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mms-1b-all-ckt-best-cer-model
tadgeis/mms-1b-all-ckt-best-cer-model
mms-1b-all-ckt-best-cer-model is a automatic speech recognition model from tadgeis. 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
- May 27, 2026
- Updated
- Jun 12, 2026
- SHA
- b57a7372
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. -->
mms-1b-ckt-staged-bible-radio-to-chuklang-source-eval-stage2
This model is a fine-tuned version of facebook/mms-1b-all on a private dataset, intended to achieve the best quality specifically on Chukchi expedition data from project Chuklang.
It achieves the following results on the evaluation set (evaluation set containing only expedition Chuklang data):
- Wer: 0.7391
- Cer: 0.1825
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: 16
- eval_batch_size: 16
- seed: 42
- 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: 25
- num_epochs: 24
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.2212 | 1.1905 | 50 | 0.8237 | 0.8397 | 0.2052 |
| 0.9554 | 2.3810 | 100 | 0.7470 | 0.7778 | 0.1877 |
| 0.8978 | 3.5714 | 150 | 0.7269 | 0.8 | 0.1921 |
| 0.8594 | 4.7619 | 200 | 0.7058 | 0.7587 | 0.1820 |
| 0.7897 | 5.9524 | 250 | 0.7039 | 0.7413 | 0.1769 |
| 0.7547 | 7.1429 | 300 | 0.6790 | 0.7111 | 0.1701 |
| 0.7233 | 8.3333 | 350 | 0.6716 | 0.7016 | 0.1677 |
| 0.6611 | 9.5238 | 400 | 0.6658 | 0.7190 | 0.1712 |
| 0.6446 | 10.7143 | 450 | 0.6566 | 0.6952 | 0.1637 |
| 0.6057 | 11.9048 | 500 | 0.6674 | 0.6937 | 0.1610 |
| 0.6135 | 13.0952 | 550 | 0.6605 | 0.6714 | 0.1584 |
| 0.5837 | 14.2857 | 600 | 0.6455 | 0.7079 | 0.1615 |
| 0.5391 | 15.4762 | 650 | 0.6601 | 0.6937 | 0.1619 |
| 0.5051 | 16.6667 | 700 | 0.6519 | 0.6889 | 0.1606 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- cc-by-nc-4.0
- Languages
- ckt
- Created
- May 27, 2026
- Updated
- Jun 12, 2026