---
license: apache-2.0
base_model: nutella-toast/wav2vec2-large-xls-r-ssw
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-ssw
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ml-superb-subset
      type: ml-superb-subset
      config: ssw
      split: dev
      args: ssw
    metrics:
    - name: Wer
      type: wer
      value: 0.7320872274143302
---

<!-- 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-large-xls-r-ssw

This model is a fine-tuned version of [nutella-toast/wav2vec2-large-xls-r-ssw](https://huggingface.co/nutella-toast/wav2vec2-large-xls-r-ssw) on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7327
- Wer: 0.7321

## Model description

Finetuned version of vanilla wav2vec2-large-xls-r for siSwati. For CS224S at Stanford University.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5779        | 1.0471 | 100  | 0.7902          | 0.8785 |
| 0.5307        | 2.0942 | 200  | 0.8185          | 0.8660 |
| 0.4826        | 3.1414 | 300  | 0.8378          | 0.8692 |
| 0.4529        | 4.1885 | 400  | 0.8048          | 0.9097 |
| 0.5053        | 5.2356 | 500  | 0.9541          | 0.8910 |
| 0.4149        | 6.2827 | 600  | 0.7687          | 0.7913 |
| 0.3179        | 7.3298 | 700  | 0.7678          | 0.7850 |
| 0.2642        | 8.3770 | 800  | 0.7151          | 0.7321 |
| 0.2147        | 9.4241 | 900  | 0.7327          | 0.7321 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1