MT_RN – AI Model by Siyong | AlphaNeural AI

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Siyong/
MT_RN

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automatic-speech-recognition

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run1

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: 1.6666
Wer: 0.6375
Cer: 0.3170

Model description

More information needed

Intended uses & limitations

More information needed

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: 2000
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.0564	2.36	2000	2.3456	0.9628	0.5549
0.5071	4.73	4000	2.0652	0.9071	0.5115
0.3952	7.09	6000	2.3649	0.9108	0.4628
0.3367	9.46	8000	1.7615	0.8253	0.4348
0.2765	11.82	10000	1.6151	0.7937	0.4087
0.2493	14.18	12000	1.4976	0.7881	0.3905
0.2318	16.55	14000	1.6731	0.8160	0.3925
0.2074	18.91	16000	1.5822	0.7658	0.3913
0.1825	21.28	18000	1.5442	0.7361	0.3704
0.1824	23.64	20000	1.5988	0.7621	0.3711
0.1699	26.0	22000	1.4261	0.7119	0.3490
0.158	28.37	24000	1.7482	0.7658	0.3648
0.1385	30.73	26000	1.4103	0.6784	0.3348
0.1199	33.1	28000	1.5214	0.6636	0.3273
0.116	35.46	30000	1.4288	0.7212	0.3486
0.1071	37.83	32000	1.5344	0.7138	0.3411
0.1007	40.19	34000	1.4501	0.6691	0.3237
0.0943	42.55	36000	1.5367	0.6859	0.3265
0.0844	44.92	38000	1.5321	0.6599	0.3273
0.0762	47.28	40000	1.6721	0.6264	0.3142
0.0778	49.65	42000	1.6666	0.6375	0.3170

Framework versions

Transformers 4.18.0
Pytorch 1.12.0+cu113
Datasets 2.0.0
Tokenizers 0.12.1