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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xlsr-mk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: mk
split: validation
args: mk
metrics:
- name: Wer
type: wer
value: 0.4437212531458821
---
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# xlsr-mk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6273
- Wer: 0.4437
- Cer: 0.1074
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 3.541 | 1.8868 | 100 | 3.5532 | 1.0 | 1.0 |
| 2.966 | 3.7736 | 200 | 2.9438 | 1.0 | 1.0 |
| 2.298 | 5.6604 | 300 | 2.1673 | 1.0 | 0.7080 |
| 0.5999 | 7.5472 | 400 | 0.7521 | 0.7476 | 0.2035 |
| 0.3941 | 9.4340 | 500 | 0.7249 | 0.6911 | 0.1845 |
| 0.2226 | 11.3208 | 600 | 0.6970 | 0.6602 | 0.1725 |
| 0.3031 | 13.2075 | 700 | 0.7692 | 0.6506 | 0.1680 |
| 0.1621 | 15.0943 | 800 | 0.7229 | 0.6232 | 0.1583 |
| 0.2052 | 16.9811 | 900 | 0.6990 | 0.5722 | 0.1471 |
| 0.1441 | 18.8679 | 1000 | 0.6829 | 0.5591 | 0.1400 |
| 0.0548 | 20.7547 | 1100 | 0.6560 | 0.5309 | 0.1333 |
| 0.1312 | 22.6415 | 1200 | 0.6590 | 0.5375 | 0.1332 |
| 0.0582 | 24.5283 | 1300 | 0.7023 | 0.5268 | 0.1321 |
| 0.1163 | 26.4151 | 1400 | 0.6900 | 0.5170 | 0.1293 |
| 0.0491 | 28.3019 | 1500 | 0.6499 | 0.5089 | 0.1274 |
| 0.063 | 30.1887 | 1600 | 0.6478 | 0.4869 | 0.1221 |
| 0.0735 | 32.0755 | 1700 | 0.6678 | 0.4967 | 0.1256 |
| 0.0437 | 33.9623 | 1800 | 0.6651 | 0.4803 | 0.1188 |
| 0.0514 | 35.8491 | 1900 | 0.6741 | 0.4724 | 0.1168 |
| 0.0306 | 37.7358 | 2000 | 0.6564 | 0.4717 | 0.1168 |
| 0.0458 | 39.6226 | 2100 | 0.6428 | 0.4679 | 0.1140 |
| 0.0398 | 41.5094 | 2200 | 0.6385 | 0.4531 | 0.1103 |
| 0.0574 | 43.3962 | 2300 | 0.5991 | 0.4392 | 0.1063 |
| 0.0481 | 45.2830 | 2400 | 0.6394 | 0.4468 | 0.1087 |
| 0.0376 | 47.1698 | 2500 | 0.6184 | 0.4434 | 0.1072 |
| 0.0275 | 49.0566 | 2600 | 0.6273 | 0.4437 | 0.1074 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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