---
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: 0.39516649755557604
---

<!-- 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: 0.3280
- Wer: 0.3952

## 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: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 25.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2512        | 0.92  | 1000  | 2.9098          | 1.0000 |
| 2.163         | 1.84  | 2000  | 1.4810          | 0.9941 |
| 1.2472        | 2.75  | 3000  | 0.9604          | 0.9196 |
| 1.0166        | 3.67  | 4000  | 0.8240          | 0.8498 |
| 0.8765        | 4.59  | 5000  | 0.6873          | 0.7741 |
| 0.7712        | 5.51  | 6000  | 0.6083          | 0.7111 |
| 0.6892        | 6.43  | 7000  | 0.5546          | 0.6592 |
| 0.6314        | 7.35  | 8000  | 0.5022          | 0.6108 |
| 0.5779        | 8.26  | 9000  | 0.4850          | 0.5825 |
| 0.5245        | 9.18  | 10000 | 0.4665          | 0.5538 |
| 0.4858        | 10.1  | 11000 | 0.4282          | 0.5279 |
| 0.4616        | 11.02 | 12000 | 0.4053          | 0.5082 |
| 0.421         | 11.94 | 13000 | 0.3809          | 0.4935 |
| 0.4064        | 12.86 | 14000 | 0.3706          | 0.4781 |
| 0.3758        | 13.77 | 15000 | 0.3743          | 0.4672 |
| 0.3598        | 14.69 | 16000 | 0.3571          | 0.4521 |
| 0.3441        | 15.61 | 17000 | 0.3455          | 0.4368 |
| 0.3279        | 16.53 | 18000 | 0.3398          | 0.4386 |
| 0.3086        | 17.45 | 19000 | 0.3512          | 0.4284 |
| 0.3013        | 18.37 | 20000 | 0.3321          | 0.4233 |
| 0.2963        | 19.28 | 21000 | 0.3391          | 0.4178 |
| 0.2831        | 20.2  | 22000 | 0.3438          | 0.4114 |
| 0.2801        | 21.12 | 23000 | 0.3336          | 0.4056 |
| 0.2623        | 22.04 | 24000 | 0.3317          | 0.4012 |
| 0.263         | 22.96 | 25000 | 0.3280          | 0.4005 |
| 0.2529        | 23.88 | 26000 | 0.3268          | 0.3951 |
| 0.2492        | 24.79 | 27000 | 0.3280          | 0.3952 |


### Framework versions

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