---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: asr-300m-turkish
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: tr
      split: test
      args: tr
    metrics:
    - name: Wer
      type: wer
      value: 0.3879072617710142
---

<!-- 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. [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) -->

# asr-300m-turkish

This model is a fine-tuned version of wav2vec2 xls-r 300M on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4060
- Wer: 0.3879

## Model description

The following datasets were used for finetuning:
 - [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) All `validated` split except `test` split was used for training.
 - [MediaSpeech](https://www.openslr.org/108/)

## Intended uses & limitations

More information needed

## Training and evaluation data

The following datasets were used for finetuning:

Common Voice 7.0 TR All validated split except test split was used for training.
MediaSpeech


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9649        | 4.26  | 400  | 0.7179          | 0.7544 |
| 0.3912        | 8.51  | 800  | 0.4798          | 0.5432 |
| 0.1848        | 12.77 | 1200 | 0.4588          | 0.4792 |
| 0.1277        | 17.02 | 1600 | 0.4676          | 0.4510 |
| 0.0923        | 21.28 | 2000 | 0.4251          | 0.4218 |
| 0.07          | 25.53 | 2400 | 0.4164          | 0.4006 |
| 0.0546        | 29.79 | 2800 | 0.4060          | 0.3879 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3