---
license: apache-2.0
base_model: Akashpb13/Swahili_xlsr
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: xty_finetune
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: xty
      split: test
      args: xty
    metrics:
    - name: Wer
      type: wer
      value: 89.12633563796355
---

<!-- 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. -->

# xty_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: 2.0874
- Wer: 89.1263

## 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      |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 8.5823        | 1.0526  | 10   | 7.7265          | 100.0629 |
| 5.0617        | 2.1053  | 20   | 4.2231          | 100.0    |
| 3.6512        | 3.1579  | 30   | 3.5631          | 100.0    |
| 3.3211        | 4.2105  | 40   | 3.3414          | 100.0    |
| 3.2144        | 5.2632  | 50   | 3.2086          | 100.0    |
| 3.128         | 6.3158  | 60   | 3.1724          | 100.0    |
| 3.0999        | 7.3684  | 70   | 3.1261          | 100.0    |
| 3.0585        | 8.4211  | 80   | 3.1200          | 100.0    |
| 3.0318        | 9.4737  | 90   | 3.1001          | 100.0    |
| 3.0166        | 10.5263 | 100  | 3.0985          | 100.0    |
| 3.0147        | 11.5789 | 110  | 3.0971          | 100.0    |
| 3.0028        | 12.6316 | 120  | 3.0775          | 100.0    |
| 2.991         | 13.6842 | 130  | 3.0619          | 100.0    |
| 2.9692        | 14.7368 | 140  | 3.0477          | 100.0    |
| 2.9355        | 15.7895 | 150  | 3.0081          | 100.0    |
| 2.8754        | 16.8421 | 160  | 2.9190          | 100.0    |
| 2.7087        | 17.8947 | 170  | 2.7367          | 100.0    |
| 2.4346        | 18.9474 | 180  | 2.5043          | 108.5481 |
| 2.3184        | 20.0    | 190  | 2.3709          | 103.8969 |
| 2.0887        | 21.0526 | 200  | 2.2196          | 103.3941 |
| 1.9198        | 22.1053 | 210  | 2.1078          | 104.7140 |
| 1.6995        | 23.1579 | 220  | 2.0556          | 98.4287  |
| 1.6576        | 24.2105 | 230  | 2.0081          | 100.6914 |
| 1.4855        | 25.2632 | 240  | 1.9958          | 98.0515  |
| 1.3788        | 26.3158 | 250  | 1.9729          | 94.8460  |
| 1.3202        | 27.3684 | 260  | 1.9618          | 98.6172  |
| 1.2237        | 28.4211 | 270  | 1.9662          | 93.6518  |
| 1.1389        | 29.4737 | 280  | 1.9882          | 92.7090  |
| 1.0597        | 30.5263 | 290  | 1.9655          | 92.3947  |
| 1.0203        | 31.5789 | 300  | 1.9616          | 90.1948  |
| 0.9778        | 32.6316 | 310  | 1.9585          | 90.8234  |
| 0.9553        | 33.6842 | 320  | 1.9875          | 90.5091  |
| 0.895         | 34.7368 | 330  | 1.9913          | 91.3891  |
| 0.9021        | 35.7895 | 340  | 1.9906          | 90.2577  |
| 0.8105        | 36.8421 | 350  | 2.0182          | 89.4406  |
| 0.8052        | 37.8947 | 360  | 2.0227          | 89.5663  |
| 0.7484        | 38.9474 | 370  | 2.0539          | 89.0006  |
| 0.7886        | 40.0    | 380  | 2.0616          | 90.6977  |
| 0.7348        | 41.0526 | 390  | 2.0590          | 89.1892  |
| 0.7079        | 42.1053 | 400  | 2.0790          | 89.8806  |
| 0.7215        | 43.1579 | 410  | 2.0701          | 89.3149  |
| 0.6997        | 44.2105 | 420  | 2.0832          | 89.3777  |
| 0.721         | 45.2632 | 430  | 2.0798          | 89.3149  |
| 0.6609        | 46.3158 | 440  | 2.0834          | 88.4349  |
| 0.6562        | 47.3684 | 450  | 2.0892          | 89.0006  |
| 0.6418        | 48.4211 | 460  | 2.0878          | 89.3777  |
| 0.677         | 49.4737 | 470  | 2.0874          | 89.2520  |
| 0.6821        | 50.5263 | 480  | 2.0874          | 89.1263  |
| 0.6798        | 51.5789 | 490  | 2.0875          | 89.0635  |
| 0.7188        | 52.6316 | 500  | 2.0874          | 89.1263  |


### Framework versions

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