---
library_name: transformers
license: mit
base_model: Sunbird/asr-whisper-large-v3-salt
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: cdli-whisper-ml-eng-lug-full-a40
  results: []
datasets:
- cdli/ugandan_luganda_nonstandard_speech_v1.0
- cdli/ugandan_english_nonstandard_speech_v1.0
---

<!-- 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. -->

# cdli-whisper-ml-eng-lug-full-a40

This model is a fine-tuned version of [Sunbird/asr-whisper-large-v3-salt](https://huggingface.co/Sunbird/asr-whisper-large-v3-salt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7960
- Wer: 0.4932
- Cer: 0.3124
- test_cer                =     0.1888
- test_loss               =     0.5511
- test_runtime            = 0:30:54.57
- test_samples_per_second =        1.1
- test_steps_per_second   =      0.275
- test_wer                =     0.3478

## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 2500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.7765        | 0.3615 | 250  | 0.8623          | 0.5111 | 0.3216 |
| 0.6632        | 0.7229 | 500  | 0.8226          | 0.5051 | 0.3236 |
| 0.5672        | 1.0839 | 750  | 0.8067          | 0.4862 | 0.3044 |
| 0.6058        | 1.4453 | 1000 | 0.7991          | 0.4949 | 0.3142 |
| 0.6589        | 1.8068 | 1250 | 0.7972          | 0.4901 | 0.3106 |
| 0.5959        | 2.1677 | 1500 | 0.7977          | 0.4926 | 0.3118 |
| 0.5402        | 2.5292 | 1750 | 0.7964          | 0.4926 | 0.3114 |
| 0.5934        | 2.8907 | 2000 | 0.7964          | 0.4921 | 0.3118 |
| 0.5464        | 3.2516 | 2250 | 0.7960          | 0.4931 | 0.3113 |
| 0.5497        | 3.6130 | 2500 | 0.7960          | 0.4932 | 0.3124 |


### Framework versions

- Transformers 4.52.0
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.4