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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: Wav2vec2-large-xlsr-53 |
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model-index: |
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- name: wav2vec2-ksponspeech |
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results: [] |
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--- |
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# wav2vec2-ksponspeech |
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This model is a fine-tuned version of [Wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- **WER(Word Error Rate)** for Third party test data : 0.373 |
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**For improving WER:** |
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- Numeric / Character Unification |
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- Decoding the word with the correct notation (from word based on pronounciation) |
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- Uniform use of special characters (. / ?) |
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- Converting non-existent words to existing words |
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## Model description |
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Korean Wav2vec with Ksponspeech dataset. |
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This model was trained by two dataset : |
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- Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech) |
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- Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech) |
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- Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech) |
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- Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech) |
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### Hardward Specification |
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- GPU : GEFORCE RTX 3080ti 12GB |
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- CPU : Intel i9-12900k |
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- RAM : 32GB |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.19.4 |
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- Pytorch 1.11.0 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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