---
license: apache-2.0
base_model: Akashpb13/Swahili_xlsr
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: xho_finetune
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: xho
      split: test
      args: xho
    metrics:
    - name: Wer
      type: wer
      value: 53.510895883777245
---

<!-- 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. -->

# xho_finetune

This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5370
- Wer: 53.5109

## 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: 9.6e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 25.5184       | 0.7692  | 10   | 24.2275         | 100.0   |
| 14.5363       | 1.5385  | 20   | 9.8357          | 100.0   |
| 4.5811        | 2.3077  | 30   | 3.8367          | 100.0   |
| 3.4822        | 3.0769  | 40   | 3.3922          | 100.0   |
| 3.2732        | 3.8462  | 50   | 3.2398          | 100.0   |
| 3.1796        | 4.6154  | 60   | 3.1705          | 100.0   |
| 3.1504        | 5.3846  | 70   | 3.1419          | 100.0   |
| 3.1119        | 6.1538  | 80   | 3.1084          | 100.0   |
| 3.0789        | 6.9231  | 90   | 3.0735          | 100.0   |
| 3.0619        | 7.6923  | 100  | 3.0590          | 100.0   |
| 3.0298        | 8.4615  | 110  | 3.0247          | 100.0   |
| 2.9933        | 9.2308  | 120  | 2.9716          | 100.0   |
| 2.9079        | 10.0    | 130  | 2.8647          | 100.0   |
| 2.8414        | 10.7692 | 140  | 2.7931          | 100.0   |
| 2.6939        | 11.5385 | 150  | 2.5932          | 100.0   |
| 2.3274        | 12.3077 | 160  | 2.1000          | 99.7579 |
| 1.7068        | 13.0769 | 170  | 1.4580          | 93.4625 |
| 1.206         | 13.8462 | 180  | 1.1027          | 83.0508 |
| 0.9587        | 14.6154 | 190  | 0.9152          | 79.4189 |
| 0.7806        | 15.3846 | 200  | 0.8122          | 69.7337 |
| 0.7118        | 16.1538 | 210  | 0.7445          | 69.0073 |
| 0.6814        | 16.9231 | 220  | 0.6945          | 62.9540 |
| 0.5709        | 17.6923 | 230  | 0.6787          | 67.5545 |
| 0.5653        | 18.4615 | 240  | 0.6758          | 62.2276 |
| 0.5437        | 19.2308 | 250  | 0.6511          | 60.7748 |
| 0.5092        | 20.0    | 260  | 0.6237          | 62.7119 |
| 0.4239        | 20.7692 | 270  | 0.6000          | 61.5012 |
| 0.4355        | 21.5385 | 280  | 0.5899          | 59.8063 |
| 0.4456        | 22.3077 | 290  | 0.5960          | 59.3220 |
| 0.3986        | 23.0769 | 300  | 0.5764          | 56.6586 |
| 0.3856        | 23.8462 | 310  | 0.5801          | 55.9322 |
| 0.3607        | 24.6154 | 320  | 0.5682          | 57.6271 |
| 0.358         | 25.3846 | 330  | 0.5675          | 55.9322 |
| 0.3452        | 26.1538 | 340  | 0.5630          | 57.8692 |
| 0.3289        | 26.9231 | 350  | 0.5515          | 57.8692 |
| 0.353         | 27.6923 | 360  | 0.5621          | 57.3850 |
| 0.2907        | 28.4615 | 370  | 0.5486          | 55.2058 |
| 0.3237        | 29.2308 | 380  | 0.5445          | 54.4794 |
| 0.3202        | 30.0    | 390  | 0.5384          | 52.7845 |
| 0.2918        | 30.7692 | 400  | 0.5370          | 55.6901 |
| 0.3106        | 31.5385 | 410  | 0.5422          | 53.7530 |
| 0.3105        | 32.3077 | 420  | 0.5438          | 55.2058 |
| 0.2835        | 33.0769 | 430  | 0.5437          | 55.9322 |
| 0.2966        | 33.8462 | 440  | 0.5416          | 54.7215 |
| 0.2719        | 34.6154 | 450  | 0.5394          | 54.2373 |
| 0.2859        | 35.3846 | 460  | 0.5384          | 53.7530 |
| 0.29          | 36.1538 | 470  | 0.5379          | 53.2688 |
| 0.2879        | 36.9231 | 480  | 0.5372          | 53.5109 |
| 0.2871        | 37.6923 | 490  | 0.5370          | 53.5109 |
| 0.3019        | 38.4615 | 500  | 0.5370          | 53.5109 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1