60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
---
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license: apache-2.0
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tags:
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- generated_from_trainer
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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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