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---
language:
- tr
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-phoneme-300m-tr
results: []
---
<!-- 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-xls-r-phoneme-300m-tr
This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6380
- PER: 0.1664
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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 | PER |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13.6687 | 0.92 | 100 | 12.4567 | 1.0 |
| 3.4219 | 1.83 | 200 | 3.4704 | 1.0 |
| 3.1846 | 2.75 | 300 | 3.2281 | 0.9935 |
| 2.0076 | 3.67 | 400 | 1.7415 | 0.5222 |
| 1.0244 | 4.59 | 500 | 1.0290 | 0.3323 |
| 0.7095 | 5.5 | 600 | 0.8424 | 0.2859 |
| 0.619 | 6.42 | 700 | 0.7389 | 0.2232 |
| 0.3541 | 7.34 | 800 | 0.7049 | 0.2043 |
| 0.2946 | 8.26 | 900 | 0.7065 | 0.2153 |
| 0.2868 | 9.17 | 1000 | 0.6840 | 0.2115 |
| 0.2245 | 10.09 | 1100 | 0.6714 | 0.1952 |
| 0.1394 | 11.01 | 1200 | 0.6864 | 0.1954 |
| 0.1288 | 11.93 | 1300 | 0.6696 | 0.2017 |
| 0.1568 | 12.84 | 1400 | 0.6468 | 0.1843 |
| 0.1269 | 13.76 | 1500 | 0.6736 | 0.1965 |
| 0.1101 | 14.68 | 1600 | 0.6689 | 0.1915 |
| 0.1388 | 15.6 | 1700 | 0.6690 | 0.1782 |
| 0.0739 | 16.51 | 1800 | 0.6364 | 0.1734 |
| 0.0897 | 17.43 | 1900 | 0.6480 | 0.1748 |
| 0.0795 | 18.35 | 2000 | 0.6356 | 0.1695 |
| 0.0823 | 19.27 | 2100 | 0.6382 | 0.1685 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.8.1
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
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