---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: timit-xls-r-300m
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: timit_asr
      type: timit_asr
      config: clean
      split: None
      args: clean
    metrics:
    - name: Wer
      type: wer
      value: 0.2466404796361381
---

<!-- 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. -->

# timit-xls-r-300m

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4457
- Wer: 0.2466

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0049        | 1.72  | 500   | 1.9735          | 1.0655 |
| 1.033         | 3.45  | 1000  | 0.6172          | 0.5115 |
| 0.4499        | 5.17  | 1500  | 0.5231          | 0.4395 |
| 0.2551        | 6.9   | 2000  | 0.4768          | 0.3772 |
| 0.1724        | 8.62  | 2500  | 0.4699          | 0.3626 |
| 0.133         | 10.34 | 3000  | 0.4346          | 0.3329 |
| 0.1082        | 12.07 | 3500  | 0.4479          | 0.3163 |
| 0.0886        | 13.79 | 4000  | 0.4393          | 0.3167 |
| 0.0766        | 15.52 | 4500  | 0.4920          | 0.3100 |
| 0.0637        | 17.24 | 5000  | 0.4510          | 0.3013 |
| 0.0607        | 18.97 | 5500  | 0.4284          | 0.2808 |
| 0.0495        | 20.69 | 6000  | 0.4270          | 0.2820 |
| 0.0479        | 22.41 | 6500  | 0.4294          | 0.2852 |
| 0.0444        | 24.14 | 7000  | 0.4456          | 0.2816 |
| 0.0378        | 25.86 | 7500  | 0.4236          | 0.2763 |
| 0.0325        | 27.59 | 8000  | 0.4365          | 0.2849 |
| 0.031         | 29.31 | 8500  | 0.4482          | 0.2862 |
| 0.0285        | 31.03 | 9000  | 0.4388          | 0.2691 |
| 0.0252        | 32.76 | 9500  | 0.4253          | 0.2692 |
| 0.0229        | 34.48 | 10000 | 0.4598          | 0.2641 |
| 0.0223        | 36.21 | 10500 | 0.4462          | 0.2533 |
| 0.0188        | 37.93 | 11000 | 0.4350          | 0.2673 |
| 0.0163        | 39.66 | 11500 | 0.4460          | 0.2608 |
| 0.0167        | 41.38 | 12000 | 0.4441          | 0.2683 |
| 0.0138        | 43.1  | 12500 | 0.4290          | 0.2528 |
| 0.0127        | 44.83 | 13000 | 0.4360          | 0.2508 |
| 0.0124        | 46.55 | 13500 | 0.4406          | 0.2511 |
| 0.0107        | 48.28 | 14000 | 0.4482          | 0.2477 |
| 0.0108        | 50.0  | 14500 | 0.4457          | 0.2466 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2