| --- |
| license: mit |
| base_model: facebook/w2v-bert-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - common_voice_17_0 |
| metrics: |
| - wer |
| model-index: |
| - name: testing_tensorboard_w_new_access_token |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: common_voice_17_0 |
| type: common_voice_17_0 |
| config: hy-AM |
| split: test |
| args: hy-AM |
| metrics: |
| - name: Wer |
| type: wer |
| value: 1.0 |
| --- |
| |
| <!-- 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. --> |
|
|
| # testing_tensorboard_w_new_access_token |
| |
| This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.1867 |
| - Wer: 1.0 |
| - Cer: 0.9653 |
|
|
| ## 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: 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: constant |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
| | 3.3114 | 0.6154 | 200 | 3.2030 | 1.0 | 1.0 | |
| | 3.1797 | 1.2308 | 400 | 3.1973 | 1.0 | 1.0 | |
| | 3.1791 | 1.8462 | 600 | 3.1899 | 1.0 | 1.0 | |
| | 3.1767 | 2.4615 | 800 | 3.1787 | 1.0 | 1.0 | |
| | 3.1681 | 3.0769 | 1000 | 3.1870 | 1.0 | 0.9987 | |
| | 3.1783 | 3.6923 | 1200 | 3.1996 | 0.9998 | 0.9822 | |
| | 3.167 | 4.3077 | 1400 | 3.1726 | 1.0 | 1.0 | |
| | 3.171 | 4.9231 | 1600 | 3.1743 | 1.0 | 0.9653 | |
| | 3.1654 | 5.5385 | 1800 | 3.1926 | 1.0000 | 0.9487 | |
| | 3.1714 | 6.1538 | 2000 | 3.1700 | 1.0 | 0.9653 | |
| | 3.1638 | 6.7692 | 2200 | 3.1688 | 1.0 | 0.9653 | |
| | 3.164 | 7.3846 | 2400 | 3.1934 | 1.0000 | 0.9487 | |
| | 3.1729 | 8.0 | 2600 | 3.1689 | 1.0 | 0.9653 | |
| | 3.1652 | 8.6154 | 2800 | 3.1660 | 1.0 | 0.9653 | |
| | 3.1569 | 9.2308 | 3000 | 3.1890 | 1.0000 | 0.9487 | |
| | 3.1639 | 9.8462 | 3200 | 3.1867 | 1.0 | 0.9653 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.2 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
