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Model: Taeham/wav2vec2-ksponspeech
Source: Original Platform

README.md

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+---
+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