|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-base-timit-eng |
|
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-base-timit-eng |
|
|
|
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: 0.4391 |
|
- Wer: 0.3836 |
|
|
|
## 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 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 3.8306 | 1.0 | 500 | 2.9588 | 1.0 | |
|
| 2.1928 | 2.01 | 1000 | 1.2215 | 0.9355 | |
|
| 1.1547 | 3.01 | 1500 | 0.9228 | 0.7135 | |
|
| 0.9487 | 4.02 | 2000 | 0.7682 | 0.6513 | |
|
| 0.8163 | 5.02 | 2500 | 0.7154 | 0.6164 | |
|
| 0.6642 | 6.02 | 3000 | 0.6160 | 0.5919 | |
|
| 0.6291 | 7.03 | 3500 | 0.6224 | 0.5485 | |
|
| 0.601 | 8.03 | 4000 | 0.5927 | 0.5371 | |
|
| 0.5443 | 9.04 | 4500 | 0.5757 | 0.5240 | |
|
| 0.4798 | 10.04 | 5000 | 0.5673 | 0.5074 | |
|
| 0.5142 | 11.04 | 5500 | 0.6138 | 0.5131 | |
|
| 0.4044 | 12.05 | 6000 | 0.5899 | 0.5120 | |
|
| 0.4214 | 13.05 | 6500 | 0.5443 | 0.4932 | |
|
| 0.377 | 14.06 | 7000 | 0.6055 | 0.5337 | |
|
| 0.3985 | 15.06 | 7500 | 0.5055 | 0.4812 | |
|
| 0.3609 | 16.06 | 8000 | 0.5764 | 0.4600 | |
|
| 0.299 | 17.07 | 8500 | 0.5524 | 0.4635 | |
|
| 0.2984 | 18.07 | 9000 | 0.5272 | 0.4435 | |
|
| 0.2908 | 19.08 | 9500 | 0.5393 | 0.4446 | |
|
| 0.2714 | 20.08 | 10000 | 0.4548 | 0.4463 | |
|
| 0.2285 | 21.08 | 10500 | 0.5126 | 0.4309 | |
|
| 0.2245 | 22.09 | 11000 | 0.4770 | 0.4309 | |
|
| 0.229 | 23.09 | 11500 | 0.4763 | 0.4150 | |
|
| 0.2032 | 24.1 | 12000 | 0.5009 | 0.4127 | |
|
| 0.2125 | 25.1 | 12500 | 0.4698 | 0.4087 | |
|
| 0.1955 | 26.1 | 13000 | 0.4592 | 0.4001 | |
|
| 0.1841 | 27.11 | 13500 | 0.4517 | 0.3898 | |
|
| 0.164 | 28.11 | 14000 | 0.4628 | 0.3927 | |
|
| 0.1687 | 29.12 | 14500 | 0.4391 | 0.3836 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.25.1 |
|
- Pytorch 1.13.0+cu116 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.13.2 |
|
|
