---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: repo_name
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: timit_asr
      type: timit_asr
      config: clean
      split: None
      args: clean
    metrics:
    - type: wer
      value: 0.22107366825167116
      name: Wer
---

<!-- 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. -->

# repo_name

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5351
- Wer: 0.2211

## 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: 8
- eval_batch_size: 8
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 3.5252        | 1.0040  | 500   | 1.6991          | 0.9701 |
| 0.854         | 2.0080  | 1000  | 0.5187          | 0.4025 |
| 0.4211        | 3.0120  | 1500  | 0.4289          | 0.3326 |
| 0.2871        | 4.0161  | 2000  | 0.3947          | 0.2896 |
| 0.2266        | 5.0201  | 2500  | 0.4034          | 0.2881 |
| 0.1789        | 6.0241  | 3000  | 0.4833          | 0.2926 |
| 0.1638        | 7.0281  | 3500  | 0.4342          | 0.2776 |
| 0.15          | 8.0321  | 4000  | 0.4643          | 0.2750 |
| 0.1251        | 9.0361  | 4500  | 0.4449          | 0.2642 |
| 0.1064        | 10.0402 | 5000  | 0.4785          | 0.2578 |
| 0.0986        | 11.0442 | 5500  | 0.4480          | 0.2627 |
| 0.0883        | 12.0482 | 6000  | 0.4876          | 0.2603 |
| 0.0784        | 13.0522 | 6500  | 0.5100          | 0.2519 |
| 0.0721        | 14.0562 | 7000  | 0.4795          | 0.2536 |
| 0.0696        | 15.0602 | 7500  | 0.4797          | 0.2456 |
| 0.0598        | 16.0643 | 8000  | 0.5064          | 0.2410 |
| 0.0575        | 17.0683 | 8500  | 0.5075          | 0.2362 |
| 0.0508        | 18.0723 | 9000  | 0.5062          | 0.2420 |
| 0.048         | 19.0763 | 9500  | 0.5078          | 0.2397 |
| 0.0402        | 20.0803 | 10000 | 0.5511          | 0.2341 |
| 0.0429        | 21.0843 | 10500 | 0.4835          | 0.2330 |
| 0.0362        | 22.0884 | 11000 | 0.5800          | 0.2308 |
| 0.0333        | 23.0924 | 11500 | 0.5250          | 0.2306 |
| 0.0285        | 24.0964 | 12000 | 0.5242          | 0.2288 |
| 0.0296        | 25.1004 | 12500 | 0.4995          | 0.2238 |
| 0.0264        | 26.1044 | 13000 | 0.5296          | 0.2236 |
| 0.0245        | 27.1084 | 13500 | 0.5530          | 0.2233 |
| 0.0214        | 28.1124 | 14000 | 0.5376          | 0.2209 |
| 0.0214        | 29.1165 | 14500 | 0.5351          | 0.2211 |


### Framework versions

- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 2.21.0
- Tokenizers 0.22.1