---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- automatic-speech-recognition
- sudoping01/malian-languages-dataset
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-malian-languages-minianka-dataset
  results: []
---

<!-- 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-malian-languages-minianka-dataset

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the SUDOPING01/MALIAN-LANGUAGES-DATASET - MINIANKA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1794
- Wer: 0.1271

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log        | 0.4706  | 100  | 3.8351          | 1.0    |
| No log        | 0.9412  | 200  | 2.8794          | 1.0    |
| No log        | 1.4094  | 300  | 0.6950          | 0.5312 |
| No log        | 1.88    | 400  | 0.3555          | 0.3603 |
| 2.6845        | 2.3482  | 500  | 0.2666          | 0.2772 |
| 2.6845        | 2.8188  | 600  | 0.2253          | 0.2401 |
| 2.6845        | 3.2871  | 700  | 0.2025          | 0.2246 |
| 2.6845        | 3.7576  | 800  | 0.1880          | 0.1979 |
| 2.6845        | 4.2259  | 900  | 0.1853          | 0.1959 |
| 0.2121        | 4.6965  | 1000 | 0.1887          | 0.1909 |
| 0.2121        | 5.1647  | 1100 | 0.1745          | 0.1709 |
| 0.2121        | 5.6353  | 1200 | 0.1602          | 0.1674 |
| 0.2121        | 6.1035  | 1300 | 0.1627          | 0.1588 |
| 0.2121        | 6.5741  | 1400 | 0.1593          | 0.1563 |
| 0.1148        | 7.0424  | 1500 | 0.1660          | 0.1593 |
| 0.1148        | 7.5129  | 1600 | 0.1655          | 0.1551 |
| 0.1148        | 7.9835  | 1700 | 0.1581          | 0.1520 |
| 0.1148        | 8.4518  | 1800 | 0.1771          | 0.1493 |
| 0.1148        | 8.9224  | 1900 | 0.1778          | 0.1482 |
| 0.0723        | 9.3906  | 2000 | 0.1683          | 0.1402 |
| 0.0723        | 9.8612  | 2100 | 0.1676          | 0.1378 |
| 0.0723        | 10.3294 | 2200 | 0.1672          | 0.1365 |
| 0.0723        | 10.8    | 2300 | 0.1646          | 0.1332 |
| 0.0723        | 11.2682 | 2400 | 0.1751          | 0.1337 |
| 0.0504        | 11.7388 | 2500 | 0.1742          | 0.1353 |
| 0.0504        | 12.2071 | 2600 | 0.1809          | 0.1329 |
| 0.0504        | 12.6776 | 2700 | 0.1769          | 0.1298 |
| 0.0504        | 13.1459 | 2800 | 0.1752          | 0.1289 |
| 0.0504        | 13.6165 | 2900 | 0.1782          | 0.1275 |
| 0.0382        | 14.0847 | 3000 | 0.1789          | 0.1289 |
| 0.0382        | 14.5553 | 3100 | 0.1783          | 0.1275 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0