Model_G_ALL_Wav2Vec2

rossevine/Model_G_ALL_Wav2Vec2

Model_G_ALL_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 31, 2023
Updated: Sep 4, 2023
SHA: 346e6fa4

Model card

From the Hugging Face model README

Model_G_ALL_Wav2Vec2

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

Loss: 0.7956
Wer: 0.1972
Cer: 0.0811

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
0.8395	0.67	400	0.5656	0.3303	0.1236
0.3196	1.34	800	0.5007	0.2922	0.1071
0.2491	2.01	1200	0.5008	0.2830	0.1056
0.2012	2.68	1600	0.5177	0.2689	0.1003
0.1882	3.35	2000	0.5517	0.2622	0.0986
0.1811	4.02	2400	0.5225	0.2543	0.0980
0.1648	4.69	2800	0.5504	0.2477	0.0948
0.1451	5.36	3200	0.5181	0.2346	0.0908
0.149	6.04	3600	0.5204	0.2375	0.0941
0.1328	6.71	4000	0.5780	0.2375	0.0920
0.1256	7.38	4400	0.5406	0.2443	0.0960
0.1193	8.05	4800	0.5489	0.2317	0.0928
0.1099	8.72	5200	0.5864	0.2363	0.0925
0.1125	9.39	5600	0.5749	0.2267	0.0902
0.1044	10.06	6000	0.5698	0.2279	0.0905
0.0925	10.73	6400	0.6051	0.2337	0.0933
0.0951	11.4	6800	0.6785	0.2286	0.0907
0.0926	12.07	7200	0.5937	0.2337	0.0919
0.0838	12.74	7600	0.5918	0.2233	0.0893
0.0775	13.41	8000	0.5642	0.2227	0.0888
0.0742	14.08	8400	0.5927	0.2249	0.0898
0.0687	14.75	8800	0.6647	0.2265	0.0900
0.0685	15.42	9200	0.7438	0.2164	0.0885
0.0645	16.09	9600	0.6351	0.2128	0.0858
0.0582	16.76	10000	0.6164	0.2169	0.0878
0.0604	17.44	10400	0.6327	0.2146	0.0867
0.0557	18.11	10800	0.6790	0.2148	0.0879
0.0552	18.78	11200	0.6859	0.2101	0.0848
0.0474	19.45	11600	0.6648	0.2071	0.0847
0.048	20.12	12000	0.7172	0.2136	0.0873
0.0475	20.79	12400	0.6451	0.2058	0.0845
0.041	21.46	12800	0.6826	0.2074	0.0839
0.0405	22.13	13200	0.6738	0.2110	0.0842
0.0355	22.8	13600	0.7020	0.2050	0.0839
0.0325	23.47	14000	0.7085	0.2117	0.0854
0.0308	24.14	14400	0.7418	0.2077	0.0854
0.0321	24.81	14800	0.7371	0.2051	0.0840
0.0274	25.48	15200	0.7611	0.2082	0.0848
0.0287	26.15	15600	0.7208	0.2021	0.0836
0.0253	26.82	16000	0.7432	0.2025	0.0831
0.0256	27.49	16400	0.7435	0.2011	0.0824
0.0243	28.16	16800	0.7543	0.1991	0.0818
0.0241	28.83	17200	0.7676	0.1986	0.0814
0.0204	29.51	17600	0.7956	0.1972	0.0811

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 31, 2023
Updated: Sep 4, 2023