---
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