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ssc-tob-mms-model-mix-adapt-max-lowlr
ctaguchi/ssc-tob-mms-model-mix-adapt-max-lowlr
ssc-tob-mms-model-mix-adapt-max-lowlr 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 6, 2025
- Updated
- Dec 6, 2025
- SHA
- fc0338ff
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-tob-mms-model-mix-adapt-max-lowlr
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: 0.7360
- Cer: 0.1766
- Wer: 0.6035
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 6
- 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 1.1871 | 0.7648 | 200 | 0.8279 | 0.1866 | 0.6352 |
| 0.7886 | 1.5277 | 400 | 0.7769 | 0.1887 | 0.6419 |
| 0.7329 | 2.2906 | 600 | 0.7502 | 0.1785 | 0.6124 |
| 0.6881 | 3.0535 | 800 | 0.7380 | 0.1798 | 0.6157 |
| 0.647 | 3.8184 | 1000 | 0.7332 | 0.1798 | 0.6102 |
| 0.673 | 4.5813 | 1200 | 0.7311 | 0.1805 | 0.6145 |
| 0.5852 | 5.3442 | 1400 | 0.7352 | 0.1780 | 0.6076 |
| 0.6142 | 6.1071 | 1600 | 0.7315 | 0.1774 | 0.6054 |
| 0.6056 | 6.8719 | 1800 | 0.7371 | 0.1778 | 0.6061 |
| 0.6021 | 7.6348 | 2000 | 0.7350 | 0.1772 | 0.6072 |
| 0.5971 | 8.3977 | 2200 | 0.7379 | 0.1787 | 0.6102 |
| 0.5732 | 9.1606 | 2400 | 0.7342 | 0.1762 | 0.6020 |
| 0.5988 | 9.9254 | 2600 | 0.7360 | 0.1766 | 0.6035 |
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 6, 2025
- Updated
- Dec 6, 2025