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ssc-sco-mms-model-mix-adapt-max
ctaguchi/ssc-sco-mms-model-mix-adapt-max
ssc-sco-mms-model-mix-adapt-max is a automatic speech recognition model from ctaguchi. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Dec 4, 2025
- Updated
- Dec 5, 2025
- SHA
- 44392a64
Base models
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-sco-mms-model-mix-adapt-max
This model is a fine-tuned version of facebook/mms-1b-all on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5079
- Cer: 0.1382
- Wer: 0.3974
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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 1.1023 | 0.3419 | 200 | 0.7274 | 0.1928 | 0.5025 |
| 0.9531 | 0.6838 | 400 | 0.6376 | 0.1665 | 0.4613 |
| 0.8493 | 1.0256 | 600 | 0.5990 | 0.1583 | 0.4471 |
| 0.8849 | 1.3675 | 800 | 0.5687 | 0.1531 | 0.4267 |
| 0.8303 | 1.7094 | 1000 | 0.5544 | 0.1481 | 0.4195 |
| 0.8295 | 2.0513 | 1200 | 0.5455 | 0.1472 | 0.4177 |
| 0.8169 | 2.3932 | 1400 | 0.5352 | 0.1465 | 0.4181 |
| 0.7759 | 2.7350 | 1600 | 0.5332 | 0.1453 | 0.4129 |
| 0.838 | 3.0769 | 1800 | 0.5252 | 0.1420 | 0.4042 |
| 0.7476 | 3.4188 | 2000 | 0.5220 | 0.1415 | 0.4049 |
| 0.7028 | 3.7607 | 2200 | 0.5161 | 0.1404 | 0.4071 |
| 0.7355 | 4.1026 | 2400 | 0.5146 | 0.1408 | 0.3999 |
| 0.7489 | 4.4444 | 2600 | 0.5122 | 0.1399 | 0.4004 |
| 0.7108 | 4.7863 | 2800 | 0.5079 | 0.1382 | 0.3974 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- cc-by-nc-4.0
- Created
- Dec 4, 2025
- Updated
- Dec 5, 2025