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---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-tr-demo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: COMMON_VOICE - TR
type: common_voice
config: tr
split: test
args: 'Config: tr, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.493922990501481
---
<!-- 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-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5335
- Wer: 0.4939
## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.83 | 100 | 4.1084 | 1.0 |
| No log | 3.67 | 200 | 3.1519 | 1.0 |
| No log | 5.5 | 300 | 1.9348 | 0.9799 |
| No log | 7.34 | 400 | 0.7185 | 0.7490 |
| 3.6165 | 9.17 | 500 | 0.6041 | 0.6368 |
| 3.6165 | 11.01 | 600 | 0.5610 | 0.5771 |
| 3.6165 | 12.84 | 700 | 0.5292 | 0.5398 |
| 3.6165 | 14.68 | 800 | 0.5242 | 0.5083 |
| 3.6165 | 16.51 | 900 | 0.5443 | 0.5037 |
| 0.1894 | 18.35 | 1000 | 0.5314 | 0.4944 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.2
|