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---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-thai
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: th
split: validation
args: th
metrics:
- name: Wer
type: wer
value: 0.7430683918669131
---
<!-- 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-xlsr-53-thai
This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3576
- Wer: 0.7431
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.7312 | 3.33 | 100 | 3.3592 | 1.0 |
| 3.3687 | 6.67 | 200 | 3.2175 | 1.0 |
| 2.4527 | 10.0 | 300 | 2.2648 | 0.7911 |
| 1.0505 | 13.33 | 400 | 2.2322 | 0.7659 |
| 0.7725 | 16.67 | 500 | 2.2775 | 0.7505 |
| 0.6289 | 20.0 | 600 | 2.3209 | 0.7498 |
| 0.543 | 23.33 | 700 | 2.4494 | 0.7572 |
| 0.4991 | 26.67 | 800 | 2.5798 | 0.7597 |
| 0.4492 | 30.0 | 900 | 2.5685 | 0.7461 |
| 0.3737 | 33.33 | 1000 | 2.6186 | 0.7486 |
| 0.3358 | 36.67 | 1100 | 2.7781 | 0.7480 |
| 0.3247 | 40.0 | 1200 | 2.8999 | 0.7535 |
| 0.2963 | 43.33 | 1300 | 2.8668 | 0.7388 |
| 0.2825 | 46.67 | 1400 | 2.8983 | 0.7449 |
| 0.2651 | 50.0 | 1500 | 2.9699 | 0.7461 |
| 0.2597 | 53.33 | 1600 | 2.9930 | 0.7314 |
| 0.2629 | 56.67 | 1700 | 2.9852 | 0.7406 |
| 0.2406 | 60.0 | 1800 | 3.0552 | 0.7474 |
| 0.2293 | 63.33 | 1900 | 3.1058 | 0.7344 |
| 0.2193 | 66.67 | 2000 | 3.1594 | 0.7406 |
| 0.2174 | 70.0 | 2100 | 3.2351 | 0.7369 |
| 0.2127 | 73.33 | 2200 | 3.2696 | 0.7388 |
| 0.2061 | 76.67 | 2300 | 3.2954 | 0.7566 |
| 0.1947 | 80.0 | 2400 | 3.2878 | 0.7529 |
| 0.199 | 83.33 | 2500 | 3.3233 | 0.7486 |
| 0.1961 | 86.67 | 2600 | 3.3136 | 0.7437 |
| 0.1928 | 90.0 | 2700 | 3.3240 | 0.7406 |
| 0.1875 | 93.33 | 2800 | 3.3479 | 0.7425 |
| 0.1852 | 96.67 | 2900 | 3.3681 | 0.7425 |
| 0.1814 | 100.0 | 3000 | 3.3576 | 0.7431 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
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