|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-large-xlsr-53 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-darija |
|
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. --> |
|
|
|
# wav2vec2-darija |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4265 |
|
- Wer: 0.4101 |
|
- Cer: 0.1327 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 8 |
|
- 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: 1000 |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
|
| 10.5265 | 0.49 | 500 | 3.2082 | 1.0 | 0.9971 | |
|
| 2.9629 | 0.98 | 1000 | 2.9619 | 1.0000 | 0.9357 | |
|
| 2.6527 | 1.46 | 1500 | 1.4934 | 1.0974 | 0.4550 | |
|
| 1.2946 | 1.95 | 2000 | 0.6904 | 0.7809 | 0.2566 | |
|
| 0.9059 | 2.44 | 2500 | 0.5906 | 0.7002 | 0.2190 | |
|
| 0.8133 | 2.93 | 3000 | 0.5199 | 0.6382 | 0.2049 | |
|
| 0.7026 | 3.41 | 3500 | 0.4570 | 0.5986 | 0.1919 | |
|
| 0.6572 | 3.9 | 4000 | 0.4238 | 0.5792 | 0.1848 | |
|
| 0.5904 | 4.39 | 4500 | 0.4116 | 0.5637 | 0.1807 | |
|
| 0.5593 | 4.88 | 5000 | 0.3850 | 0.5414 | 0.1734 | |
|
| 0.5084 | 5.37 | 5500 | 0.3951 | 0.5409 | 0.1733 | |
|
| 0.5033 | 5.85 | 6000 | 0.4449 | 0.5176 | 0.1649 | |
|
| 0.4712 | 6.34 | 6500 | 0.5485 | 0.5172 | 0.1658 | |
|
| 0.4459 | 6.83 | 7000 | 0.5259 | 0.5061 | 0.1623 | |
|
| 0.4246 | 7.32 | 7500 | 0.3686 | 0.4991 | 0.1605 | |
|
| 0.4261 | 7.8 | 8000 | 0.3663 | 0.4898 | 0.1589 | |
|
| 0.4078 | 8.29 | 8500 | 0.3740 | 0.4858 | 0.1564 | |
|
| 0.3783 | 8.78 | 9000 | 0.3907 | 0.4824 | 0.1566 | |
|
| 0.3647 | 9.27 | 9500 | 0.3424 | 0.4750 | 0.1525 | |
|
| 0.3527 | 9.76 | 10000 | 0.3444 | 0.4692 | 0.1513 | |
|
| 0.3482 | 10.24 | 10500 | 0.3856 | 0.4692 | 0.1507 | |
|
| 0.3338 | 10.73 | 11000 | 0.3650 | 0.4664 | 0.1512 | |
|
| 0.3198 | 11.22 | 11500 | 0.3516 | 0.4628 | 0.1492 | |
|
| 0.3218 | 11.71 | 12000 | 0.3660 | 0.4644 | 0.1491 | |
|
| 0.3115 | 12.2 | 12500 | 0.3490 | 0.4545 | 0.1475 | |
|
| 0.2977 | 12.68 | 13000 | 0.3555 | 0.4504 | 0.1451 | |
|
| 0.2958 | 13.17 | 13500 | 0.3425 | 0.4571 | 0.1449 | |
|
| 0.278 | 13.66 | 14000 | 0.4035 | 0.4520 | 0.1446 | |
|
| 0.2716 | 14.15 | 14500 | 0.3552 | 0.4492 | 0.1437 | |
|
| 0.2729 | 14.63 | 15000 | 0.3665 | 0.4470 | 0.1432 | |
|
| 0.2691 | 15.12 | 15500 | 0.3700 | 0.4498 | 0.1444 | |
|
| 0.2563 | 15.61 | 16000 | 0.3658 | 0.4423 | 0.1421 | |
|
| 0.2511 | 16.1 | 16500 | 0.4152 | 0.4408 | 0.1425 | |
|
| 0.2348 | 16.59 | 17000 | 0.4889 | 0.4375 | 0.1416 | |
|
| 0.2437 | 17.07 | 17500 | 0.4209 | 0.4382 | 0.1413 | |
|
| 0.2388 | 17.56 | 18000 | 0.6032 | 0.4359 | 0.1408 | |
|
| 0.2235 | 18.05 | 18500 | 0.4831 | 0.4369 | 0.1402 | |
|
| 0.2197 | 18.54 | 19000 | 0.4989 | 0.4345 | 0.1402 | |
|
| 0.2285 | 19.02 | 19500 | 0.5929 | 0.4342 | 0.1393 | |
|
| 0.2224 | 19.51 | 20000 | 0.4098 | 0.4317 | 0.1398 | |
|
| 0.2183 | 20.0 | 20500 | 0.3547 | 0.4254 | 0.1384 | |
|
| 0.2113 | 20.49 | 21000 | 0.3926 | 0.4324 | 0.1385 | |
|
| 0.2125 | 20.98 | 21500 | 0.3982 | 0.4299 | 0.1386 | |
|
| 0.201 | 21.46 | 22000 | 0.3929 | 0.4293 | 0.1389 | |
|
| 0.2002 | 21.95 | 22500 | 0.4047 | 0.4218 | 0.1372 | |
|
| 0.2029 | 22.44 | 23000 | 0.5153 | 0.4235 | 0.1375 | |
|
| 0.195 | 22.93 | 23500 | 0.5601 | 0.4198 | 0.1364 | |
|
| 0.182 | 23.41 | 24000 | 0.4596 | 0.4168 | 0.1355 | |
|
| 0.1889 | 23.9 | 24500 | 0.4165 | 0.4209 | 0.1353 | |
|
| 0.1795 | 24.39 | 25000 | 0.4096 | 0.4185 | 0.1352 | |
|
| 0.1809 | 24.88 | 25500 | 0.4732 | 0.4126 | 0.1341 | |
|
| 0.1762 | 25.37 | 26000 | 0.4324 | 0.4146 | 0.1347 | |
|
| 0.1764 | 25.85 | 26500 | 0.4462 | 0.4160 | 0.1347 | |
|
| 0.1805 | 26.34 | 27000 | 0.3955 | 0.4107 | 0.1333 | |
|
| 0.1733 | 26.83 | 27500 | 0.4182 | 0.4135 | 0.1336 | |
|
| 0.1651 | 27.32 | 28000 | 0.4111 | 0.4104 | 0.1330 | |
|
| 0.1713 | 27.8 | 28500 | 0.4426 | 0.4126 | 0.1332 | |
|
| 0.1766 | 28.29 | 29000 | 0.4426 | 0.4085 | 0.1328 | |
|
| 0.1631 | 28.78 | 29500 | 0.4248 | 0.4083 | 0.1328 | |
|
| 0.1608 | 29.27 | 30000 | 0.4334 | 0.4096 | 0.1327 | |
|
| 0.1688 | 29.76 | 30500 | 0.4265 | 0.4101 | 0.1327 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.34.0 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |
|
|
