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library_name: transformers
license: apache-2.0
base_model: alamsher/wav2vec2-large-xlsr-53-common-voice-sw
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: sw
split: validation
args: sw
metrics:
- name: Wer
type: wer
value: 0.18270008084074374
---
<!-- 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. -->
# model
This model is a fine-tuned version of [alamsher/wav2vec2-large-xlsr-53-common-voice-sw](https://huggingface.co/alamsher/wav2vec2-large-xlsr-53-common-voice-sw) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2929
- Wer: 0.1827
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.2938 | 0.5028 | 500 | 0.3439 | 0.1940 |
| 0.273 | 1.0050 | 1000 | 0.3088 | 0.1923 |
| 0.2327 | 1.5078 | 1500 | 0.3031 | 0.1907 |
| 0.2351 | 2.0101 | 2000 | 0.2877 | 0.1897 |
| 0.2213 | 2.5128 | 2500 | 0.2915 | 0.1889 |
| 0.2172 | 3.0151 | 3000 | 0.2863 | 0.1879 |
| 0.191 | 3.5178 | 3500 | 0.2881 | 0.1880 |
| 0.2048 | 4.0201 | 4000 | 0.2832 | 0.1875 |
| 0.1928 | 4.5229 | 4500 | 0.2825 | 0.1863 |
| 0.1871 | 5.0251 | 5000 | 0.2861 | 0.1863 |
| 0.1856 | 5.5279 | 5500 | 0.2856 | 0.1856 |
| 0.1763 | 6.0302 | 6000 | 0.2854 | 0.1856 |
| 0.1707 | 6.5329 | 6500 | 0.2883 | 0.1853 |
| 0.1714 | 7.0352 | 7000 | 0.2849 | 0.1850 |
| 0.1577 | 7.5380 | 7500 | 0.2875 | 0.1851 |
| 0.162 | 8.0402 | 8000 | 0.2852 | 0.1850 |
| 0.1489 | 8.5430 | 8500 | 0.2911 | 0.1833 |
| 0.1674 | 9.0452 | 9000 | 0.2887 | 0.1826 |
| 0.1818 | 9.5480 | 9500 | 0.2894 | 0.1828 |
| 0.165 | 10.0503 | 10000 | 0.2915 | 0.1830 |
| 0.1715 | 10.5530 | 10500 | 0.2929 | 0.1827 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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