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--- |
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language: |
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- tr |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: hello_2b_2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# hello_2b_2 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5324 |
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- Wer: 0.5109 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 3.3543 | 0.92 | 100 | 3.4342 | 1.0 | |
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| 3.0521 | 1.85 | 200 | 3.1243 | 1.0 | |
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| 1.4905 | 2.77 | 300 | 1.1760 | 0.9876 | |
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| 0.5852 | 3.7 | 400 | 0.7678 | 0.7405 | |
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| 0.4442 | 4.63 | 500 | 0.7637 | 0.7179 | |
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| 0.3816 | 5.55 | 600 | 0.7114 | 0.6726 | |
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| 0.2923 | 6.48 | 700 | 0.7109 | 0.6837 | |
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| 0.2771 | 7.4 | 800 | 0.6800 | 0.6530 | |
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| 0.1643 | 8.33 | 900 | 0.6031 | 0.6089 | |
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| 0.2931 | 9.26 | 1000 | 0.6467 | 0.6308 | |
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| 0.1495 | 10.18 | 1100 | 0.6042 | 0.6085 | |
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| 0.2093 | 11.11 | 1200 | 0.5850 | 0.5889 | |
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| 0.1329 | 12.04 | 1300 | 0.5557 | 0.5567 | |
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| 0.1005 | 12.96 | 1400 | 0.5964 | 0.5814 | |
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| 0.2162 | 13.88 | 1500 | 0.5692 | 0.5626 | |
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| 0.0923 | 14.81 | 1600 | 0.5508 | 0.5462 | |
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| 0.075 | 15.74 | 1700 | 0.5477 | 0.5307 | |
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| 0.2029 | 16.66 | 1800 | 0.5501 | 0.5300 | |
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| 0.0985 | 17.59 | 1900 | 0.5350 | 0.5303 | |
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| 0.1674 | 18.51 | 2000 | 0.5429 | 0.5241 | |
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| 0.1305 | 19.44 | 2100 | 0.5645 | 0.5443 | |
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| 0.0774 | 20.37 | 2200 | 0.5313 | 0.5216 | |
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| 0.1372 | 21.29 | 2300 | 0.5644 | 0.5392 | |
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| 0.1095 | 22.22 | 2400 | 0.5577 | 0.5306 | |
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| 0.0958 | 23.15 | 2500 | 0.5461 | 0.5273 | |
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| 0.0544 | 24.07 | 2600 | 0.5290 | 0.5055 | |
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| 0.0579 | 24.99 | 2700 | 0.5295 | 0.5150 | |
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| 0.1213 | 25.92 | 2800 | 0.5311 | 0.5221 | |
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| 0.0691 | 26.85 | 2900 | 0.5228 | 0.5095 | |
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| 0.1729 | 27.77 | 3000 | 0.5340 | 0.5095 | |
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| 0.0697 | 28.7 | 3100 | 0.5334 | 0.5139 | |
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| 0.0734 | 29.63 | 3200 | 0.5323 | 0.5140 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.10.0 |
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- Datasets 1.15.2.dev0 |
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- Tokenizers 0.10.3 |
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