---
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- fleurs
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-Wolof-20-hours-alffa-plus-fleurs-dataset
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: None
      args: wo_sn
    metrics:
    - type: wer
      value: 0.423307335820052
      name: Wer
---

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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/m728q8fb)
[<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/m728q8fb)
# wav2vec2-xls-r-Wolof-20-hours-alffa-plus-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: 1.2175
- Wer: 0.4233
- Cer: 0.1476

## 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 2.7783        | 1.2365  | 400   | 1.5400          | 0.8840 | 0.3321 |
| 0.9198        | 2.4730  | 800   | 0.8296          | 0.6037 | 0.2088 |
| 0.6527        | 3.7094  | 1200  | 0.8048          | 0.5326 | 0.1865 |
| 0.5793        | 4.9459  | 1600  | 0.7323          | 0.5155 | 0.1849 |
| 0.5234        | 6.1824  | 2000  | 0.6832          | 0.5016 | 0.1782 |
| 0.4583        | 7.4189  | 2400  | 0.7613          | 0.4925 | 0.1770 |
| 0.4251        | 8.6553  | 2800  | 0.6324          | 0.4951 | 0.1750 |
| 0.3763        | 9.8918  | 3200  | 0.7067          | 0.4810 | 0.1697 |
| 0.3503        | 11.1283 | 3600  | 0.7494          | 0.5030 | 0.1767 |
| 0.325         | 12.3648 | 4000  | 0.6757          | 0.4997 | 0.1734 |
| 0.288         | 13.6012 | 4400  | 0.6880          | 0.4851 | 0.1679 |
| 0.2673        | 14.8377 | 4800  | 0.6832          | 0.5268 | 0.1930 |
| 0.2485        | 16.0742 | 5200  | 0.6879          | 0.4730 | 0.1726 |
| 0.2229        | 17.3107 | 5600  | 0.7245          | 0.4907 | 0.1727 |
| 0.2088        | 18.5471 | 6000  | 0.7458          | 0.4642 | 0.1626 |
| 0.1903        | 19.7836 | 6400  | 0.7750          | 0.4572 | 0.1610 |
| 0.1767        | 21.0201 | 6800  | 0.8254          | 0.4550 | 0.1606 |
| 0.1613        | 22.2566 | 7200  | 0.7481          | 0.4613 | 0.1600 |
| 0.1554        | 23.4930 | 7600  | 0.8343          | 0.4425 | 0.1573 |
| 0.1429        | 24.7295 | 8000  | 0.8984          | 0.4465 | 0.1590 |
| 0.1384        | 25.9660 | 8400  | 0.7643          | 0.4624 | 0.1649 |
| 0.1295        | 27.2025 | 8800  | 0.8534          | 0.4489 | 0.1568 |
| 0.1178        | 28.4389 | 9200  | 0.9115          | 0.4481 | 0.1587 |
| 0.1146        | 29.6754 | 9600  | 0.8835          | 0.4403 | 0.1565 |
| 0.1033        | 30.9119 | 10000 | 0.9292          | 0.4525 | 0.1575 |
| 0.0957        | 32.1484 | 10400 | 1.0917          | 0.4407 | 0.1526 |
| 0.0944        | 33.3849 | 10800 | 0.9554          | 0.4474 | 0.1573 |
| 0.0878        | 34.6213 | 11200 | 1.0647          | 0.4385 | 0.1531 |
| 0.0828        | 35.8578 | 11600 | 1.0365          | 0.4356 | 0.1512 |
| 0.0821        | 37.0943 | 12000 | 1.0613          | 0.4461 | 0.1571 |
| 0.0736        | 38.3308 | 12400 | 1.0852          | 0.4333 | 0.1521 |
| 0.0701        | 39.5672 | 12800 | 1.0551          | 0.4334 | 0.1518 |
| 0.0677        | 40.8037 | 13200 | 1.1009          | 0.4327 | 0.1500 |
| 0.0645        | 42.0402 | 13600 | 1.1279          | 0.4277 | 0.1490 |
| 0.0607        | 43.2767 | 14000 | 1.1581          | 0.4214 | 0.1483 |
| 0.0593        | 44.5131 | 14400 | 1.2038          | 0.4231 | 0.1473 |
| 0.0557        | 45.7496 | 14800 | 1.1791          | 0.4249 | 0.1525 |
| 0.0554        | 46.9861 | 15200 | 1.2012          | 0.4248 | 0.1484 |
| 0.0543        | 48.2226 | 15600 | 1.2087          | 0.4232 | 0.1471 |
| 0.0525        | 49.4590 | 16000 | 1.2175          | 0.4233 | 0.1476 |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.19.1