---
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: 0.4928097345132743
      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: 0.4972
- Wer: 0.4928

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7378        | 0.9   | 500  | 2.9498          | 1.0    |
| 2.91          | 1.8   | 1000 | 2.8716          | 1.0    |
| 2.683         | 2.7   | 1500 | 1.9348          | 1.0    |
| 1.5179        | 3.6   | 2000 | 0.8042          | 0.6992 |
| 1.014         | 4.5   | 2500 | 0.6370          | 0.5932 |
| 0.87          | 5.4   | 3000 | 0.5648          | 0.5443 |
| 0.795         | 6.29  | 3500 | 0.5328          | 0.5177 |
| 0.742         | 7.19  | 4000 | 0.5148          | 0.5016 |
| 0.701         | 8.09  | 4500 | 0.4969          | 0.4943 |
| 0.7002        | 8.99  | 5000 | 0.4972          | 0.4928 |


### Framework versions

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