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ssc-led-mms-model-mix-adapt-max3
ctaguchi/ssc-led-mms-model-mix-adapt-max3
ssc-led-mms-model-mix-adapt-max3 is a automatic speech recognition model from ctaguchi. Use it when you need speech turned into text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- wav2vec2
- Access
- Public
- Created
- Dec 11, 2025
- Updated
- Dec 11, 2025
- SHA
- 18135296
Model card
From the Hugging Face model README
<!-- 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. -->
ssc-led-mms-model-mix-adapt-max3
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4678
- Cer: 0.1114
- Wer: 0.3031
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: 0.0005
- train_batch_size: 1
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 0.3797 | 0.2683 | 200 | 0.5283 | 0.1347 | 0.3950 |
| 0.4209 | 0.5366 | 400 | 0.5236 | 0.1286 | 0.3583 |
| 0.4153 | 0.8048 | 600 | 0.4995 | 0.1234 | 0.3425 |
| 0.4124 | 1.0724 | 800 | 0.4922 | 0.1189 | 0.3264 |
| 0.4092 | 1.3407 | 1000 | 0.4881 | 0.1198 | 0.3321 |
| 0.3843 | 1.6090 | 1200 | 0.4863 | 0.1181 | 0.3218 |
| 0.3939 | 1.8773 | 1400 | 0.4833 | 0.1205 | 0.3414 |
| 0.3319 | 2.1449 | 1600 | 0.4808 | 0.1195 | 0.3304 |
| 0.3488 | 2.4131 | 1800 | 0.4830 | 0.1208 | 0.3366 |
| 0.3774 | 2.6814 | 2000 | 0.4786 | 0.1172 | 0.3258 |
| 0.3224 | 2.9497 | 2200 | 0.4714 | 0.1146 | 0.3121 |
| 0.3388 | 3.2173 | 2400 | 0.4774 | 0.1166 | 0.3190 |
| 0.3087 | 3.4856 | 2600 | 0.4768 | 0.1169 | 0.3274 |
| 0.3038 | 3.7539 | 2800 | 0.4725 | 0.1145 | 0.3162 |
| 0.3451 | 4.0215 | 3000 | 0.4674 | 0.1124 | 0.3081 |
| 0.2823 | 4.2897 | 3200 | 0.4744 | 0.1130 | 0.3107 |
| 0.2894 | 4.5580 | 3400 | 0.4713 | 0.1121 | 0.3063 |
| 0.2791 | 4.8263 | 3600 | 0.4678 | 0.1114 | 0.3031 |
Framework versions
- Transformers 4.52.1
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- Created
- Dec 11, 2025
- Updated
- Dec 11, 2025