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wav2vec2-ksponspeech – AI Model by Taeham | AlphaNeural AI
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Taeham
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wav2vec2-ksponspeech
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transformers
pytorch
wav2vec2
automatic-speech-recognition
generated_from_trainer
apache-2.0
endpoints_compatible
us
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wav2vec2-ksponspeech
This model is a fine-tuned version of
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
