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wav2vec2-large-mms-1b-aft-bew
robertp408/wav2vec2-large-mms-1b-aft-bew
wav2vec2-large-mms-1b-aft-bew is a automatic speech recognition model from robertp408. 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
- Sep 28, 2025
- Updated
- Oct 7, 2025
- SHA
- 6c7b10a1
Base models
Datasets
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. -->
wav2vec2-large-mms-1b-aft-bew
This model is a fine-tuned version of facebook/mms-1b-all on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4987
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: 2
- eval_batch_size: 8
- 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: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.0585 | 0.2469 | 100 | 0.8727 | 0.6269 |
| 0.8154 | 0.4938 | 200 | 0.8308 | 0.5949 |
| 0.8045 | 0.7407 | 300 | 0.7554 | 0.5535 |
| 0.7555 | 0.9877 | 400 | 0.7224 | 0.5515 |
| 0.6954 | 1.2346 | 500 | 0.7175 | 0.5414 |
| 0.7207 | 1.4815 | 600 | 0.7198 | 0.5393 |
| 0.7121 | 1.7284 | 700 | 0.7117 | 0.5420 |
| 0.6841 | 1.9753 | 800 | 0.6982 | 0.5284 |
| 0.6241 | 2.2222 | 900 | 0.6955 | 0.5327 |
| 0.6435 | 2.4691 | 1000 | 0.6822 | 0.5336 |
| 0.6824 | 2.7160 | 1100 | 0.6768 | 0.5269 |
| 0.6562 | 2.9630 | 1200 | 0.6706 | 0.5265 |
| 0.6151 | 3.2099 | 1300 | 0.6838 | 0.5118 |
| 0.6691 | 3.4568 | 1400 | 0.6691 | 0.5096 |
| 0.6004 | 3.7037 | 1500 | 0.6625 | 0.5067 |
| 0.5886 | 3.9506 | 1600 | 0.6611 | 0.5077 |
| 0.6093 | 4.1975 | 1700 | 0.6592 | 0.5019 |
| 0.5688 | 4.4444 | 1800 | 0.6599 | 0.4999 |
| 0.5745 | 4.6914 | 1900 | 0.6533 | 0.5010 |
| 0.6027 | 4.9383 | 2000 | 0.6521 | 0.4987 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- cc-by-nc-4.0
- Created
- Sep 28, 2025
- Updated
- Oct 7, 2025