---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xtreme_s_xlsr_300m_fleurs_asr_western_european_nomask
  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. -->

# xtreme_s_xlsr_300m_fleurs_asr_western_european_nomask

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2572
- Wer: 0.6278
- Cer: 0.2394

## 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: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.1411        | 0.49  | 500   | 3.1673          | 1.0    | 1.0    |
| 0.6397        | 0.97  | 1000  | 0.9039          | 0.7171 | 0.2862 |
| 0.4033        | 1.46  | 1500  | 0.8914          | 0.6862 | 0.2763 |
| 0.3473        | 1.94  | 2000  | 0.8017          | 0.6505 | 0.2536 |
| 0.3143        | 2.43  | 2500  | 0.8568          | 0.6566 | 0.2627 |
| 0.3004        | 2.91  | 3000  | 0.8898          | 0.6640 | 0.2686 |
| 0.282         | 3.4   | 3500  | 0.8489          | 0.6637 | 0.2571 |
| 0.2489        | 3.88  | 4000  | 0.8955          | 0.6744 | 0.2691 |
| 0.1706        | 4.37  | 4500  | 0.9190          | 0.6788 | 0.2688 |
| 0.3336        | 4.85  | 5000  | 0.8915          | 0.6594 | 0.2572 |
| 0.1426        | 5.34  | 5500  | 0.9501          | 0.6784 | 0.2686 |
| 0.2301        | 5.83  | 6000  | 1.0217          | 0.6719 | 0.2735 |
| 0.1325        | 6.31  | 6500  | 0.9578          | 0.6691 | 0.2655 |
| 0.1145        | 6.8   | 7000  | 0.9129          | 0.6680 | 0.2593 |
| 0.1202        | 7.28  | 7500  | 0.9646          | 0.6749 | 0.2619 |
| 0.143         | 7.77  | 8000  | 0.9200          | 0.6554 | 0.2554 |
| 0.1012        | 8.25  | 8500  | 0.9553          | 0.6787 | 0.2628 |
| 0.1018        | 8.74  | 9000  | 0.9455          | 0.6445 | 0.2511 |
| 0.1148        | 9.22  | 9500  | 1.0206          | 0.6725 | 0.2629 |
| 0.0794        | 9.71  | 10000 | 0.9305          | 0.6547 | 0.2526 |
| 0.2891        | 10.19 | 10500 | 1.0424          | 0.6709 | 0.2570 |
| 0.1665        | 10.68 | 11000 | 0.9760          | 0.6596 | 0.2507 |
| 0.1956        | 11.17 | 11500 | 0.9549          | 0.6340 | 0.2440 |
| 0.0828        | 11.65 | 12000 | 0.9598          | 0.6403 | 0.2460 |
| 0.059         | 12.14 | 12500 | 0.9972          | 0.6574 | 0.2531 |
| 0.0505        | 12.62 | 13000 | 0.9836          | 0.6534 | 0.2525 |
| 0.0336        | 13.11 | 13500 | 1.0619          | 0.6564 | 0.2519 |
| 0.0435        | 13.59 | 14000 | 1.0844          | 0.6480 | 0.2543 |
| 0.0216        | 14.08 | 14500 | 1.1084          | 0.6512 | 0.2521 |
| 0.0265        | 14.56 | 15000 | 1.1152          | 0.6607 | 0.2563 |
| 0.0975        | 15.05 | 15500 | 1.1060          | 0.6456 | 0.2471 |
| 0.1396        | 15.53 | 16000 | 1.1100          | 0.6337 | 0.2418 |
| 0.0701        | 16.02 | 16500 | 1.1731          | 0.6309 | 0.2415 |
| 0.1171        | 16.5  | 17000 | 1.1302          | 0.6315 | 0.2396 |
| 0.0778        | 16.99 | 17500 | 1.1485          | 0.6379 | 0.2447 |
| 0.0642        | 17.48 | 18000 | 1.2009          | 0.6400 | 0.2464 |
| 0.0322        | 17.96 | 18500 | 1.2028          | 0.6357 | 0.2425 |
| 0.031         | 18.45 | 19000 | 1.2381          | 0.6285 | 0.2416 |
| 0.0579        | 18.93 | 19500 | 1.2299          | 0.6265 | 0.2409 |
| 0.0628        | 19.42 | 20000 | 1.2582          | 0.6277 | 0.2395 |
| 0.074         | 19.9  | 20500 | 1.2572          | 0.6278 | 0.2394 |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.1+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6