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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
- generated_from_trainer
datasets:
- common_voice_14_0
metrics:
- wer
model-index:
- name: XLS-R-SWAHILI-ASR-CV-14-1B
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: 0.2794303764906829
---
<!-- 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-CV-14-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_14_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.2794
- Cer: 0.0903
## 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 |
|:-------------:|:-----:|:-----:|:------:|:---------------:|:------:|
| 1.9691 | 0.33 | 400 | 0.2374 | inf | 0.6776 |
| 0.5464 | 0.66 | 800 | 0.1758 | inf | 0.5598 |
| 0.4909 | 1.0 | 1200 | 0.1680 | inf | 0.5243 |
| 0.4263 | 1.33 | 1600 | 0.1502 | inf | 0.4706 |
| 0.4047 | 1.66 | 2000 | 0.1580 | inf | 0.4858 |
| 0.4054 | 1.99 | 2400 | 0.1426 | inf | 0.4348 |
| 0.3542 | 2.32 | 2800 | 0.1340 | inf | 0.4185 |
| 0.3525 | 2.66 | 3200 | 0.1400 | inf | 0.4311 |
| 0.3359 | 2.99 | 3600 | 0.1308 | inf | 0.4012 |
| 0.3006 | 3.32 | 4000 | 0.1278 | inf | 0.3939 |
| 0.326 | 1.83 | 4400 | inf | 0.4232 | 0.1362 |
| 0.326 | 1.99 | 4800 | inf | 0.4136 | 0.1350 |
| 0.3034 | 2.16 | 5200 | inf | 0.4282 | 0.1419 |
| 0.2925 | 2.32 | 5600 | inf | 0.3901 | 0.1282 |
| 0.2822 | 2.49 | 6000 | inf | 0.3876 | 0.1270 |
| 0.2659 | 2.66 | 6400 | inf | 0.3586 | 0.1159 |
| 0.2582 | 2.82 | 6800 | inf | 0.3536 | 0.1168 |
| 0.2414 | 2.99 | 7200 | inf | 0.3327 | 0.1069 |
| 0.208 | 3.15 | 7600 | inf | 0.3249 | 0.1053 |
| 0.1934 | 3.32 | 8000 | inf | 0.3120 | 0.1015 |
| 0.1881 | 3.49 | 8400 | inf | 0.3058 | 0.0993 |
| 0.1774 | 3.65 | 8800 | inf | 0.2962 | 0.0959 |
| 0.1736 | 3.82 | 9200 | inf | 0.2902 | 0.0935 |
| 0.1679 | 3.98 | 9600 | inf | 0.2843 | 0.0917 |
| 0.1436 | 4.15 | 10000 | inf | 0.2794 | 0.0903 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2
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