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Wave2Vec2_OV_Vie
This model is a fine-tuned version of facebook/wav2vec2-base on the VIVOS - NA dataset. It achieves the following results on the evaluation set:
- Loss: 3.5894
- Wer: 1.0
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.27 | 100 | 3.9210 | 1.0 |
| No log | 0.55 | 200 | 3.4375 | 1.0 |
| No log | 0.82 | 300 | 3.4356 | 1.0 |
| No log | 1.1 | 400 | 3.4045 | 1.0 |
| 4.1866 | 1.37 | 500 | 3.4694 | 1.0 |
| 4.1866 | 1.65 | 600 | 3.6266 | 1.0 |
| 4.1866 | 1.92 | 700 | 3.5694 | 1.0 |
| 4.1866 | 2.19 | 800 | 3.5733 | 1.0 |
| 4.1866 | 2.47 | 900 | 3.6381 | 1.0 |
| 3.4376 | 2.74 | 1000 | 3.6604 | 1.0 |
| 3.4376 | 3.02 | 1100 | 3.5868 | 1.0 |
| 3.4376 | 3.29 | 1200 | 3.4988 | 1.0 |
| 3.4376 | 3.57 | 1300 | 3.5409 | 1.0 |
| 3.4376 | 3.84 | 1400 | 3.4883 | 1.0 |
| 3.4365 | 4.12 | 1500 | 3.6125 | 1.0 |
| 3.4365 | 4.39 | 1600 | 3.6123 | 1.0 |
| 3.4365 | 4.66 | 1700 | 3.5978 | 1.0 |
| 3.4365 | 4.94 | 1800 | 3.5693 | 1.0 |
| 3.4365 | 5.21 | 1900 | 3.5659 | 1.0 |
| 3.4339 | 5.49 | 2000 | 3.6234 | 1.0 |
| 3.4339 | 5.76 | 2100 | 3.5997 | 1.0 |
| 3.4339 | 6.04 | 2200 | 3.6529 | 1.0 |
| 3.4339 | 6.31 | 2300 | 3.5780 | 1.0 |
| 3.4339 | 6.58 | 2400 | 3.5844 | 1.0 |
| 3.4333 | 6.86 | 2500 | 3.5792 | 1.0 |
| 3.4333 | 7.13 | 2600 | 3.5468 | 1.0 |
| 3.4333 | 7.41 | 2700 | 3.5691 | 1.0 |
| 3.4333 | 7.68 | 2800 | 3.5408 | 1.0 |
| 3.4333 | 7.96 | 2900 | 3.5482 | 1.0 |
| 3.4294 | 8.23 | 3000 | 3.6070 | 1.0 |
| 3.4294 | 8.5 | 3100 | 3.5905 | 1.0 |
| 3.4294 | 8.78 | 3200 | 3.6018 | 1.0 |
| 3.4294 | 9.05 | 3300 | 3.6326 | 1.0 |
| 3.4294 | 9.33 | 3400 | 3.6214 | 1.0 |
| 3.4293 | 9.6 | 3500 | 3.6372 | 1.0 |
| 3.4293 | 9.88 | 3600 | 3.6215 | 1.0 |
| 3.4293 | 10.15 | 3700 | 3.5106 | 1.0 |
| 3.4293 | 10.43 | 3800 | 3.5066 | 1.0 |
| 3.4293 | 10.7 | 3900 | 3.5352 | 1.0 |
| 3.4295 | 10.97 | 4000 | 3.5129 | 1.0 |
| 3.4295 | 11.25 | 4100 | 3.6384 | 1.0 |
| 3.4295 | 11.52 | 4200 | 3.6019 | 1.0 |
| 3.4295 | 11.8 | 4300 | 3.5876 | 1.0 |
| 3.4295 | 12.07 | 4400 | 3.6207 | 1.0 |
| 3.4252 | 12.35 | 4500 | 3.5998 | 1.0 |
| 3.4252 | 12.62 | 4600 | 3.6216 | 1.0 |
| 3.4252 | 12.89 | 4700 | 3.6073 | 1.0 |
| 3.4252 | 13.17 | 4800 | 3.5567 | 1.0 |
| 3.4252 | 13.44 | 4900 | 3.5745 | 1.0 |
| 3.4274 | 13.72 | 5000 | 3.5738 | 1.0 |
| 3.4274 | 13.99 | 5100 | 3.5914 | 1.0 |
| 3.4274 | 14.27 | 5200 | 3.6004 | 1.0 |
| 3.4274 | 14.54 | 5300 | 3.5968 | 1.0 |
| 3.4274 | 14.81 | 5400 | 3.5908 | 1.0 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
