---
tags:
- generated_from_trainer
datasets:
- evanarlian/common_voice_11_0_id_filtered
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-113m-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: 0.6403468314731113
---

<!-- 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-113m-id

This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5214
- Wer: 0.6403

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8452        | 0.61  | 1000 | 2.8065          | 1.0    |
| 1.3277        | 1.22  | 2000 | 1.0774          | 0.9330 |
| 1.025         | 1.84  | 3000 | 0.8000          | 0.8474 |
| 0.8497        | 2.45  | 4000 | 0.6812          | 0.7669 |
| 0.7678        | 3.06  | 5000 | 0.6125          | 0.7186 |
| 0.6886        | 3.67  | 6000 | 0.5758          | 0.6812 |
| 0.6318        | 4.29  | 7000 | 0.5420          | 0.6570 |
| 0.6086        | 4.9   | 8000 | 0.5214          | 0.6403 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.1