106 lines
3.5 KiB
Markdown
106 lines
3.5 KiB
Markdown
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---
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library_name: transformers
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license: mit
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base_model: Sunbird/asr-whisper-large-v3-salt
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: cdli-whisper-ml-eng-lug-full-a40-5e-5
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results: []
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datasets:
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- cdli/ugandan_luganda_nonstandard_speech_v1.0
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- cdli/ugandan_english_nonstandard_speech_v1.0
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# cdli-whisper-ml-eng-lug-full-a40-5e-5
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This is a multilingual model and is a fine-tuned version of [Sunbird/asr-whisper-large-v3-salt](https://huggingface.co/Sunbird/asr-whisper-large-v3-salt) on the Ugandan CDLI Atypical speech datasets.
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It achieves the following results on the evaluation set:
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- Loss: 1.2283
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- Wer: 0.4137
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- Cer: 0.2271
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On the test set with repetition penalty of 1.3 and no_repeat_ngram_size of 2 it obtains:
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- test_cer = 0.1268
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- test_loss = 0.8137
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- test_runtime = 0:22:24.23
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- test_samples_per_second = 1.518
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- test_steps_per_second = 0.379
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- test_wer = 0.2851
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# English
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- Overall WER (normalized): 0.224
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- Overall CER (normalized): 0.135
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- Avg WER (normalized): 0.214
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- Avg CER (normalized): 0.133
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# Luganda
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- Overall WER (normalized): 0.414
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- Overall CER (normalized): 0.146
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- Avg WER (normalized): 0.354
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- Avg CER (normalized): 0.12
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## Model description
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The training was resumed from epoch 7.2255 and the Wer reported after that is a bit dirty (CER instead of WER)
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 150
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- training_steps: 4000
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### Training results
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
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|:-------------:|:-------:|:----:|:------:|:---------------:|:------:|
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| 0.6253 | 0.7228 | 250 | 0.2722 | 0.8156 | 0.4660 |
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| 0.4188 | 1.4452 | 500 | 0.2362 | 0.8119 | 0.4247 |
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| 0.2709 | 2.1677 | 750 | 0.2352 | 0.8229 | 0.4206 |
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| 0.2571 | 2.8905 | 1000 | 0.2261 | 0.8141 | 0.4153 |
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| 0.1581 | 3.6129 | 1250 | 0.2292 | 0.9097 | 0.4167 |
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| 0.083 | 4.3354 | 1500 | 0.2271 | 0.9749 | 0.4177 |
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| 0.0593 | 5.0578 | 1750 | 0.2266 | 1.0613 | 0.4107 |
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| 0.0518 | 5.7806 | 2000 | 0.2235 | 1.0547 | 0.4108 |
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| 0.0382 | 6.5031 | 2250 | 0.2249 | 1.1098 | 0.4095 |
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| 0.0356 | 7.2255 | 2500 | 0.2238 | 1.1149 | 0.4087 |
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| 0.0408 | 7.9483 | 2750 | 1.1168 | 0.4139 | 0.2261 |
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| 0.0368 | 8.6737 | 3000 | 1.1499 | 0.4172 | 0.2279 |
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| 0.0271 | 9.3961 | 3250 | 1.2052 | 0.4132 | 0.2271 |
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| 0.0237 | 10.1185 | 3500 | 1.2107 | 0.4114 | 0.2263 |
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| 0.0212 | 10.8413 | 3750 | 1.2275 | 0.4111 | 0.2250 |
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| 0.0221 | 11.5638 | 4000 | 1.2283 | 0.4137 | 0.2271 |
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### Framework versions
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- Transformers 4.52.0
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- Pytorch 2.7.1+cu118
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- Datasets 3.6.0
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- Tokenizers 0.21.4
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