098 |
| 0.0391 | 12.01 | 3400 | 0.7081 | 0.3984 |
| 0.0507 | 12.37 | 3500 | 0.7012 | 0.4111 |
| 0.0598 | 12.72 | 3600 | 0.7169 | 0.4035 |
| 0.0515 | 13.07 | 3700 | 0.7358 | 0.4102 |
| 0.0429 | 13.43 | 3800 | 0.7236 | 0.4013 |
| 0.0398 | 13.78 | 3900 | 0.7404 | 0.4026 |
| 0.0946 | 14.13 | 4000 | 0.7285 | 0.4029 |
| 0.0428 | 14.49 | 4100 | 0.7271 | 0.3991 |
| 0.0329 | 14.84 | 4200 | 0.7294 | 0.4026 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
