<!-- 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. -->
wav2vec2-xls-r-300m-asr_xh-run2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4952
- Wer: 0.5546
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: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 14.5246 | 0.64 | 50 | 8.0044 | 1.0 |
| 5.7225 | 1.27 | 100 | 4.2174 | 1.0 |
| 3.7885 | 1.91 | 150 | 3.2739 | 1.0 |
| 3.1939 | 2.55 | 200 | 3.0574 | 1.0 |
| 3.0792 | 3.18 | 250 | 3.0012 | 1.0 |
| 2.8734 | 3.82 | 300 | 2.4681 | 1.0 |
| 1.735 | 4.46 | 350 | 0.9881 | 0.9242 |
| 0.933 | 5.1 | 400 | 0.6460 | 0.7483 |
| 0.6435 | 5.73 | 450 | 0.6392 | 0.7668 |
| 0.5548 | 6.37 | 500 | 0.5045 | 0.6725 |
| 0.4812 | 7.01 | 550 | 0.5334 | 0.6667 |
| 0.3797 | 7.64 | 600 | 0.4583 | 0.6135 |
| 0.3542 | 8.28 | 650 | 0.4543 | 0.5811 |
| 0.2959 | 8.92 | 700 | 0.5105 | 0.6466 |
| 0.2686 | 9.55 | 750 | 0.4518 | 0.5948 |
| 0.22 | 10.19 | 800 | 0.4408 | 0.5870 |
| 0.2155 | 10.83 | 850 | 0.4228 | 0.5513 |
| 0.1755 | 11.46 | 900 | 0.4862 | 0.5708 |
| 0.1777 | 12.1 | 950 | 0.4304 | 0.5212 |
| 0.1627 | 12.74 | 1000 | 0.4358 | 0.5445 |
| 0.1444 | 13.38 | 1050 | 0.4418 | 0.5157 |
| 0.1364 | 14.01 | 1100 | 0.4711 | 0.5348 |
| 0.1213 | 14.65 | 1150 | 0.4952 | 0.5546 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
