|
--- |
|
license: apache-2.0 |
|
base_model: facebook/wav2vec2-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: w2v2-base-pretrained_lr1e-4_at0.8_da0.9 |
|
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. --> |
|
|
|
# w2v2-base-pretrained_lr1e-4_at0.8_da0.9 |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.2030 |
|
- Wer: 0.1756 |
|
|
|
## 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: 32 |
|
- 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 |
|
- training_steps: 3500 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 15.3627 | 6.1 | 250 | 3.8053 | 1.0 | |
|
| 3.1688 | 12.2 | 500 | 3.1948 | 1.0 | |
|
| 2.7881 | 18.29 | 750 | 1.6319 | 1.0047 | |
|
| 0.3364 | 24.39 | 1000 | 0.9451 | 0.2704 | |
|
| 0.1193 | 30.49 | 1250 | 1.3362 | 0.2089 | |
|
| 0.0741 | 36.59 | 1500 | 1.8263 | 0.1914 | |
|
| 0.0511 | 42.68 | 1750 | 1.8832 | 0.1747 | |
|
| 0.0369 | 48.78 | 2000 | 1.9675 | 0.1794 | |
|
| 0.0256 | 54.88 | 2250 | 2.2933 | 0.1820 | |
|
| 0.0194 | 60.98 | 2500 | 2.3546 | 0.1875 | |
|
| 0.0151 | 67.07 | 2750 | 2.3144 | 0.1773 | |
|
| 0.0126 | 73.17 | 3000 | 2.1852 | 0.1713 | |
|
| 0.0104 | 79.27 | 3250 | 2.2503 | 0.1756 | |
|
| 0.0095 | 85.37 | 3500 | 2.2030 | 0.1756 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.0 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.14.6 |
|
- Tokenizers 0.14.1 |
|
|
