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wav2vec2-large-mms-1b-aft-hch
robertp408/wav2vec2-large-mms-1b-aft-hch
wav2vec2-large-mms-1b-aft-hch 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
- f824c590
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-hch
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: 0.5270
- Wer: 0.6696
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 |
|---|---|---|---|---|
| 7.3706 | 0.2370 | 100 | 1.2824 | 0.9847 |
| 0.9732 | 0.4739 | 200 | 0.9079 | 0.9222 |
| 0.8624 | 0.7109 | 300 | 0.8058 | 0.8791 |
| 0.7599 | 0.9479 | 400 | 0.8033 | 0.8824 |
| 0.6971 | 1.1848 | 500 | 0.6960 | 0.8122 |
| 0.6642 | 1.4218 | 600 | 0.6869 | 0.8134 |
| 0.6401 | 1.6588 | 700 | 0.6817 | 0.7969 |
| 0.6022 | 1.8957 | 800 | 0.6189 | 0.7678 |
| 0.5619 | 2.1327 | 900 | 0.6786 | 0.7951 |
| 0.5339 | 2.3697 | 1000 | 0.6041 | 0.7354 |
| 0.5639 | 2.6066 | 1100 | 0.5733 | 0.7110 |
| 0.5242 | 2.8436 | 1200 | 0.6208 | 0.7495 |
| 0.499 | 3.0806 | 1300 | 0.5747 | 0.7106 |
| 0.4904 | 3.3175 | 1400 | 0.5403 | 0.6728 |
| 0.4751 | 3.5545 | 1500 | 0.5549 | 0.6902 |
| 0.4814 | 3.7915 | 1600 | 0.5485 | 0.6990 |
| 0.4518 | 4.0284 | 1700 | 0.5487 | 0.6847 |
| 0.4134 | 4.2654 | 1800 | 0.5363 | 0.6733 |
| 0.4227 | 4.5024 | 1900 | 0.5499 | 0.6939 |
| 0.4246 | 4.7393 | 2000 | 0.5248 | 0.6678 |
| 0.4069 | 4.9763 | 2100 | 0.5270 | 0.6696 |
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