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---
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
|