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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: w2v2-lmk_augmented
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 0.4878048780487805
---
<!-- 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. -->
# w2v2-lmk_augmented
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: 1.2406
- Wer: 0.4878
- Cer: 0.1858
## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 8.5498 | 2.7123 | 100 | 4.0528 | 1.0 | 1.0 |
| 3.1716 | 5.4110 | 200 | 2.9634 | 1.0 | 1.0 |
| 2.9756 | 8.1096 | 300 | 2.8924 | 1.0 | 1.0 |
| 2.8279 | 10.8219 | 400 | 2.5968 | 1.0 | 1.0 |
| 2.2866 | 13.5205 | 500 | 1.7827 | 0.9895 | 0.6283 |
| 1.619 | 16.2192 | 600 | 1.3242 | 0.9443 | 0.4021 |
| 1.2926 | 18.9315 | 700 | 1.1299 | 0.7875 | 0.2833 |
| 1.0181 | 21.6301 | 800 | 1.1390 | 0.6585 | 0.2513 |
| 0.8774 | 24.3288 | 900 | 1.0760 | 0.6132 | 0.2338 |
| 0.7471 | 27.0274 | 1000 | 0.9959 | 0.5889 | 0.2155 |
| 0.6542 | 29.7397 | 1100 | 1.0575 | 0.5575 | 0.2117 |
| 0.5632 | 32.4384 | 1200 | 1.0240 | 0.5784 | 0.2171 |
| 0.4834 | 35.1370 | 1300 | 1.0971 | 0.5505 | 0.1912 |
| 0.4716 | 37.8493 | 1400 | 1.1336 | 0.5749 | 0.2056 |
| 0.45 | 40.5479 | 1500 | 1.0703 | 0.5679 | 0.2079 |
| 0.394 | 43.2466 | 1600 | 1.1579 | 0.5645 | 0.2178 |
| 0.3588 | 45.9589 | 1700 | 1.0555 | 0.5296 | 0.1896 |
| 0.3217 | 48.6575 | 1800 | 1.2323 | 0.5575 | 0.2102 |
| 0.3245 | 51.3562 | 1900 | 1.1639 | 0.5401 | 0.2018 |
| 0.289 | 54.0548 | 2000 | 1.1304 | 0.5122 | 0.1927 |
| 0.28 | 56.7671 | 2100 | 1.2295 | 0.5296 | 0.2003 |
| 0.2521 | 59.4658 | 2200 | 1.1612 | 0.5226 | 0.1950 |
| 0.2624 | 62.1644 | 2300 | 1.1982 | 0.5157 | 0.2003 |
| 0.2402 | 64.8767 | 2400 | 1.2075 | 0.5296 | 0.1988 |
| 0.2258 | 67.5753 | 2500 | 1.2091 | 0.5366 | 0.2003 |
| 0.2232 | 70.2740 | 2600 | 1.1830 | 0.5296 | 0.1957 |
| 0.2181 | 72.9863 | 2700 | 1.2001 | 0.5157 | 0.1942 |
| 0.2214 | 75.6849 | 2800 | 1.1942 | 0.5052 | 0.1889 |
| 0.1752 | 78.3836 | 2900 | 1.1873 | 0.5087 | 0.1896 |
| 0.1891 | 81.0822 | 3000 | 1.2159 | 0.5192 | 0.1927 |
| 0.1733 | 83.7945 | 3100 | 1.2105 | 0.5017 | 0.1881 |
| 0.1982 | 86.4932 | 3200 | 1.2331 | 0.5087 | 0.1874 |
| 0.1681 | 89.1918 | 3300 | 1.1848 | 0.4808 | 0.1790 |
| 0.1631 | 91.9041 | 3400 | 1.2273 | 0.4878 | 0.1858 |
| 0.1579 | 94.6027 | 3500 | 1.2334 | 0.4948 | 0.1843 |
| 0.1795 | 97.3014 | 3600 | 1.2399 | 0.4878 | 0.1851 |
| 0.1592 | 100.0 | 3700 | 1.2406 | 0.4878 | 0.1858 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 3.0.0
- Tokenizers 0.22.1
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