README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - zeroth_korean_asr
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set:

• Loss: 0.2088
• Wer: 0.2954
• Cer: 0.0953

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: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
19.7138	0.72	500	19.6427	1.0	1.0
4.8039	1.44	1000	4.7842	1.0	1.0
4.5619	2.16	1500	4.5608	0.9992	0.9598
4.254	2.88	2000	4.2729	0.9955	0.9063
4.1905	3.6	2500	4.2257	0.9903	0.8758
4.0683	4.32	3000	3.9294	0.9937	0.7911
3.486	5.04	3500	2.7045	1.0012	0.5934
2.946	5.75	4000	1.9691	0.9425	0.4634
2.634	6.47	4500	1.5212	0.8807	0.3850
2.4066	7.19	5000	1.2551	0.8177	0.3601
2.2651	7.91	5500	1.0423	0.7650	0.3039
2.1828	8.63	6000	0.9599	0.7273	0.3106
2.1023	9.35	6500	0.9482	0.7161	0.3063
2.0536	10.07	7000	0.8242	0.6767	0.2860
1.9803	10.79	7500	0.7643	0.6563	0.2637
1.9468	11.51	8000	0.7319	0.6441	0.2505
1.9178	12.23	8500	0.6937	0.6320	0.2489
1.8515	12.95	9000	0.6443	0.6053	0.2196
1.8083	13.67	9500	0.6286	0.6122	0.2148
1.819	14.39	10000	0.6015	0.5986	0.2074
1.7684	15.11	10500	0.5682	0.5741	0.1982
1.7195	15.83	11000	0.5385	0.5592	0.2007
1.7044	16.55	11500	0.5362	0.5524	0.2097
1.6879	17.27	12000	0.5119	0.5489	0.2083
1.656	17.98	12500	0.4990	0.5362	0.1968
1.6122	18.7	13000	0.4561	0.5092	0.1900
1.5919	19.42	13500	0.4778	0.5225	0.1975
1.5896	20.14	14000	0.4563	0.5098	0.1859
1.5589	20.86	14500	0.4362	0.4940	0.1725
1.5353	21.58	15000	0.4140	0.4826	0.1580
1.5441	22.3	15500	0.4031	0.4742	0.1550
1.5116	23.02	16000	0.3916	0.4748	0.1545
1.4731	23.74	16500	0.3841	0.4810	0.1542
1.4647	24.46	17000	0.3752	0.4524	0.1475
1.4328	25.18	17500	0.3587	0.4476	0.1461
1.4129	25.9	18000	0.3429	0.4242	0.1366
1.4062	26.62	18500	0.3450	0.4251	0.1355
1.3928	27.34	19000	0.3297	0.4145	0.1322
1.3906	28.06	19500	0.3210	0.4185	0.1336
1.358	28.78	20000	0.3131	0.3970	0.1275
1.3445	29.5	20500	0.3069	0.3920	0.1276
1.3159	30.22	21000	0.3035	0.3961	0.1255
1.3044	30.93	21500	0.2952	0.3854	0.1242
1.3034	31.65	22000	0.2966	0.3772	0.1227
1.2963	32.37	22500	0.2844	0.3706	0.1208
1.2765	33.09	23000	0.2841	0.3567	0.1173
1.2438	33.81	23500	0.2734	0.3552	0.1137
1.2487	34.53	24000	0.2703	0.3502	0.1118
1.2249	35.25	24500	0.2650	0.3484	0.1142
1.2229	35.97	25000	0.2584	0.3374	0.1097
1.2374	36.69	25500	0.2568	0.3337	0.1095
1.2153	37.41	26000	0.2494	0.3327	0.1071
1.1925	38.13	26500	0.2518	0.3366	0.1077
1.1908	38.85	27000	0.2437	0.3272	0.1057
1.1858	39.57	27500	0.2396	0.3265	0.1044
1.1808	40.29	28000	0.2373	0.3156	0.1028
1.1842	41.01	28500	0.2356	0.3152	0.1026
1.1668	41.73	29000	0.2319	0.3188	0.1025
1.1448	42.45	29500	0.2293	0.3099	0.0995
1.1327	43.17	30000	0.2265	0.3047	0.0979
1.1307	43.88	30500	0.2222	0.3078	0.0989
1.1419	44.6	31000	0.2215	0.3038	0.0981
1.1231	45.32	31500	0.2193	0.3013	0.0972
1.139	46.04	32000	0.2162	0.3007	0.0968
1.1114	46.76	32500	0.2122	0.2982	0.0960
1.111	47.48	33000	0.2125	0.2946	0.0948
1.0982	48.2	33500	0.2099	0.2957	0.0953
1.109	48.92	34000	0.2092	0.2955	0.0955
1.0905	49.64	34500	0.2088	0.2954	0.0953

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.10.3