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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-th-main
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: 0.4686162624821683
---
<!-- 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-th-main
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: 1.1340
- Wer: 0.4686
## 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: 30
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2524 | 3.23 | 100 | 3.3222 | 1.0 |
| 3.2913 | 6.45 | 200 | 3.1818 | 1.0 |
| 2.222 | 9.68 | 300 | 1.2497 | 0.5335 |
| 1.1558 | 12.9 | 400 | 1.0792 | 0.5214 |
| 0.934 | 16.13 | 500 | 1.0663 | 0.4986 |
| 0.8023 | 19.35 | 600 | 1.0331 | 0.4893 |
| 0.7041 | 22.58 | 700 | 1.0801 | 0.4800 |
| 0.6576 | 25.81 | 800 | 1.1123 | 0.4886 |
| 0.6061 | 29.03 | 900 | 1.0748 | 0.4829 |
| 0.5649 | 32.26 | 1000 | 1.1187 | 0.4679 |
| 0.5717 | 35.48 | 1100 | 1.1267 | 0.4715 |
| 0.5267 | 38.71 | 1200 | 1.1340 | 0.4686 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
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