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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: wav2vec2-xlarge-...-common_voice-tr-demo |
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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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# wav2vec2-xlarge-...-common_voice-tr-demo |
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This model is a fine-tuned version of [facebook/wav2vec2-xlarge-xlsr-...](https://huggingface.co/facebook/wav2vec2-xlarge-xlsr-...) on the COMMON_VOICE - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2701 |
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- Wer: 0.2309 |
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- Cer: 0.0527 |
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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: 0.00005 |
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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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| 0.4388 | 3.7 | 400 | 1.366 | 0.9701 | |
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| 0.3766 | 7.4 | 800 | 0.4914 | 0.5374 | |
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| 0.2295 | 11.11 | 1200 | 0.3934 | 0.4125 | |
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| 0.1121 | 14.81 | 1600 | 0.3264 | 0.2904 | |
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| 0.1473 | 18.51 | 2000 | 0.3103 | 0.2671 | |
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| 0.1013 | 22.22 | 2400 | 0.2589 | 0.2324 | |
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| 0.0704 | 25.92 | 2800 | 0.2826 | 0.2339 | |
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| 0.0537 | 29.63 | 3200 | 0.2704 | 0.2309 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.8.1 |
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- Datasets 1.14.1.dev0 |
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- Tokenizers 0.10.3 |
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