pytorch tensorboard wav2vec2 automatic-speech-recognition dataset:common_voice transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
  results: []
---

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# wav2vec2-large-xls-r-300m-turkish-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3965
- Wer: 0.3807

## 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: 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.974         | 3.67  | 400  | 0.7102          | 0.7318 |
| 0.4216        | 7.34  | 800  | 0.4273          | 0.4941 |
| 0.1891        | 11.01 | 1200 | 0.4548          | 0.4864 |
| 0.1267        | 14.68 | 1600 | 0.4208          | 0.4082 |
| 0.0958        | 18.35 | 2000 | 0.4236          | 0.4033 |
| 0.0799        | 22.02 | 2400 | 0.4052          | 0.3829 |
| 0.0624        | 25.69 | 2800 | 0.4088          | 0.3875 |
| 0.0491        | 29.36 | 3200 | 0.3965          | 0.3807 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
