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Model: KasuleTrevor/cdli-whisper-ml-eng-lug-full-a40 Source: Original Platform
2026-05-12 11:00:36 +08:00
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+---
+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