---
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 |


### Framework versions

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