|
--- |
|
language: |
|
- lt |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- common_voice |
|
- generated_from_trainer |
|
model-index: |
|
- name: wav2vec2-liepa-1-percent |
|
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-liepa-1-percent |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - LT dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5774 |
|
- Wer: 0.5079 |
|
|
|
## 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 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| No log | 0.23 | 100 | 3.3596 | 1.0 | |
|
| No log | 0.46 | 200 | 2.9280 | 1.0 | |
|
| No log | 0.69 | 300 | 1.5091 | 0.9650 | |
|
| No log | 0.93 | 400 | 0.9943 | 0.9177 | |
|
| 3.1184 | 1.16 | 500 | 0.7590 | 0.7793 | |
|
| 3.1184 | 1.39 | 600 | 0.7336 | 0.7408 | |
|
| 3.1184 | 1.62 | 700 | 0.7040 | 0.7618 | |
|
| 3.1184 | 1.85 | 800 | 0.6815 | 0.7233 | |
|
| 3.1184 | 2.08 | 900 | 0.6457 | 0.6865 | |
|
| 0.7917 | 2.31 | 1000 | 0.5705 | 0.6813 | |
|
| 0.7917 | 2.55 | 1100 | 0.5708 | 0.6620 | |
|
| 0.7917 | 2.78 | 1200 | 0.5888 | 0.6462 | |
|
| 0.7917 | 3.01 | 1300 | 0.6509 | 0.6970 | |
|
| 0.7917 | 3.24 | 1400 | 0.5871 | 0.6462 | |
|
| 0.5909 | 3.47 | 1500 | 0.6199 | 0.6813 | |
|
| 0.5909 | 3.7 | 1600 | 0.6230 | 0.5919 | |
|
| 0.5909 | 3.94 | 1700 | 0.5721 | 0.6427 | |
|
| 0.5909 | 4.17 | 1800 | 0.5331 | 0.5867 | |
|
| 0.5909 | 4.4 | 1900 | 0.5561 | 0.6007 | |
|
| 0.4607 | 4.63 | 2000 | 0.5414 | 0.5849 | |
|
| 0.4607 | 4.86 | 2100 | 0.5390 | 0.5587 | |
|
| 0.4607 | 5.09 | 2200 | 0.5313 | 0.5569 | |
|
| 0.4607 | 5.32 | 2300 | 0.5893 | 0.5797 | |
|
| 0.4607 | 5.56 | 2400 | 0.5507 | 0.5954 | |
|
| 0.3933 | 5.79 | 2500 | 0.5521 | 0.6025 | |
|
| 0.3933 | 6.02 | 2600 | 0.5663 | 0.5989 | |
|
| 0.3933 | 6.25 | 2700 | 0.5636 | 0.5832 | |
|
| 0.3933 | 6.48 | 2800 | 0.5464 | 0.5919 | |
|
| 0.3933 | 6.71 | 2900 | 0.5623 | 0.5832 | |
|
| 0.3367 | 6.94 | 3000 | 0.5324 | 0.5692 | |
|
| 0.3367 | 7.18 | 3100 | 0.5907 | 0.5394 | |
|
| 0.3367 | 7.41 | 3200 | 0.5653 | 0.5814 | |
|
| 0.3367 | 7.64 | 3300 | 0.5707 | 0.5814 | |
|
| 0.3367 | 7.87 | 3400 | 0.5754 | 0.5429 | |
|
| 0.2856 | 8.1 | 3500 | 0.5953 | 0.5569 | |
|
| 0.2856 | 8.33 | 3600 | 0.6275 | 0.5394 | |
|
| 0.2856 | 8.56 | 3700 | 0.6253 | 0.5569 | |
|
| 0.2856 | 8.8 | 3800 | 0.5930 | 0.5429 | |
|
| 0.2856 | 9.03 | 3900 | 0.6082 | 0.5219 | |
|
| 0.2522 | 9.26 | 4000 | 0.6026 | 0.5447 | |
|
| 0.2522 | 9.49 | 4100 | 0.6052 | 0.5271 | |
|
| 0.2522 | 9.72 | 4200 | 0.5871 | 0.5219 | |
|
| 0.2522 | 9.95 | 4300 | 0.5870 | 0.5236 | |
|
| 0.2522 | 10.19 | 4400 | 0.5881 | 0.5131 | |
|
| 0.2167 | 10.42 | 4500 | 0.6122 | 0.5289 | |
|
| 0.2167 | 10.65 | 4600 | 0.6128 | 0.5166 | |
|
| 0.2167 | 10.88 | 4700 | 0.6135 | 0.5377 | |
|
| 0.2167 | 11.11 | 4800 | 0.6055 | 0.5184 | |
|
| 0.2167 | 11.34 | 4900 | 0.6725 | 0.5569 | |
|
| 0.1965 | 11.57 | 5000 | 0.6482 | 0.5429 | |
|
| 0.1965 | 11.81 | 5100 | 0.6037 | 0.5096 | |
|
| 0.1965 | 12.04 | 5200 | 0.5931 | 0.5131 | |
|
| 0.1965 | 12.27 | 5300 | 0.5853 | 0.5114 | |
|
| 0.1965 | 12.5 | 5400 | 0.5798 | 0.5219 | |
|
| 0.172 | 12.73 | 5500 | 0.5775 | 0.5009 | |
|
| 0.172 | 12.96 | 5600 | 0.5782 | 0.5044 | |
|
| 0.172 | 13.19 | 5700 | 0.5804 | 0.5184 | |
|
| 0.172 | 13.43 | 5800 | 0.5977 | 0.5219 | |
|
| 0.172 | 13.66 | 5900 | 0.6069 | 0.5236 | |
|
| 0.1622 | 13.89 | 6000 | 0.5850 | 0.5131 | |
|
| 0.1622 | 14.12 | 6100 | 0.5758 | 0.5096 | |
|
| 0.1622 | 14.35 | 6200 | 0.5752 | 0.5009 | |
|
| 0.1622 | 14.58 | 6300 | 0.5727 | 0.5184 | |
|
| 0.1622 | 14.81 | 6400 | 0.5795 | 0.5044 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.0.dev0 |
|
- Pytorch 1.10.0+cu113 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
