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---
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: outputs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: tr
split: train+validation
args: tr
metrics:
- name: Wer
type: wer
value: 0.35818608926565215
---
<!-- 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. -->
# outputs
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3878
- Wer: 0.3582
## 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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7391 | 0.92 | 100 | 3.5760 | 1.0 |
| 2.927 | 1.83 | 200 | 3.0796 | 0.9999 |
| 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 |
| 0.6529 | 3.67 | 400 | 0.5926 | 0.6367 |
| 0.3623 | 4.59 | 500 | 0.5372 | 0.5692 |
| 0.2888 | 5.5 | 600 | 0.4407 | 0.4838 |
| 0.285 | 6.42 | 700 | 0.4341 | 0.4694 |
| 0.0842 | 7.34 | 800 | 0.4153 | 0.4302 |
| 0.1415 | 8.26 | 900 | 0.4317 | 0.4136 |
| 0.1552 | 9.17 | 1000 | 0.4145 | 0.4013 |
| 0.1184 | 10.09 | 1100 | 0.4115 | 0.3844 |
| 0.0556 | 11.01 | 1200 | 0.4182 | 0.3862 |
| 0.0851 | 11.93 | 1300 | 0.3985 | 0.3688 |
| 0.0961 | 12.84 | 1400 | 0.4030 | 0.3665 |
| 0.0596 | 13.76 | 1500 | 0.3880 | 0.3631 |
| 0.0917 | 14.68 | 1600 | 0.3878 | 0.3582 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
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