---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: MSP-VSR
  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-VSR

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: 1.2109
- Wer: 0.6198

## 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: 50000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.6988        | 0.02  | 1000  | 2.5501          | 0.9915 |
| 2.0297        | 0.04  | 2000  | 1.7684          | 0.8450 |
| 1.9479        | 0.06  | 3000  | 1.6473          | 0.8139 |
| 1.9878        | 0.08  | 4000  | 1.5835          | 0.7850 |
| 2.0281        | 0.1   | 5000  | 1.5461          | 0.7615 |
| 1.9042        | 0.12  | 6000  | 1.4989          | 0.7539 |
| 1.9134        | 0.14  | 7000  | 1.4811          | 0.7418 |
| 1.9830        | 0.16  | 8000  | 1.4620          | 0.7214 |
| 1.9236        | 0.18  | 9000  | 1.4404          | 0.7128 |
| 1.8155        | 0.2   | 10000 | 1.4440          | 0.7099 |
| 1.8339        | 0.22  | 11000 | 1.4199          | 0.7160 |
| 1.7363        | 0.24  | 12000 | 1.4074          | 0.7001 |
| 1.9054        | 0.26  | 13000 | 1.3908          | 0.6984 |
| 1.8643        | 0.28  | 14000 | 1.3891          | 0.6907 |
| 1.6696        | 0.3   | 15000 | 1.3644          | 0.6904 |
| 1.6995        | 0.32  | 16000 | 1.3502          | 0.6964 |
| 1.7230        | 0.34  | 17000 | 1.3431          | 0.6806 |
| 1.7547        | 0.36  | 18000 | 1.3435          | 0.6762 |
| 1.6977        | 0.38  | 19000 | 1.3134          | 0.6728 |
| 1.6884        | 0.4   | 20000 | 1.3223          | 0.6699 |
| 1.7758        | 0.42  | 21000 | 1.2926          | 0.6814 |
| 1.8115        | 0.44  | 22000 | 1.2995          | 0.6636 |
| 1.7980        | 0.46  | 23000 | 1.2844          | 0.6669 |
| 1.7737        | 0.48  | 24000 | 1.3020          | 0.6591 |
| 1.6655        | 0.5   | 25000 | 1.2727          | 0.6544 |
| 1.7493        | 0.52  | 26000 | 1.2830          | 0.6547 |
| 1.7016        | 0.54  | 27000 | 1.2820          | 0.6497 |
| 1.6763        | 0.56  | 28000 | 1.2767          | 0.6473 |
| 1.7027        | 0.58  | 29000 | 1.2730          | 0.6454 |
| 1.7984        | 0.6   | 30000 | 1.2610          | 0.6471 |
| 1.7301        | 0.62  | 31000 | 1.2475          | 0.6438 |
| 1.7133        | 0.64  | 32000 | 1.2569          | 0.6342 |
| 1.6079        | 0.66  | 33000 | 1.2468          | 0.6314 |
| 1.8220        | 0.68  | 34000 | 1.2299          | 0.6355 |
| 1.6110        | 0.7   | 35000 | 1.2369          | 0.6310 |
| 1.6863        | 0.72  | 36000 | 1.2286          | 0.6357 |
| 1.5938        | 0.74  | 37000 | 1.2285          | 0.6288 |
| 1.7096        | 0.76  | 38000 | 1.2211          | 0.6266 |
| 1.5388        | 0.78  | 39000 | 1.2222          | 0.6246 |
| 1.6024        | 0.8   | 40000 | 1.2288          | 0.6268 |
| 1.5912        | 0.82  | 41000 | 1.2150          | 0.6285 |
| 1.6931        | 0.84  | 42000 | 1.2121          | 0.6220 |
| 1.6600        | 0.86  | 43000 | 1.2134          | 0.6246 |
| 1.6601        | 0.88  | 44000 | 1.2062          | 0.6222 |
| 1.6800        | 0.9   | 45000 | 1.2109          | 0.6198 |
| 1.5803        | 0.92  | 46000 | 1.2080          | 0.6222 |
| 1.6128        | 0.94  | 47000 | 1.2034          | 0.6216 |
| 1.5225        | 0.96  | 48000 | 1.2063          | 0.6210 |
| 1.6296        | 0.98  | 49000 | 1.2091          | 0.6219 |
| 1.6504        | 1.0   | 50000 | 1.2102          | 0.6214 |


### Framework versions

- Transformers 5.10.2
- Pytorch 2.10.0+rocm7.2.4.git3d3aa833
- Datasets 4.0.0
- Tokenizers 0.22.2