| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: MSP-AVSR |
| 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-AVSR |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1193 |
| - Wer: 0.1736 |
|
|
| ## 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: 30000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:------:|:-----:|:---------------:|:------:| |
| | 2.2821 | 0.0333 | 1000 | 1.5690 | 0.2983 | |
| | 2.1664 | 0.0667 | 2000 | 1.3053 | 0.2234 | |
| | 2.0936 | 0.1 | 3000 | 1.1193 | 0.1736 | |
| | 2.2092 | 0.1333 | 4000 | 1.2822 | 0.2420 | |
| | 2.3215 | 0.1667 | 5000 | 1.4375 | 0.2676 | |
| | 2.0802 | 0.2 | 6000 | 1.3366 | 0.2295 | |
| | 2.0530 | 0.2333 | 7000 | 1.2210 | 0.1985 | |
| | 2.1923 | 0.2667 | 8000 | 1.3655 | 0.2465 | |
| | 2.1158 | 0.3 | 9000 | 1.4065 | 0.2484 | |
| | 2.0377 | 0.3333 | 10000 | 1.4430 | 0.2271 | |
| | 2.0040 | 0.3667 | 11000 | 1.3231 | 0.2188 | |
| | 1.9140 | 0.4 | 12000 | 1.5497 | 0.2668 | |
| | 2.1310 | 0.4333 | 13000 | 1.1773 | 0.1909 | |
| | 2.0774 | 0.4667 | 14000 | 1.1900 | 0.1888 | |
| | 1.8577 | 0.5 | 15000 | 1.4826 | 0.2567 | |
| | 1.8542 | 0.5333 | 16000 | 1.3038 | 0.2196 | |
| | 1.9256 | 0.5667 | 17000 | 1.1100 | 0.1767 | |
| | 2.0081 | 0.6 | 18000 | 1.3671 | 0.2258 | |
| | 1.8983 | 0.6333 | 19000 | 1.2541 | 0.2035 | |
| | 1.8399 | 0.6667 | 20000 | 1.2768 | 0.2127 | |
| | 1.9230 | 0.0333 | 21000 | 1.3529 | 0.2279 | |
| | 1.7808 | 0.0667 | 22000 | 1.3944 | 0.2262 | |
| | 1.8058 | 0.1 | 23000 | 1.2840 | 0.2044 | |
| | 1.8971 | 0.1333 | 24000 | 1.2563 | 0.2056 | |
| | 2.0928 | 0.1667 | 25000 | 1.2498 | 0.2054 | |
| | 1.8353 | 0.2 | 26000 | 1.2187 | 0.1997 | |
| | 1.8451 | 0.2333 | 27000 | 1.2304 | 0.1999 | |
| | 2.0854 | 0.2667 | 28000 | 1.2552 | 0.2079 | |
| | 1.9424 | 0.3 | 29000 | 1.2496 | 0.2042 | |
| | 1.8996 | 0.3333 | 30000 | 1.2481 | 0.2046 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.10.2 |
| - Pytorch 2.10.0+rocm7.2.4.git3d3aa833 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
