---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- wer
base_model: facebook/wav2vec2-base
model-index:
- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod2
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: xtreme_s
      type: xtreme_s
      config: fleurs.id_id
      split: test
      args: fleurs.id_id
    metrics:
    - type: wer
      value: 1.0
      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-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod2

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8837
- Wer: 1.0

## 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: 0.005
- 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_steps: 200
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.962         | 3.08  | 100  | 2.8983          | 1.0 |
| 2.9085        | 6.15  | 200  | 2.8864          | 1.0 |
| 2.9094        | 9.23  | 300  | 2.9040          | 1.0 |
| 2.8976        | 12.31 | 400  | 2.9628          | 1.0 |
| 2.901         | 15.38 | 500  | 2.8694          | 1.0 |
| 2.8913        | 18.46 | 600  | 2.8954          | 1.0 |
| 2.8918        | 21.54 | 700  | 2.8726          | 1.0 |
| 2.892         | 24.62 | 800  | 2.8865          | 1.0 |
| 2.8856        | 27.69 | 900  | 2.9127          | 1.0 |
| 2.8893        | 30.77 | 1000 | 2.8989          | 1.0 |
| 2.8862        | 33.85 | 1100 | 2.8831          | 1.0 |
| 2.8853        | 36.92 | 1200 | 2.8960          | 1.0 |
| 2.8856        | 40.0  | 1300 | 2.8911          | 1.0 |
| 2.8849        | 43.08 | 1400 | 2.8926          | 1.0 |
| 2.8829        | 46.15 | 1500 | 2.8837          | 1.0 |
| 2.8812        | 49.23 | 1600 | 2.8859          | 1.0 |
| 2.8825        | 52.31 | 1700 | 2.8858          | 1.0 |
| 2.8833        | 55.38 | 1800 | 2.8856          | 1.0 |
| 2.88          | 58.46 | 1900 | 2.8837          | 1.0 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.2+cu121
- Datasets 1.18.3
- Tokenizers 0.15.1