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ssc-qxp-mms-model-mix-adapt-max-longcv
ctaguchi/ssc-qxp-mms-model-mix-adapt-max-longcv
ssc-qxp-mms-model-mix-adapt-max-longcv 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 10, 2025
- Updated
- Dec 10, 2025
- SHA
- 74899891
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-qxp-mms-model-mix-adapt-max-longcv
This model is a fine-tuned version of facebook/mms-1b-all on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6787
- Cer: 0.2539
- Wer: 0.8033
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 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: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 3.0113 | 0.9524 | 200 | 2.9124 | 0.8802 | 1.0138 |
| 2.793 | 1.9048 | 400 | 2.6125 | 0.8607 | 1.0221 |
| 2.6415 | 2.8571 | 600 | 2.5472 | 0.8418 | 1.0074 |
| 2.4118 | 3.8095 | 800 | 2.4415 | 0.7797 | 1.0028 |
| 2.3343 | 4.7619 | 1000 | 2.3311 | 0.8075 | 1.0083 |
| 2.2079 | 5.7143 | 1200 | 2.1933 | 0.7873 | 0.9917 |
| 1.9533 | 6.6667 | 1400 | 1.7645 | 0.6747 | 0.9798 |
| 1.6268 | 7.6190 | 1600 | 1.4405 | 0.5509 | 0.9504 |
| 1.3779 | 8.5714 | 1800 | 1.1779 | 0.4539 | 0.9164 |
| 1.1936 | 9.5238 | 2000 | 1.0616 | 0.3850 | 0.8980 |
| 1.0331 | 10.4762 | 2200 | 0.9177 | 0.3650 | 0.8704 |
| 0.9716 | 11.4286 | 2400 | 0.8287 | 0.3159 | 0.8428 |
| 0.8937 | 12.3810 | 2600 | 0.7543 | 0.2861 | 0.8373 |
| 0.8052 | 13.3333 | 2800 | 0.6951 | 0.2653 | 0.8125 |
| 0.757 | 14.2857 | 3000 | 0.6787 | 0.2539 | 0.8033 |
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
- License
- cc-by-nc-4.0
- Created
- Dec 10, 2025
- Updated
- Dec 10, 2025