---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-arabic-demo-17-july
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: None
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.03302203188689954
---

<!-- 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. -->

# wav2vec2-large-xlsr-arabic-demo-17-july

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0192
- Wer: 0.0330

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 7.5233        | 0.3300  | 100  | 5.9510          | 1.0    |
| 3.4           | 0.6601  | 200  | 3.3562          | 1.0    |
| 2.8077        | 0.9901  | 300  | 2.7972          | 1.0    |
| 1.3772        | 1.3201  | 400  | 1.0323          | 0.8612 |
| 0.6436        | 1.6502  | 500  | 0.3189          | 0.4047 |
| 0.4996        | 1.9802  | 600  | 0.1811          | 0.3562 |
| 0.4313        | 2.3102  | 700  | 0.2062          | 0.3067 |
| 0.4207        | 2.6403  | 800  | 0.2273          | 0.3495 |
| 0.4021        | 2.9703  | 900  | 0.1336          | 0.2045 |
| 0.339         | 3.3003  | 1000 | 0.1281          | 0.1873 |
| 0.3848        | 3.6304  | 1100 | 0.1254          | 0.1969 |
| 0.33          | 3.9604  | 1200 | 0.1392          | 0.2297 |
| 0.3039        | 4.2904  | 1300 | 0.1041          | 0.1704 |
| 0.287         | 4.6205  | 1400 | 0.1037          | 0.1818 |
| 0.2526        | 4.9505  | 1500 | 0.1156          | 0.2215 |
| 0.2806        | 5.2805  | 1600 | 0.1125          | 0.1780 |
| 0.2742        | 5.6106  | 1700 | 0.0892          | 0.1472 |
| 0.2715        | 5.9406  | 1800 | 0.0884          | 0.1540 |
| 0.2282        | 6.2706  | 1900 | 0.0906          | 0.1569 |
| 0.2558        | 6.6007  | 2000 | 0.0894          | 0.1505 |
| 0.2267        | 6.9307  | 2100 | 0.0889          | 0.1927 |
| 0.2188        | 7.2607  | 2200 | 0.0796          | 0.1318 |
| 0.2347        | 7.5908  | 2300 | 0.1093          | 0.1779 |
| 0.223         | 7.9208  | 2400 | 0.0802          | 0.1305 |
| 0.2069        | 8.2508  | 2500 | 0.0831          | 0.1381 |
| 0.1939        | 8.5809  | 2600 | 0.0748          | 0.1252 |
| 0.1857        | 8.9109  | 2700 | 0.0691          | 0.1182 |
| 0.1671        | 9.2409  | 2800 | 0.0628          | 0.1009 |
| 0.1822        | 9.5710  | 2900 | 0.0687          | 0.1101 |
| 0.1823        | 9.9010  | 3000 | 0.0642          | 0.1048 |
| 0.2006        | 10.2310 | 3100 | 0.0821          | 0.1360 |
| 0.1686        | 10.5611 | 3200 | 0.0750          | 0.1175 |
| 0.1702        | 10.8911 | 3300 | 0.0810          | 0.1366 |
| 0.1824        | 11.2211 | 3400 | 0.1038          | 0.1736 |
| 0.1794        | 11.5512 | 3500 | 0.0711          | 0.1145 |
| 0.1834        | 11.8812 | 3600 | 0.0695          | 0.1130 |
| 0.1601        | 12.2112 | 3700 | 0.0620          | 0.1000 |
| 0.138         | 12.5413 | 3800 | 0.0593          | 0.1074 |
| 0.1394        | 12.8713 | 3900 | 0.0542          | 0.0873 |
| 0.1636        | 13.2013 | 4000 | 0.0661          | 0.1210 |
| 0.1272        | 13.5314 | 4100 | 0.0575          | 0.0966 |
| 0.1428        | 13.8614 | 4200 | 0.0665          | 0.0992 |
| 0.2048        | 14.1914 | 4300 | 0.0592          | 0.1110 |
| 0.1789        | 14.5215 | 4400 | 0.0544          | 0.0892 |
| 0.181         | 14.8515 | 4500 | 0.0534          | 0.0934 |
| 0.2337        | 15.1815 | 4600 | 0.0560          | 0.0931 |
| 0.2261        | 15.5116 | 4700 | 0.0565          | 0.0985 |
| 0.2106        | 15.8416 | 4800 | 0.0473          | 0.0808 |
| 0.2219        | 16.1716 | 4900 | 0.0520          | 0.0875 |
| 0.2647        | 16.5017 | 5000 | 0.0607          | 0.0962 |
| 0.2376        | 16.8317 | 5100 | 0.0500          | 0.0859 |
| 0.2211        | 17.1617 | 5200 | 0.0478          | 0.0752 |
| 0.229         | 17.4917 | 5300 | 0.0574          | 0.0928 |
| 0.1958        | 17.8218 | 5400 | 0.0481          | 0.0772 |
| 0.1646        | 18.1518 | 5500 | 0.0432          | 0.0715 |
| 0.1949        | 18.4818 | 5600 | 0.0483          | 0.0787 |
| 0.2152        | 18.8119 | 5700 | 0.0489          | 0.0822 |
| 0.1749        | 19.1419 | 5800 | 0.0422          | 0.0746 |
| 0.1663        | 19.4719 | 5900 | 0.0421          | 0.0766 |
| 0.1684        | 19.8020 | 6000 | 0.0367          | 0.0592 |
| 0.1484        | 20.1320 | 6100 | 0.0374          | 0.0599 |
| 0.1501        | 20.4620 | 6200 | 0.0358          | 0.0613 |
| 0.1426        | 20.7921 | 6300 | 0.0388          | 0.0650 |
| 0.1422        | 21.1221 | 6400 | 0.0355          | 0.0594 |
| 0.1343        | 21.4521 | 6500 | 0.0352          | 0.0585 |
| 0.1275        | 21.7822 | 6600 | 0.0305          | 0.0511 |
| 0.1235        | 22.1122 | 6700 | 0.0329          | 0.0562 |
| 0.1033        | 22.4422 | 6800 | 0.0301          | 0.0499 |
| 0.1064        | 22.7723 | 6900 | 0.0310          | 0.0502 |
| 0.1173        | 23.1023 | 7000 | 0.0280          | 0.0479 |
| 0.114         | 23.4323 | 7100 | 0.0310          | 0.0501 |
| 0.1183        | 23.7624 | 7200 | 0.0293          | 0.0499 |
| 0.0891        | 24.0924 | 7300 | 0.0287          | 0.0488 |
| 0.091         | 24.4224 | 7400 | 0.0273          | 0.0473 |
| 0.1043        | 24.7525 | 7500 | 0.0269          | 0.0467 |
| 0.0887        | 25.0825 | 7600 | 0.0253          | 0.0434 |
| 0.0806        | 25.4125 | 7700 | 0.0253          | 0.0448 |
| 0.0902        | 25.7426 | 7800 | 0.0244          | 0.0414 |
| 0.0705        | 26.0726 | 7900 | 0.0239          | 0.0402 |
| 0.0885        | 26.4026 | 8000 | 0.0226          | 0.0364 |
| 0.0889        | 26.7327 | 8100 | 0.0230          | 0.0395 |
| 0.0835        | 27.0627 | 8200 | 0.0222          | 0.0373 |
| 0.0762        | 27.3927 | 8300 | 0.0216          | 0.0362 |
| 0.0609        | 27.7228 | 8400 | 0.0211          | 0.0360 |
| 0.0592        | 28.0528 | 8500 | 0.0204          | 0.0353 |
| 0.0683        | 28.3828 | 8600 | 0.0212          | 0.0363 |
| 0.072         | 28.7129 | 8700 | 0.0198          | 0.0341 |
| 0.0653        | 29.0429 | 8800 | 0.0198          | 0.0338 |
| 0.0588        | 29.3729 | 8900 | 0.0193          | 0.0333 |
| 0.065         | 29.7030 | 9000 | 0.0192          | 0.0330 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1