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alexziweiwang/
exp18-M03-both

Name: exp18-M03-both
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

generated_from_trainer

endpoints_compatible

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exp18-M03-both

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: 0.4134
Wer: 0.8533

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
44.7613	0.35	500	3.3891	1.0525
3.1954	0.7	1000	2.8766	1.0
2.9522	1.05	1500	2.7578	1.0
2.843	1.4	2000	2.5628	1.1318
2.6645	1.75	2500	2.1406	1.2864
2.3587	2.1	3000	1.8164	1.2934
2.1731	2.45	3500	1.5732	1.2775
2.0242	2.8	4000	1.4249	1.2666
1.9453	3.15	4500	1.3079	1.2220
1.7871	3.5	5000	1.2389	1.2081
1.7147	3.85	5500	1.1724	1.2101
1.5729	4.2	6000	1.1638	1.1982
1.4966	4.55	6500	1.0529	1.1497
1.3898	4.9	7000	1.0808	1.1506
1.3447	5.25	7500	0.9702	1.1229
1.2342	5.6	8000	0.8994	1.1219
1.1918	5.95	8500	0.9212	1.1169
1.1037	6.3	9000	0.9057	1.1080
1.0661	6.65	9500	0.8231	1.1110
1.0501	7.0	10000	0.8291	1.0912
0.9069	7.35	10500	0.8360	1.0902
0.8959	7.7	11000	0.7961	1.0684
0.9256	8.05	11500	0.7459	1.0684
0.8686	8.4	12000	0.7276	1.0456
0.7998	8.75	12500	0.7195	1.0525
0.7406	9.1	13000	0.7471	1.0515
0.7646	9.45	13500	0.7716	1.0624
0.7018	9.8	14000	0.7262	1.0446
0.7114	10.15	14500	0.6795	1.0327
0.6498	10.5	15000	0.6724	1.0347
0.6652	10.85	15500	0.6994	1.0347
0.638	11.2	16000	0.6565	1.0159
0.6078	11.55	16500	0.6695	1.0575
0.588	11.9	17000	0.6391	1.0149
0.5722	12.25	17500	0.6321	1.0188
0.5505	12.6	18000	0.6306	1.0089
0.5297	12.95	18500	0.6100	1.0139
0.5188	13.3	19000	0.5426	0.9931
0.4865	13.65	19500	0.5410	0.9881
0.5132	14.0	20000	0.5095	0.9792
0.4782	14.35	20500	0.4962	0.9901
0.4627	14.7	21000	0.5277	0.9871
0.4568	15.05	21500	0.4958	0.9683
0.4312	15.4	22000	0.5146	0.9752
0.4286	15.75	22500	0.4682	0.9693
0.428	16.1	23000	0.5121	0.9851
0.3656	16.45	23500	0.4894	0.9485
0.3884	16.79	24000	0.4832	0.9465
0.3835	17.14	24500	0.4925	0.9841
0.3584	17.49	25000	0.5503	0.9782
0.3719	17.84	25500	0.4960	0.9415
0.3555	18.19	26000	0.4238	0.9594
0.3196	18.54	26500	0.4501	0.9495
0.3288	18.89	27000	0.5292	0.9564
0.3402	19.24	27500	0.4156	0.9475
0.2889	19.59	28000	0.4056	0.9633
0.3562	19.94	28500	0.3972	0.9504
0.336	20.29	29000	0.4021	0.9257
0.2952	20.64	29500	0.3920	0.9167
0.2678	20.99	30000	0.3610	0.9049
0.2816	21.34	30500	0.3782	0.9267
0.2718	21.69	31000	0.3502	0.9068
0.2948	22.04	31500	0.3412	0.9078
0.2782	22.39	32000	0.3799	0.9039
0.2668	22.74	32500	0.3725	0.9058
0.2685	23.09	33000	0.3825	0.8880
0.2514	23.44	33500	0.3618	0.8791
0.2305	23.79	34000	0.4211	0.8870
0.2671	24.14	34500	0.4126	0.8900
0.2153	24.49	35000	0.4106	0.8801
0.2323	24.84	35500	0.3845	0.8751
0.2208	25.19	36000	0.4017	0.8741
0.2023	25.54	36500	0.4451	0.8662
0.232	25.89	37000	0.4133	0.8583
0.2101	26.24	37500	0.4118	0.8662
0.2139	26.59	38000	0.3937	0.8682
0.1917	26.94	38500	0.4015	0.8603
0.1904	27.29	39000	0.4018	0.8622
0.2265	27.64	39500	0.3983	0.8573
0.2081	27.99	40000	0.4027	0.8563
0.2124	28.34	40500	0.4172	0.8523
0.191	28.69	41000	0.4018	0.8444
0.1906	29.04	41500	0.4148	0.8494
0.1613	29.39	42000	0.4195	0.8543
0.1864	29.74	42500	0.4134	0.8533

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 1.18.3
Tokenizers 0.13.2

alexziweiwang/exp18-M03-both

exp18-M03-both

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alexziweiwang/
exp18-M03-both