---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-large-xlsr-53
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-CV-demo-google-colab-Ezra_William_Prod13
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: id
      split: test
      args: id
    metrics:
    - type: wer
      value: 0.5021202064896755
      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. -->

# wav2vec2-xlsr-53-CV-demo-google-colab-Ezra_William_Prod13

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5021
- Wer: 0.5021

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9125        | 0.9   | 500  | 2.9530          | 1.0    |
| 2.9145        | 1.8   | 1000 | 2.8763          | 1.0    |
| 2.7636        | 2.7   | 1500 | 2.1172          | 1.0    |
| 1.6023        | 3.6   | 2000 | 0.8278          | 0.7302 |
| 1.0405        | 4.5   | 2500 | 0.6488          | 0.6256 |
| 0.8858        | 5.4   | 3000 | 0.5819          | 0.5666 |
| 0.8119        | 6.29  | 3500 | 0.5431          | 0.5321 |
| 0.7547        | 7.19  | 4000 | 0.5203          | 0.5145 |
| 0.713         | 8.09  | 4500 | 0.5039          | 0.5037 |
| 0.712         | 8.99  | 5000 | 0.5021          | 0.5021 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2