|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: tun_msa_wav2vec3 |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# tun_msa_wav2vec3 |
|
|
|
This model was trained from scratch on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5827 |
|
- Wer: 0.5757 |
|
- Cer: 0.1836 |
|
|
|
## 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: 1e-06 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 700 |
|
- num_epochs: 50 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
|
| 5.268 | 2.9221 | 900 | 2.2839 | 1.0005 | 0.5919 | |
|
| 2.0283 | 5.8442 | 1800 | 0.9714 | 0.6901 | 0.2320 | |
|
| 1.4551 | 8.7662 | 2700 | 0.7691 | 0.6608 | 0.2154 | |
|
| 1.2854 | 11.6883 | 3600 | 0.7028 | 0.6369 | 0.2057 | |
|
| 1.1381 | 14.6104 | 4500 | 0.6529 | 0.6172 | 0.1991 | |
|
| 1.1017 | 17.5325 | 5400 | 0.6325 | 0.6050 | 0.1952 | |
|
| 1.0674 | 20.4545 | 6300 | 0.6189 | 0.5958 | 0.1914 | |
|
| 0.9982 | 23.3766 | 7200 | 0.6089 | 0.5918 | 0.1895 | |
|
| 0.9585 | 26.2987 | 8100 | 0.5986 | 0.5860 | 0.1877 | |
|
| 0.9073 | 29.2208 | 9000 | 0.5948 | 0.5822 | 0.1866 | |
|
| 0.91 | 32.1429 | 9900 | 0.5915 | 0.5804 | 0.1854 | |
|
| 0.8775 | 35.0649 | 10800 | 0.5885 | 0.5787 | 0.1849 | |
|
| 0.8973 | 37.9870 | 11700 | 0.5877 | 0.5775 | 0.1844 | |
|
| 0.8908 | 40.9091 | 12600 | 0.5857 | 0.5763 | 0.1841 | |
|
| 0.8503 | 43.8312 | 13500 | 0.5831 | 0.5764 | 0.1837 | |
|
| 0.8843 | 46.7532 | 14400 | 0.5831 | 0.5765 | 0.1838 | |
|
| 0.8554 | 49.6753 | 15300 | 0.5827 | 0.5757 | 0.1836 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.19.2 |
|
- Tokenizers 0.19.1 |
|
|
