---
base_model: airesearch/wav2vec2-large-xlsr-53-th
datasets:
- common_voice_17_0
library_name: transformers
license: cc-by-sa-4.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: srtx-demo
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: th
      split: validation
      args: th
    metrics:
    - type: wer
      value: 0.2324334251606979
      name: Wer
---

<!-- 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. -->

# srtx-demo

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_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1418
- Wer: 0.2324
- Cer: 0.0762

## 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: 1000
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 4.1321        | 0.9747 | 1000 | 0.1418          | 0.2324 | 0.0762 |
| 0.3512        | 1.9493 | 2000 | 0.1219          | 0.2229 | 0.0738 |
| 0.303         | 2.9240 | 3000 | 0.1174          | 0.2258 | 0.0741 |
| 0.2701        | 3.8986 | 4000 | 0.1214          | 0.2245 | 0.0740 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1