metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: xls-r-uzbek-cv8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_8_0
type: common_voice_8_0
config: uz
split: validation[50%:]
args: uz
metrics:
- name: Wer
type: wer
value: 0.3785223774567843
xls-r-uzbek-cv8
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_8_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2927
- Wer: 0.3785
- Cer: 0.0760
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.1444 | 0.4055 | 500 | 3.1200 | 1.0 | 1.0 |
| 2.9488 | 0.8110 | 1000 | 2.9562 | 1.0 | 0.9807 |
| 1.4553 | 1.2165 | 1500 | 0.7868 | 0.7034 | 0.1644 |
| 1.1495 | 1.6221 | 2000 | 0.5598 | 0.6076 | 0.1337 |
| 1.041 | 2.0276 | 2500 | 0.4650 | 0.5537 | 0.1174 |
| 0.9524 | 2.4331 | 3000 | 0.4204 | 0.5098 | 0.1061 |
| 0.902 | 2.8386 | 3500 | 0.3919 | 0.4984 | 0.1026 |
| 0.8505 | 3.2441 | 4000 | 0.3688 | 0.4678 | 0.0965 |
| 0.8353 | 3.6496 | 4500 | 0.3491 | 0.4488 | 0.0915 |
| 0.8015 | 4.0552 | 5000 | 0.3410 | 0.4356 | 0.0896 |
| 0.7771 | 4.4607 | 5500 | 0.3367 | 0.4330 | 0.0883 |
| 0.7894 | 4.8662 | 6000 | 0.3274 | 0.4201 | 0.0858 |
| 0.7624 | 5.2717 | 6500 | 0.3266 | 0.4115 | 0.0835 |
| 0.7522 | 5.6772 | 7000 | 0.3172 | 0.4072 | 0.0825 |
| 0.7545 | 6.0827 | 7500 | 0.3096 | 0.4034 | 0.0817 |
| 0.7412 | 6.4882 | 8000 | 0.3062 | 0.4014 | 0.0810 |
| 0.7405 | 6.8938 | 8500 | 0.3057 | 0.3933 | 0.0796 |
| 0.703 | 7.2993 | 9000 | 0.2966 | 0.3894 | 0.0784 |
| 0.7091 | 7.7048 | 9500 | 0.3000 | 0.3895 | 0.0784 |
| 0.7117 | 8.1103 | 10000 | 0.2988 | 0.3881 | 0.0781 |
| 0.6871 | 8.5158 | 10500 | 0.2939 | 0.3832 | 0.0771 |
| 0.6942 | 8.9213 | 11000 | 0.2950 | 0.3816 | 0.0766 |
| 0.6919 | 9.3268 | 11500 | 0.2910 | 0.3781 | 0.0760 |
| 0.6756 | 9.7324 | 12000 | 0.2927 | 0.3785 | 0.0760 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1