| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-large-robust-ft-libri-960h |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: MSP-Audio |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # MSP-Audio |
|
|
| This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-libri-960h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-libri-960h) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4829 |
| - Wer: 0.2566 |
| - Cer: 0.1474 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - 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: cosine |
| - lr_scheduler_warmup_steps: 1000.0 |
| - training_steps: 20000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:------:|:---------------:|:------:| |
| | 1.5170 | 0.05 | 1000 | 0.2912 | 0.7151 | 0.4457 | |
| | 1.4106 | 0.1 | 2000 | 0.2405 | 0.5715 | 0.3834 | |
| | 1.3445 | 0.15 | 3000 | 0.2075 | 0.5755 | 0.3395 | |
| | 1.1670 | 0.2 | 4000 | 0.1713 | 0.4470 | 0.2948 | |
| | 1.1405 | 0.25 | 5000 | 0.1559 | 0.4444 | 0.2830 | |
| | 1.0518 | 0.3 | 6000 | 0.2054 | 0.6352 | 0.3497 | |
| | 1.0164 | 0.35 | 7000 | 0.1550 | 0.4675 | 0.2926 | |
| | 1.0954 | 0.4 | 8000 | 0.2192 | 0.6849 | 0.3549 | |
| | 1.0427 | 0.45 | 9000 | 0.1521 | 0.5033 | 0.2706 | |
| | 1.0515 | 0.5 | 10000 | 0.1804 | 0.6117 | 0.2952 | |
| | 0.9930 | 0.55 | 11000 | 0.1802 | 0.6416 | 0.2949 | |
| | 1.1711 | 0.05 | 12000 | 0.5594 | 0.2755 | 0.1603 | |
| | 1.0789 | 0.1 | 13000 | 0.4829 | 0.2566 | 0.1474 | |
| | 1.1322 | 0.15 | 14000 | 0.5620 | 0.2777 | 0.1640 | |
| | 0.9884 | 0.2 | 15000 | 0.4972 | 0.2594 | 0.1534 | |
| | 0.9589 | 0.25 | 16000 | 0.5521 | 0.2804 | 0.1689 | |
| | 0.9326 | 0.3 | 17000 | 0.5657 | 0.2834 | 0.1761 | |
| | 0.9061 | 0.35 | 18000 | 0.5497 | 0.2771 | 0.1701 | |
| | 0.9746 | 0.4 | 19000 | 0.5283 | 0.2681 | 0.1632 | |
| | 0.9603 | 0.45 | 20000 | 0.5331 | 0.2696 | 0.1639 | |
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|
| ### Framework versions |
|
|
| - Transformers 5.10.2 |
| - Pytorch 2.8.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
