| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: wav2vec2-ksponspeech |
| | results: [] |
| | |
| | --- |
| | |
| |
|
| | # wav2vec2-ksponspeech |
| |
|
| | This model is a fine-tuned version of [Wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| |
|
| | - **WER(Word Error Rate)** for Third party test data : 0.373 |
| |
|
| | **For improving WER:** |
| | - Numeric / Character Unification |
| | - Decoding the word with the correct notation (from word based on pronounciation) |
| | - Uniform use of special characters (. / ?) |
| | - Converting non-existent words to existing words |
| |
|
| | ## Model description |
| |
|
| | Korean Wav2vec with Ksponspeech dataset. |
| |
|
| | This model was trained by two dataset : |
| |
|
| | - Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech) |
| | - Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech) |
| | - Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech) |
| | - Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech) |
| |
|
| | ### Hardward Specification |
| | - GPU : GEFORCE RTX 3080ti 12GB |
| | - CPU : Intel i9-12900k |
| | - RAM : 32GB |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0003 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 30 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.19.4 |
| | - Pytorch 1.11.0 |
| | - Datasets 2.2.2 |
| | - Tokenizers 0.12.1 |
| | |
