combined-MTL9

alexziweiwang/combined-MTL9

combined-MTL9 is a automatic speech recognition model from alexziweiwang. 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: Nov 16, 2022
Updated: Nov 22, 2022
SHA: 889d20a5

Model card

From the Hugging Face model README

combined-MTL9

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

Loss: 1.3413
Wer: 0.8603

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: 4
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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
76.4918	0.35	500	3.4171	1.0
2.9927	0.69	1000	2.4743	1.0667
2.2033	1.04	1500	1.6693	1.25
1.6165	1.39	2000	1.5341	1.1808
1.4208	1.74	2500	1.3148	1.1179
1.2858	2.08	3000	1.2272	1.0872
1.1317	2.43	3500	1.0865	1.0731
1.0668	2.78	4000	1.0798	1.0474
1.0429	3.12	4500	1.4627	1.0936
0.9615	3.47	5000	1.2540	1.0090
0.975	3.82	5500	0.9936	0.9679
0.8517	4.17	6000	1.1039	1.0282
0.8281	4.51	6500	1.0609	0.9897
0.8413	4.86	7000	0.9513	0.9397
0.7618	5.21	7500	1.1656	0.9718
0.7173	5.56	8000	1.1974	0.9603
0.7449	5.9	8500	1.0144	0.9731
0.6762	6.25	9000	1.1774	0.9231
0.6749	6.6	9500	1.1823	0.9205
0.6776	6.94	10000	0.9167	0.9244
0.5937	7.29	10500	1.3344	0.9769
0.6488	7.64	11000	1.0245	0.9692
0.6116	7.99	11500	0.9444	0.9141
0.5497	8.33	12000	0.9499	0.9692
0.5937	8.68	12500	1.1087	0.9231
0.5268	9.03	13000	1.3408	0.9269
0.5078	9.38	13500	1.1737	0.9038
0.497	9.72	14000	0.9963	0.8987
0.5231	10.07	14500	1.3247	0.9590
0.4651	10.42	15000	1.1988	0.9308
0.481	10.76	15500	1.0034	0.9308
0.481	11.11	16000	1.0040	0.8782
0.4751	11.46	16500	0.8824	0.8538
0.4554	11.81	17000	0.9741	0.8821
0.426	12.15	17500	0.8552	0.8615
0.4186	12.5	18000	1.0646	0.8833
0.4154	12.85	18500	0.9618	0.8936
0.5115	13.19	19000	1.0312	0.8910
0.3564	13.54	19500	1.0686	0.8769
0.3927	13.89	20000	1.2533	0.9103
0.3628	14.24	20500	1.2945	0.8872
0.3808	14.58	21000	1.0195	0.8538
0.3981	14.93	21500	1.0388	0.8808
0.3337	15.28	22000	1.0464	0.8923
0.3092	15.62	22500	1.0843	0.8705
0.378	15.97	23000	1.0880	0.8859
0.3231	16.32	23500	0.9205	0.8782
0.3588	16.67	24000	1.0064	0.8962
0.3048	17.01	24500	0.9130	0.8705
0.3	17.36	25000	1.0100	0.9077
0.3045	17.71	25500	1.0559	0.9077
0.3024	18.06	26000	1.1225	0.9026
0.2614	18.4	26500	1.0911	0.8897
0.2755	18.75	27000	1.0872	0.8808
0.2798	19.1	27500	1.2911	0.9154
0.2455	19.44	28000	1.0646	0.8821
0.2524	19.79	28500	1.3356	0.9154
0.2435	20.14	29000	1.1257	0.8641
0.2458	20.49	29500	1.2221	0.8667
0.2216	20.83	30000	1.1364	0.8769
0.234	21.18	30500	1.2094	0.8808
0.233	21.53	31000	1.1604	0.8910
0.2536	21.88	31500	1.0934	0.8808
0.1885	22.22	32000	1.2177	0.8718
0.2186	22.57	32500	1.0539	0.8667
0.1991	22.92	33000	1.2222	0.8641
0.2027	23.26	33500	1.3863	0.8577
0.193	23.61	34000	1.2293	0.8705
0.2054	23.96	34500	1.3398	0.8769
0.2197	24.31	35000	1.3138	0.8705
0.1898	24.65	35500	1.2897	0.8679
0.1933	25.0	36000	1.2666	0.8769
0.1632	25.35	36500	1.2758	0.8756
0.1869	25.69	37000	1.1811	0.8603
0.1731	26.04	37500	1.2511	0.8679
0.1821	26.39	38000	1.3391	0.8718
0.1648	26.74	38500	1.2505	0.8628
0.1909	27.08	39000	1.2984	0.85
0.1902	27.43	39500	1.2261	0.8487
0.1449	27.78	40000	1.2853	0.8487
0.1583	28.12	40500	1.3361	0.8628
0.148	28.47	41000	1.3638	0.8654
0.1648	28.82	41500	1.3380	0.8603
0.1461	29.17	42000	1.3561	0.8603
0.1565	29.51	42500	1.3489	0.8615
0.16	29.86	43000	1.3413	0.8603

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
Created: Nov 16, 2022
Updated: Nov 22, 2022