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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-common_voice_13_0-eo-10_1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: eo
split: validation
args: eo
metrics:
- name: Wer
type: wer
value: 0.053735309652713587
---
<!-- 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. -->
# wav2vec2-common_voice_13_0-eo-10_1
This model is a fine-tuned version of [xekri/wav2vec2-common_voice_13_0-eo-10](https://huggingface.co/xekri/wav2vec2-common_voice_13_0-eo-10) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0391
- Cer: 0.0098
- Wer: 0.0537
## 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: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.1142 | 0.22 | 1000 | 0.0483 | 0.0126 | 0.0707 |
| 0.1049 | 0.44 | 2000 | 0.0474 | 0.0123 | 0.0675 |
| 0.0982 | 0.67 | 3000 | 0.0471 | 0.0120 | 0.0664 |
| 0.092 | 0.89 | 4000 | 0.0459 | 0.0117 | 0.0640 |
| 0.0847 | 1.11 | 5000 | 0.0459 | 0.0115 | 0.0631 |
| 0.0837 | 1.33 | 6000 | 0.0453 | 0.0113 | 0.0624 |
| 0.0803 | 1.56 | 7000 | 0.0443 | 0.0109 | 0.0598 |
| 0.0826 | 1.78 | 8000 | 0.0441 | 0.0110 | 0.0604 |
| 0.0809 | 2.0 | 9000 | 0.0437 | 0.0110 | 0.0605 |
| 0.0728 | 2.22 | 10000 | 0.0451 | 0.0109 | 0.0597 |
| 0.0707 | 2.45 | 11000 | 0.0444 | 0.0108 | 0.0591 |
| 0.0698 | 2.67 | 12000 | 0.0442 | 0.0105 | 0.0576 |
| 0.0981 | 2.89 | 13000 | 0.0411 | 0.0104 | 0.0572 |
| 0.0928 | 3.11 | 14000 | 0.0413 | 0.0102 | 0.0561 |
| 0.0927 | 3.34 | 15000 | 0.0410 | 0.0102 | 0.0565 |
| 0.0886 | 3.56 | 16000 | 0.0402 | 0.0102 | 0.0558 |
| 0.091 | 3.78 | 17000 | 0.0400 | 0.0101 | 0.0553 |
| 0.0888 | 4.0 | 18000 | 0.0398 | 0.0100 | 0.0546 |
| 0.0885 | 4.23 | 19000 | 0.0395 | 0.0099 | 0.0542 |
| 0.0869 | 4.45 | 20000 | 0.0394 | 0.0099 | 0.0540 |
| 0.0844 | 4.67 | 21000 | 0.0393 | 0.0098 | 0.0539 |
| 0.0882 | 4.89 | 22000 | 0.0391 | 0.0098 | 0.0537 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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