---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-Wolof-10-hours-Google-Fleurs-dataset
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: None
      args: wo_sn
    metrics:
    - name: Wer
      type: wer
      value: 0.442296823782073
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/1lxkt8t0)
# wav2vec2-xls-r-Wolof-10-hours-Google-Fleurs-dataset

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2082
- Wer: 0.4423
- Cer: 0.1524

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.6995        | 2.6144  | 200  | 3.0428          | 1.0    | 1.0    |
| 3.0391        | 5.2288  | 400  | 3.0117          | 1.0    | 1.0    |
| 3.0035        | 7.8431  | 600  | 2.9794          | 1.0    | 1.0    |
| 2.0946        | 10.4575 | 800  | 0.9827          | 0.7357 | 0.2560 |
| 0.8407        | 13.0719 | 1000 | 0.7398          | 0.5189 | 0.1848 |
| 0.5774        | 15.6863 | 1200 | 0.7214          | 0.4926 | 0.1745 |
| 0.4229        | 18.3007 | 1400 | 0.6996          | 0.4852 | 0.1707 |
| 0.3332        | 20.9150 | 1600 | 0.7950          | 0.4878 | 0.1708 |
| 0.2488        | 23.5294 | 1800 | 0.8972          | 0.4645 | 0.1624 |
| 0.2043        | 26.1438 | 2000 | 0.9122          | 0.4576 | 0.1609 |
| 0.1699        | 28.7582 | 2200 | 1.0064          | 0.4777 | 0.1672 |
| 0.1472        | 31.3725 | 2400 | 1.0141          | 0.4554 | 0.1581 |
| 0.1251        | 33.9869 | 2600 | 1.0362          | 0.4553 | 0.1580 |
| 0.1152        | 36.6013 | 2800 | 1.1312          | 0.4490 | 0.1554 |
| 0.0986        | 39.2157 | 3000 | 1.1552          | 0.4499 | 0.1555 |
| 0.0905        | 41.8301 | 3200 | 1.1811          | 0.4463 | 0.1547 |
| 0.0879        | 44.4444 | 3400 | 1.1849          | 0.4513 | 0.1551 |
| 0.0793        | 47.0588 | 3600 | 1.2074          | 0.4422 | 0.1527 |
| 0.0802        | 49.6732 | 3800 | 1.2082          | 0.4423 | 0.1524 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.19.1