Model_G_S_D_Wav2Vec2

rossevine/Model_G_S_D_Wav2Vec2

Model_G_S_D_Wav2Vec2 is a automatic speech recognition model from rossevine. Use it when you need speech turned into text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
Model type: wav2vec2
Access: Public
Created: Aug 17, 2023
Updated: Aug 24, 2023
SHA: 566a4557

Model card

From the Hugging Face model README

Model_G_S_D_Wav2Vec2

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0425
Wer: 0.0310
Cer: 0.0095

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: 0.0003
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.4434	0.85	400	0.0763	0.0984	0.0254
0.1737	1.71	800	0.0639	0.0781	0.0199
0.1293	2.56	1200	0.0522	0.0653	0.0167
0.0965	3.41	1600	0.0471	0.0659	0.0163
0.0874	4.26	2000	0.0464	0.0535	0.0139
0.0679	5.12	2400	0.0395	0.0490	0.0132
0.0618	5.97	2800	0.0424	0.0533	0.0143
0.056	6.82	3200	0.0471	0.0511	0.0132
0.0568	7.68	3600	0.0432	0.0468	0.0123
0.046	8.53	4000	0.0425	0.0472	0.0130
0.0459	9.38	4400	0.0502	0.0499	0.0134
0.0408	10.23	4800	0.0450	0.0488	0.0131
0.0436	11.09	5200	0.0431	0.0420	0.0119
0.0375	11.94	5600	0.0463	0.0484	0.0132
0.0327	12.79	6000	0.0412	0.0424	0.0116
0.0322	13.65	6400	0.0381	0.0382	0.0111
0.0316	14.5	6800	0.0441	0.0460	0.0128
0.0296	15.35	7200	0.0426	0.0415	0.0119
0.0274	16.2	7600	0.0421	0.0383	0.0106
0.0247	17.06	8000	0.0442	0.0391	0.0120
0.0235	17.91	8400	0.0449	0.0409	0.0116
0.0219	18.76	8800	0.0394	0.0353	0.0106
0.0174	19.62	9200	0.0489	0.0393	0.0117
0.0161	20.47	9600	0.0421	0.0347	0.0099
0.0158	21.32	10000	0.0425	0.0349	0.0108
0.0141	22.17	10400	0.0436	0.0397	0.0116
0.0156	23.03	10800	0.0432	0.0375	0.0114
0.0138	23.88	11200	0.0438	0.0364	0.0110
0.0116	24.73	11600	0.0420	0.0368	0.0108
0.0108	25.59	12000	0.0407	0.0341	0.0103
0.0073	26.44	12400	0.0428	0.0336	0.0101
0.0085	27.29	12800	0.0432	0.0328	0.0101
0.0078	28.14	13200	0.0416	0.0318	0.0096
0.0065	29.0	13600	0.0423	0.0310	0.0097
0.0062	29.85	14000	0.0425	0.0310	0.0095

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 1.18.3
Tokenizers 0.13.3

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
Created: Aug 17, 2023
Updated: Aug 24, 2023