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---
language:
- sw
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: Incremental Swahili Luganda
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mix data
type: mozilla-foundation/common_voice_15_0
config: lg
split: validation
args: 'config: lu, split: test'
metrics:
- name: Wer
type: wer
value: 30.81815492541098
---
<!-- 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. -->
# Incremental Swahili Luganda
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mix data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3450
- Wer: 30.8182
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1442 | 0.1129 | 500 | 0.3683 | 32.9502 |
| 0.1532 | 0.2258 | 1000 | 0.3707 | 32.7589 |
| 0.1478 | 0.3388 | 1500 | 0.3663 | 33.0019 |
| 0.1375 | 0.4517 | 2000 | 0.3625 | 31.7817 |
| 0.1509 | 0.5646 | 2500 | 0.3552 | 32.2106 |
| 0.139 | 0.6775 | 3000 | 0.3510 | 31.5271 |
| 0.1404 | 0.7904 | 3500 | 0.3473 | 30.9787 |
| 0.1349 | 0.9033 | 4000 | 0.3450 | 30.8182 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
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