|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-large-asr-th |
|
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-large-asr-th |
|
|
|
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: 5.1807 |
|
- Wer: 1.0 |
|
- Cer: 1.0000 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 200 |
|
- training_steps: 2000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:| |
|
| 17.808 | 1.09 | 200 | 25.2775 | 1.0 | 1.0000 | |
|
| 6.2017 | 2.19 | 400 | 7.3944 | 1.0 | 1.0000 | |
|
| 4.858 | 3.28 | 600 | 5.1807 | 1.0 | 1.0000 | |
|
| 3.9172 | 4.37 | 800 | 4.4228 | 1.0 | 1.0000 | |
|
| 3.7413 | 5.46 | 1000 | 4.1509 | 1.0 | 1.0000 | |
|
| 3.7496 | 6.56 | 1200 | 4.0403 | 1.0 | 1.0000 | |
|
| 3.6742 | 7.65 | 1400 | 4.0565 | 1.0 | 1.0000 | |
|
| 3.7246 | 8.74 | 1600 | 3.9468 | 1.0 | 1.0000 | |
|
| 3.6329 | 9.84 | 1800 | 3.9271 | 1.0 | 1.0000 | |
|
| 3.6562 | 10.93 | 2000 | 3.9091 | 1.0 | 1.0000 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.13.2 |
|
|
