---
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 |


### Framework versions

- Transformers 5.10.2
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2