---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: asr_skripsi_colab_common_voice
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: id
      split: test
      args: id
    metrics:
    - name: Wer
      type: wer
      value: 0.36856617647058826
---

<!-- 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. -->

# asr_skripsi_colab_common_voice

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3839
- Wer: 0.3686

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4354        | 3.64  | 400  | 1.9595          | 1.0    |
| 0.7227        | 7.27  | 800  | 0.4532          | 0.5039 |
| 0.3293        | 10.91 | 1200 | 0.4277          | 0.4425 |
| 0.2298        | 14.55 | 1600 | 0.3947          | 0.4182 |
| 0.1789        | 18.18 | 2000 | 0.3960          | 0.4009 |
| 0.1496        | 21.82 | 2400 | 0.3793          | 0.3848 |
| 0.122         | 25.45 | 2800 | 0.3794          | 0.3795 |
| 0.1056        | 29.09 | 3200 | 0.3839          | 0.3686 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2