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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_14_0
metrics:
- wer
model-index:
- name: XLS-R-SWAHILI-ASR-CV14
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_14_0
type: common_voice_14_0
config: sw
split: test
args: sw
metrics:
- name: Wer
type: wer
value: 0.21479210182431807
---
<!-- 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. -->
# XLS-R-SWAHILI-ASR-CV14
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_14_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.2148
- Cer: 0.0684
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
| 3.9008 | 0.33 | 400 | 0.2565 | inf | 0.8327 |
| 0.5689 | 0.66 | 800 | 0.1306 | inf | 0.4598 |
| 0.3838 | 1.0 | 1200 | 0.1130 | inf | 0.3786 |
| 0.3054 | 1.33 | 1600 | 0.1032 | inf | 0.3407 |
| 0.2877 | 1.66 | 2000 | 0.0976 | inf | 0.3239 |
| 0.2698 | 1.99 | 2400 | 0.0952 | inf | 0.3078 |
| 0.2285 | 2.32 | 2800 | 0.0956 | inf | 0.3031 |
| 0.224 | 2.66 | 3200 | 0.0892 | inf | 0.2861 |
| 0.2224 | 2.99 | 3600 | 0.0877 | inf | 0.2809 |
| 0.1906 | 3.32 | 4000 | 0.0853 | inf | 0.2748 |
| 0.1897 | 3.65 | 4400 | 0.0844 | inf | 0.2707 |
| 0.183 | 3.98 | 4800 | 0.0814 | inf | 0.2614 |
| 0.1586 | 4.32 | 5200 | 0.0809 | inf | 0.2569 |
| 0.162 | 4.65 | 5600 | 0.0782 | inf | 0.2493 |
| 0.1548 | 4.98 | 6000 | 0.0772 | inf | 0.2467 |
| 0.1364 | 5.31 | 6400 | 0.0782 | inf | 0.2459 |
| 0.1344 | 5.64 | 6800 | 0.0760 | inf | 0.2404 |
| 0.1301 | 5.98 | 7200 | 0.0738 | inf | 0.2346 |
| 0.1165 | 6.31 | 7600 | inf | 0.2321 | 0.0729 |
| 0.1142 | 6.64 | 8000 | inf | 0.2266 | 0.0719 |
| 0.1103 | 6.97 | 8400 | inf | 0.2229 | 0.0705 |
| 0.101 | 7.3 | 8800 | inf | 0.2203 | 0.0699 |
| 0.1006 | 7.63 | 9200 | inf | 0.2174 | 0.0692 |
| 0.0958 | 7.97 | 9600 | inf | 0.2160 | 0.0688 |
| 0.0896 | 8.3 | 10000 | inf | 0.2148 | 0.0684 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2
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