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---
language:
- tr
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: hello_2b_2
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. -->
# hello_2b_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5324
- Wer: 0.5109
## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- 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: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3543 | 0.92 | 100 | 3.4342 | 1.0 |
| 3.0521 | 1.85 | 200 | 3.1243 | 1.0 |
| 1.4905 | 2.77 | 300 | 1.1760 | 0.9876 |
| 0.5852 | 3.7 | 400 | 0.7678 | 0.7405 |
| 0.4442 | 4.63 | 500 | 0.7637 | 0.7179 |
| 0.3816 | 5.55 | 600 | 0.7114 | 0.6726 |
| 0.2923 | 6.48 | 700 | 0.7109 | 0.6837 |
| 0.2771 | 7.4 | 800 | 0.6800 | 0.6530 |
| 0.1643 | 8.33 | 900 | 0.6031 | 0.6089 |
| 0.2931 | 9.26 | 1000 | 0.6467 | 0.6308 |
| 0.1495 | 10.18 | 1100 | 0.6042 | 0.6085 |
| 0.2093 | 11.11 | 1200 | 0.5850 | 0.5889 |
| 0.1329 | 12.04 | 1300 | 0.5557 | 0.5567 |
| 0.1005 | 12.96 | 1400 | 0.5964 | 0.5814 |
| 0.2162 | 13.88 | 1500 | 0.5692 | 0.5626 |
| 0.0923 | 14.81 | 1600 | 0.5508 | 0.5462 |
| 0.075 | 15.74 | 1700 | 0.5477 | 0.5307 |
| 0.2029 | 16.66 | 1800 | 0.5501 | 0.5300 |
| 0.0985 | 17.59 | 1900 | 0.5350 | 0.5303 |
| 0.1674 | 18.51 | 2000 | 0.5429 | 0.5241 |
| 0.1305 | 19.44 | 2100 | 0.5645 | 0.5443 |
| 0.0774 | 20.37 | 2200 | 0.5313 | 0.5216 |
| 0.1372 | 21.29 | 2300 | 0.5644 | 0.5392 |
| 0.1095 | 22.22 | 2400 | 0.5577 | 0.5306 |
| 0.0958 | 23.15 | 2500 | 0.5461 | 0.5273 |
| 0.0544 | 24.07 | 2600 | 0.5290 | 0.5055 |
| 0.0579 | 24.99 | 2700 | 0.5295 | 0.5150 |
| 0.1213 | 25.92 | 2800 | 0.5311 | 0.5221 |
| 0.0691 | 26.85 | 2900 | 0.5228 | 0.5095 |
| 0.1729 | 27.77 | 3000 | 0.5340 | 0.5095 |
| 0.0697 | 28.7 | 3100 | 0.5334 | 0.5139 |
| 0.0734 | 29.63 | 3200 | 0.5323 | 0.5140 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
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