---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-darija
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-darija

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4265
- Wer: 0.4101
- Cer: 0.1327

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 10.5265       | 0.49  | 500   | 3.2082          | 1.0    | 0.9971 |
| 2.9629        | 0.98  | 1000  | 2.9619          | 1.0000 | 0.9357 |
| 2.6527        | 1.46  | 1500  | 1.4934          | 1.0974 | 0.4550 |
| 1.2946        | 1.95  | 2000  | 0.6904          | 0.7809 | 0.2566 |
| 0.9059        | 2.44  | 2500  | 0.5906          | 0.7002 | 0.2190 |
| 0.8133        | 2.93  | 3000  | 0.5199          | 0.6382 | 0.2049 |
| 0.7026        | 3.41  | 3500  | 0.4570          | 0.5986 | 0.1919 |
| 0.6572        | 3.9   | 4000  | 0.4238          | 0.5792 | 0.1848 |
| 0.5904        | 4.39  | 4500  | 0.4116          | 0.5637 | 0.1807 |
| 0.5593        | 4.88  | 5000  | 0.3850          | 0.5414 | 0.1734 |
| 0.5084        | 5.37  | 5500  | 0.3951          | 0.5409 | 0.1733 |
| 0.5033        | 5.85  | 6000  | 0.4449          | 0.5176 | 0.1649 |
| 0.4712        | 6.34  | 6500  | 0.5485          | 0.5172 | 0.1658 |
| 0.4459        | 6.83  | 7000  | 0.5259          | 0.5061 | 0.1623 |
| 0.4246        | 7.32  | 7500  | 0.3686          | 0.4991 | 0.1605 |
| 0.4261        | 7.8   | 8000  | 0.3663          | 0.4898 | 0.1589 |
| 0.4078        | 8.29  | 8500  | 0.3740          | 0.4858 | 0.1564 |
| 0.3783        | 8.78  | 9000  | 0.3907          | 0.4824 | 0.1566 |
| 0.3647        | 9.27  | 9500  | 0.3424          | 0.4750 | 0.1525 |
| 0.3527        | 9.76  | 10000 | 0.3444          | 0.4692 | 0.1513 |
| 0.3482        | 10.24 | 10500 | 0.3856          | 0.4692 | 0.1507 |
| 0.3338        | 10.73 | 11000 | 0.3650          | 0.4664 | 0.1512 |
| 0.3198        | 11.22 | 11500 | 0.3516          | 0.4628 | 0.1492 |
| 0.3218        | 11.71 | 12000 | 0.3660          | 0.4644 | 0.1491 |
| 0.3115        | 12.2  | 12500 | 0.3490          | 0.4545 | 0.1475 |
| 0.2977        | 12.68 | 13000 | 0.3555          | 0.4504 | 0.1451 |
| 0.2958        | 13.17 | 13500 | 0.3425          | 0.4571 | 0.1449 |
| 0.278         | 13.66 | 14000 | 0.4035          | 0.4520 | 0.1446 |
| 0.2716        | 14.15 | 14500 | 0.3552          | 0.4492 | 0.1437 |
| 0.2729        | 14.63 | 15000 | 0.3665          | 0.4470 | 0.1432 |
| 0.2691        | 15.12 | 15500 | 0.3700          | 0.4498 | 0.1444 |
| 0.2563        | 15.61 | 16000 | 0.3658          | 0.4423 | 0.1421 |
| 0.2511        | 16.1  | 16500 | 0.4152          | 0.4408 | 0.1425 |
| 0.2348        | 16.59 | 17000 | 0.4889          | 0.4375 | 0.1416 |
| 0.2437        | 17.07 | 17500 | 0.4209          | 0.4382 | 0.1413 |
| 0.2388        | 17.56 | 18000 | 0.6032          | 0.4359 | 0.1408 |
| 0.2235        | 18.05 | 18500 | 0.4831          | 0.4369 | 0.1402 |
| 0.2197        | 18.54 | 19000 | 0.4989          | 0.4345 | 0.1402 |
| 0.2285        | 19.02 | 19500 | 0.5929          | 0.4342 | 0.1393 |
| 0.2224        | 19.51 | 20000 | 0.4098          | 0.4317 | 0.1398 |
| 0.2183        | 20.0  | 20500 | 0.3547          | 0.4254 | 0.1384 |
| 0.2113        | 20.49 | 21000 | 0.3926          | 0.4324 | 0.1385 |
| 0.2125        | 20.98 | 21500 | 0.3982          | 0.4299 | 0.1386 |
| 0.201         | 21.46 | 22000 | 0.3929          | 0.4293 | 0.1389 |
| 0.2002        | 21.95 | 22500 | 0.4047          | 0.4218 | 0.1372 |
| 0.2029        | 22.44 | 23000 | 0.5153          | 0.4235 | 0.1375 |
| 0.195         | 22.93 | 23500 | 0.5601          | 0.4198 | 0.1364 |
| 0.182         | 23.41 | 24000 | 0.4596          | 0.4168 | 0.1355 |
| 0.1889        | 23.9  | 24500 | 0.4165          | 0.4209 | 0.1353 |
| 0.1795        | 24.39 | 25000 | 0.4096          | 0.4185 | 0.1352 |
| 0.1809        | 24.88 | 25500 | 0.4732          | 0.4126 | 0.1341 |
| 0.1762        | 25.37 | 26000 | 0.4324          | 0.4146 | 0.1347 |
| 0.1764        | 25.85 | 26500 | 0.4462          | 0.4160 | 0.1347 |
| 0.1805        | 26.34 | 27000 | 0.3955          | 0.4107 | 0.1333 |
| 0.1733        | 26.83 | 27500 | 0.4182          | 0.4135 | 0.1336 |
| 0.1651        | 27.32 | 28000 | 0.4111          | 0.4104 | 0.1330 |
| 0.1713        | 27.8  | 28500 | 0.4426          | 0.4126 | 0.1332 |
| 0.1766        | 28.29 | 29000 | 0.4426          | 0.4085 | 0.1328 |
| 0.1631        | 28.78 | 29500 | 0.4248          | 0.4083 | 0.1328 |
| 0.1608        | 29.27 | 30000 | 0.4334          | 0.4096 | 0.1327 |
| 0.1688        | 29.76 | 30500 | 0.4265          | 0.4101 | 0.1327 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1