<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
Speech5
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: 2.9659
- Wer: 1
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: 0.01
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
2.9422 | 0.19 | 100 | 2.9742 | 1 |
3.006 | 0.38 | 200 | 2.9864 | 1 |
2.9665 | 0.56 | 300 | 2.9967 | 1 |
2.9411 | 0.75 | 400 | 2.9926 | 1 |
2.9937 | 0.94 | 500 | 3.0086 | 1 |
2.9476 | 1.13 | 600 | 3.0102 | 1 |
3.3385 | 1.31 | 700 | 2.9926 | 1 |
2.9979 | 1.5 | 800 | 2.9922 | 1 |
2.9534 | 1.69 | 900 | 3.0587 | 1 |
2.9544 | 1.88 | 1000 | 2.9880 | 1 |
2.9607 | 2.06 | 1100 | 2.9828 | 1 |
2.9926 | 2.25 | 1200 | 3.0249 | 1 |
2.9731 | 2.44 | 1300 | 2.9755 | 1 |
2.9438 | 2.63 | 1400 | 2.9707 | 1 |
2.9688 | 2.81 | 1500 | 3.0368 | 1 |
2.9335 | 3.0 | 1600 | 2.9695 | 1 |
2.9479 | 3.19 | 1700 | 3.0050 | 1 |
3.0182 | 3.38 | 1800 | 3.2477 | 1 |
2.9549 | 3.56 | 1900 | 2.9778 | 1 |
2.9212 | 3.75 | 2000 | 2.9697 | 1 |
2.9718 | 3.94 | 2100 | 2.9677 | 1 |
2.9288 | 4.13 | 2200 | 3.0418 | 1 |
2.9838 | 4.32 | 2300 | 2.9823 | 1 |
2.9812 | 4.5 | 2400 | 2.9902 | 1 |
2.9766 | 4.69 | 2500 | 2.9719 | 1 |
2.9479 | 4.88 | 2600 | 2.9805 | 1 |
2.9462 | 5.07 | 2700 | 3.1010 | 1 |
2.9998 | 5.25 | 2800 | 3.0566 | 1 |
2.9451 | 5.44 | 2900 | 3.1167 | 1 |
3.0465 | 5.63 | 3000 | 3.1573 | 1 |
2.9672 | 5.82 | 3100 | 2.9748 | 1 |
2.9555 | 6.0 | 3200 | 2.9929 | 1 |
2.9354 | 6.19 | 3300 | 2.9743 | 1 |
2.9281 | 6.38 | 3400 | 2.9746 | 1 |
3.0183 | 6.57 | 3500 | 2.9809 | 1 |
2.9602 | 6.75 | 3600 | 2.9662 | 1 |
2.9344 | 6.94 | 3700 | 3.0192 | 1 |
2.9748 | 7.13 | 3800 | 2.9653 | 1 |
3.0283 | 7.32 | 3900 | 3.0159 | 1 |
2.9367 | 7.5 | 4000 | 2.9780 | 1 |
2.9534 | 7.69 | 4100 | 2.9890 | 1 |
2.9481 | 7.88 | 4200 | 2.9701 | 1 |
2.89 | 8.07 | 4300 | 2.9625 | 1 |
2.9153 | 8.26 | 4400 | 2.9650 | 1 |
2.9648 | 8.44 | 4500 | 2.9726 | 1 |
2.9245 | 8.63 | 4600 | 3.0114 | 1 |
2.9608 | 8.82 | 4700 | 2.9645 | 1 |
2.9074 | 9.01 | 4800 | 2.9613 | 1 |
2.9059 | 9.19 | 4900 | 2.9639 | 1 |
2.9538 | 9.38 | 5000 | 2.9659 | 1 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3