---
tags:
- generated_from_trainer
datasets:
- evanarlian/common_voice_11_0_id_filtered
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-164m-id
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: evanarlian/common_voice_11_0_id_filtered
      type: evanarlian/common_voice_11_0_id_filtered
    metrics:
    - name: Wer
      type: wer
      value: 0.2990499031454663
---

<!-- 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-xls-r-164m-id

This model is a fine-tuned version of [evanarlian/wav2vec2-xls-r-164m-id](https://huggingface.co/evanarlian/wav2vec2-xls-r-164m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3510
- Wer: 0.2990

## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.089         | 1.84  | 2000  | 0.3205          | 0.3168 |
| 0.0882        | 3.67  | 4000  | 0.3243          | 0.3203 |
| 0.0868        | 5.51  | 6000  | 0.3272          | 0.3183 |
| 0.0926        | 7.35  | 8000  | 0.3365          | 0.3209 |
| 0.0943        | 9.18  | 10000 | 0.3400          | 0.3221 |
| 0.0979        | 11.02 | 12000 | 0.3269          | 0.3192 |
| 0.09          | 12.86 | 14000 | 0.3384          | 0.3164 |
| 0.0877        | 14.69 | 16000 | 0.3284          | 0.3183 |
| 0.0808        | 16.53 | 18000 | 0.3366          | 0.3189 |
| 0.0835        | 18.37 | 20000 | 0.3306          | 0.3156 |
| 0.08          | 20.2  | 22000 | 0.3384          | 0.3133 |
| 0.0806        | 22.04 | 24000 | 0.3307          | 0.3109 |
| 0.0749        | 23.88 | 26000 | 0.3493          | 0.3118 |
| 0.073         | 25.71 | 28000 | 0.3479          | 0.3088 |
| 0.0754        | 27.55 | 30000 | 0.3482          | 0.3109 |
| 0.0697        | 29.38 | 32000 | 0.3515          | 0.3090 |
| 0.07          | 31.22 | 34000 | 0.3532          | 0.3101 |
| 0.0672        | 33.06 | 36000 | 0.3668          | 0.3086 |
| 0.0713        | 34.89 | 38000 | 0.3560          | 0.3048 |
| 0.0637        | 36.73 | 40000 | 0.3522          | 0.3028 |
| 0.0695        | 38.57 | 42000 | 0.3407          | 0.3014 |
| 0.0657        | 40.4  | 44000 | 0.3456          | 0.3025 |
| 0.0598        | 42.24 | 46000 | 0.3498          | 0.3013 |
| 0.059         | 44.08 | 48000 | 0.3563          | 0.3012 |
| 0.0645        | 45.91 | 50000 | 0.3514          | 0.3002 |
| 0.0595        | 47.75 | 52000 | 0.3545          | 0.3000 |
| 0.064         | 49.59 | 54000 | 0.3510          | 0.2990 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2