STT_Model_9 – AI Model by LowGI | AlphaNeural AI

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STT_Model_9

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STT_Model_9

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2506
Wer: 0.1718

Model description

More information needed

Intended uses & limitations

More information needed

Dataset info

Name: LJSpeech
Source: https://www.kaggle.com/datasets/mathurinache/the-lj-speech-dataset
Total audios (in Google Drive): 1420
Total transcripts (in Google Drive): 13100
No. of rows selected: 500
Train-test ratio: 70:30
No. of training set: 350
No. of testing set: 150

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	4.55	200	2.9217	0.9846
No log	9.09	400	1.2293	0.7093
2.3111	13.64	600	0.3885	0.3602
2.3111	18.18	800	0.3123	0.3097
0.2471	22.73	1000	0.3094	0.2737
0.2471	27.27	1200	0.3007	0.2537
0.2471	31.82	1400	0.2650	0.2008
0.0853	36.36	1600	0.2599	0.1884
0.0853	40.91	1800	0.2462	0.1734
0.0344	45.45	2000	0.2663	0.1730
0.0344	50.0	2200	0.2506	0.1718

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2