| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| - cer |
| model-index: |
| - name: MSP-Audio |
| results: [] |
| license: apache-2.0 |
| datasets: |
| - MahmoodAnaam/lrs2_train_validation_test |
| language: |
| - en |
| base_model: |
| - facebook/wav2vec2-base-960h |
| --- |
| |
| <!-- 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 [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2711 |
| - Wer: 0.3066 |
| - Cer: 0.2433 |
|
|
|
|
| ## Evaluation |
|
|
| **Note**: we evaluate the test data set with `batch_size=1` on purpose |
| due to this [issue](https://github.com/pytorch/fairseq/issues/3227). |
| Since padded inputs don\'t yield the exact same output as non-padded |
| inputs, a better WER can be achieved by not padding the input at all. |
|
|
| - Test WER: 0.169 |
| - Test CER: 0.062 |
|
|
|
|
| ## 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: 64 |
| - eval_batch_size: 1 |
| - 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: 1000.0 |
| - num_epochs: 20.0 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
| | 2.9020 | 0.6821 | 500 | 0.3734 | 0.3326 | 0.2717 | |
| | 2.9679 | 1.3643 | 1000 | 0.3505 | 0.3264 | 0.2593 | |
| | 2.9390 | 2.0464 | 1500 | 0.3923 | 0.3659 | 0.2725 | |
| | 2.8775 | 2.7285 | 2000 | 0.3607 | 0.3614 | 0.2675 | |
| | 2.9122 | 3.4106 | 2500 | 0.3953 | 0.3812 | 0.2770 | |
| | 2.8879 | 4.0928 | 3000 | 0.3950 | 0.3800 | 0.2774 | |
| | 2.8735 | 4.7749 | 3500 | 0.4303 | 0.3827 | 0.2849 | |
| | 2.9131 | 5.4570 | 4000 | 0.4071 | 0.3833 | 0.2847 | |
| | 2.8792 | 6.1392 | 4500 | 0.3638 | 0.3640 | 0.2703 | |
| | 2.8804 | 6.8213 | 5000 | 0.3389 | 0.3544 | 0.2669 | |
| | 2.8883 | 7.5034 | 5500 | 0.3495 | 0.3583 | 0.2693 | |
| | 2.8861 | 8.1855 | 6000 | 0.3985 | 0.3827 | 0.2849 | |
| | 2.8934 | 8.8677 | 6500 | 0.3453 | 0.3590 | 0.2694 | |
| | 2.9068 | 9.5498 | 7000 | 0.3327 | 0.3344 | 0.2596 | |
| | 2.8741 | 10.2319 | 7500 | 0.3176 | 0.3321 | 0.2577 | |
| | 2.8961 | 10.9141 | 8000 | 0.3362 | 0.3309 | 0.2591 | |
| | 2.8826 | 11.5962 | 8500 | 0.3344 | 0.3272 | 0.2564 | |
| | 2.8922 | 12.2783 | 9000 | 0.3172 | 0.3359 | 0.2568 | |
| | 2.8963 | 12.9604 | 9500 | 0.3175 | 0.3228 | 0.2525 | |
| | 2.8683 | 13.6426 | 10000 | 0.2987 | 0.3147 | 0.2521 | |
| | 2.8781 | 14.3247 | 10500 | 0.2992 | 0.3222 | 0.2552 | |
| | 2.8693 | 15.0068 | 11000 | 0.2764 | 0.3099 | 0.2482 | |
| | 2.8676 | 15.6889 | 11500 | 0.3020 | 0.3140 | 0.2522 | |
| | 2.8953 | 16.3711 | 12000 | 0.2932 | 0.3080 | 0.2470 | |
| | 2.9023 | 17.0532 | 12500 | 0.2895 | 0.3075 | 0.2478 | |
| | 2.8665 | 17.7353 | 13000 | 0.2889 | 0.3098 | 0.2466 | |
| | 2.9208 | 18.4175 | 13500 | 0.2753 | 0.3114 | 0.2461 | |
| | 2.8623 | 19.0996 | 14000 | 0.2749 | 0.3077 | 0.2447 | |
| | 2.9092 | 19.7817 | 14500 | 0.2711 | 0.3066 | 0.2433 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 5.0.0 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
