---
license: apache-2.0
base_model: Akashpb13/Swahili_xlsr
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: ssw_finetune
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: ssw
      split: test
      args: ssw
    metrics:
    - name: Wer
      type: wer
      value: 42.14876033057851
---

<!-- 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. -->

# ssw_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: 0.4301
- Wer: 42.1488

## 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      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 22.1208       | 0.8333  | 10   | 25.1031         | 100.5510 |
| 12.838        | 1.6667  | 20   | 10.4898         | 100.0    |
| 4.2236        | 2.5     | 30   | 3.9356          | 100.0    |
| 3.4491        | 3.3333  | 40   | 3.4590          | 100.0    |
| 3.2593        | 4.1667  | 50   | 3.3211          | 100.0    |
| 3.1611        | 5.0     | 60   | 3.1737          | 100.0    |
| 3.1157        | 5.8333  | 70   | 3.1089          | 100.0    |
| 3.0472        | 6.6667  | 80   | 3.0868          | 100.0    |
| 3.0291        | 7.5     | 90   | 3.0445          | 100.0    |
| 2.9996        | 8.3333  | 100  | 3.0058          | 100.0    |
| 2.9187        | 9.1667  | 110  | 2.9600          | 100.0    |
| 2.7708        | 10.0    | 120  | 2.7274          | 100.0    |
| 2.5396        | 10.8333 | 130  | 2.4602          | 100.0    |
| 2.0911        | 11.6667 | 140  | 1.8863          | 100.0    |
| 1.4477        | 12.5    | 150  | 1.2924          | 95.8678  |
| 1.042         | 13.3333 | 160  | 0.9620          | 80.1653  |
| 0.8089        | 14.1667 | 170  | 0.7520          | 67.4931  |
| 0.6621        | 15.0    | 180  | 0.6530          | 53.7190  |
| 0.5476        | 15.8333 | 190  | 0.5838          | 50.6887  |
| 0.4866        | 16.6667 | 200  | 0.5662          | 50.4132  |
| 0.4296        | 17.5    | 210  | 0.5303          | 49.5868  |
| 0.3977        | 18.3333 | 220  | 0.5121          | 47.9339  |
| 0.392         | 19.1667 | 230  | 0.4895          | 47.3829  |
| 0.346         | 20.0    | 240  | 0.4825          | 44.3526  |
| 0.3226        | 20.8333 | 250  | 0.4628          | 45.1791  |
| 0.3145        | 21.6667 | 260  | 0.4662          | 45.1791  |
| 0.2948        | 22.5    | 270  | 0.4492          | 41.8733  |
| 0.2857        | 23.3333 | 280  | 0.4484          | 43.2507  |
| 0.2571        | 24.1667 | 290  | 0.4511          | 43.2507  |
| 0.2706        | 25.0    | 300  | 0.4382          | 41.8733  |
| 0.2404        | 25.8333 | 310  | 0.4528          | 42.1488  |
| 0.2498        | 26.6667 | 320  | 0.4428          | 41.5978  |
| 0.2381        | 27.5    | 330  | 0.4377          | 40.2204  |
| 0.2142        | 28.3333 | 340  | 0.4300          | 41.0468  |
| 0.2236        | 29.1667 | 350  | 0.4305          | 42.1488  |
| 0.2249        | 30.0    | 360  | 0.4253          | 41.0468  |
| 0.209         | 30.8333 | 370  | 0.4272          | 42.9752  |
| 0.2071        | 31.6667 | 380  | 0.4363          | 43.8017  |
| 0.2209        | 32.5    | 390  | 0.4328          | 44.6281  |
| 0.2012        | 33.3333 | 400  | 0.4351          | 44.0771  |
| 0.1895        | 34.1667 | 410  | 0.4362          | 43.8017  |
| 0.1921        | 35.0    | 420  | 0.4383          | 45.1791  |
| 0.1805        | 35.8333 | 430  | 0.4381          | 45.1791  |
| 0.1963        | 36.6667 | 440  | 0.4331          | 41.3223  |
| 0.1829        | 37.5    | 450  | 0.4301          | 41.5978  |
| 0.1927        | 38.3333 | 460  | 0.4290          | 41.8733  |
| 0.1779        | 39.1667 | 470  | 0.4289          | 42.4242  |
| 0.1892        | 40.0    | 480  | 0.4302          | 42.1488  |
| 0.2025        | 40.8333 | 490  | 0.4300          | 42.4242  |
| 0.2105        | 41.6667 | 500  | 0.4301          | 42.1488  |


### Framework versions

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