---
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-dogon-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-dogon-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 - DOGON dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1688
- Wer: 0.1320

## 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        | 0.4890  | 100  | 3.3308          | 1.0    |
| No log        | 0.9780  | 200  | 2.6855          | 1.0    |
| No log        | 1.4645  | 300  | 0.4930          | 0.4569 |
| No log        | 1.9535  | 400  | 0.2583          | 0.2946 |
| 2.4539        | 2.4401  | 500  | 0.2015          | 0.2439 |
| 2.4539        | 2.9291  | 600  | 0.1660          | 0.1912 |
| 2.4539        | 3.4156  | 700  | 0.1410          | 0.1676 |
| 2.4539        | 3.9046  | 800  | 0.1337          | 0.1610 |
| 2.4539        | 4.3912  | 900  | 0.1225          | 0.1531 |
| 0.1717        | 4.8802  | 1000 | 0.1195          | 0.1402 |
| 0.1717        | 5.3667  | 1100 | 0.1282          | 0.1479 |
| 0.1717        | 5.8557  | 1200 | 0.1198          | 0.1393 |
| 0.1717        | 6.3423  | 1300 | 0.1141          | 0.1334 |
| 0.1717        | 6.8313  | 1400 | 0.1167          | 0.1285 |
| 0.1059        | 7.3178  | 1500 | 0.1103          | 0.1344 |
| 0.1059        | 7.8068  | 1600 | 0.1121          | 0.1230 |
| 0.1059        | 8.2934  | 1700 | 0.1206          | 0.1343 |
| 0.1059        | 8.7824  | 1800 | 0.1110          | 0.1242 |
| 0.1059        | 9.2689  | 1900 | 0.1125          | 0.1238 |
| 0.073         | 9.7579  | 2000 | 0.1196          | 0.1275 |
| 0.073         | 10.2445 | 2100 | 0.1223          | 0.1204 |
| 0.073         | 10.7335 | 2200 | 0.1195          | 0.1200 |
| 0.073         | 11.2200 | 2300 | 0.1223          | 0.1225 |
| 0.073         | 11.7090 | 2400 | 0.1164          | 0.1204 |
| 0.0574        | 12.1956 | 2500 | 0.1211          | 0.1203 |
| 0.0574        | 12.6846 | 2600 | 0.1259          | 0.1213 |
| 0.0574        | 13.1711 | 2700 | 0.1312          | 0.1285 |
| 0.0574        | 13.6601 | 2800 | 0.1399          | 0.1210 |
| 0.0574        | 14.1467 | 2900 | 0.1486          | 0.1272 |
| 0.0852        | 14.6357 | 3000 | 0.1698          | 0.1280 |


### Framework versions

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