This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:

Loss: inf
Wer: 0.3082

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

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.8691	0.11	400	inf	1.0
1.673	0.22	800	inf	0.7178
0.6454	0.33	1200	inf	0.6274
0.5527	0.44	1600	inf	0.5747
0.4989	0.55	2000	inf	0.5174
0.4827	0.66	2400	inf	0.5302
0.4462	0.77	2800	inf	0.4916
0.4374	0.88	3200	inf	0.4769
0.4183	0.99	3600	inf	0.4687
0.3854	1.1	4000	inf	0.4669
0.3802	1.2	4400	inf	0.4513
0.3727	1.31	4800	inf	0.4505
0.3694	1.42	5200	inf	0.4405
0.3709	1.53	5600	inf	0.4364
0.363	1.64	6000	inf	0.4318
0.3669	1.75	6400	inf	0.4398
0.3597	1.86	6800	inf	0.4353
0.3541	1.97	7200	inf	0.4251
0.3277	2.08	7600	inf	0.4153
0.3211	2.19	8000	inf	0.4178
0.3225	2.3	8400	inf	0.4267
0.3215	2.41	8800	inf	0.4139
0.3224	2.52	9200	inf	0.4054
0.3106	2.63	9600	inf	0.4155
0.3141	2.74	10000	inf	0.4188
0.3189	2.85	10400	inf	0.4036
0.3213	2.96	10800	inf	0.4071
0.3005	3.07	11200	inf	0.3954
0.2872	3.18	11600	inf	0.3974
0.2855	3.29	12000	inf	0.3982
0.2898	3.39	12400	inf	0.3987
0.288	3.5	12800	inf	0.4021
0.2941	3.61	13200	inf	0.3955
0.2951	3.72	13600	inf	0.4022
0.2916	3.83	14000	inf	0.3960
0.2896	3.94	14400	inf	0.3903
0.2794	4.05	14800	inf	0.3918
0.2707	4.16	15200	inf	0.3873
0.2682	4.27	15600	inf	0.3927
0.2654	4.38	16000	inf	0.3844
0.2699	4.49	16400	inf	0.3909
0.2723	4.6	16800	inf	0.3904
0.2762	4.71	17200	inf	0.3857
0.2621	4.82	17600	inf	0.3795
0.26	4.93	18000	inf	0.3764
0.2659	5.04	18400	inf	0.3842
0.2479	5.15	18800	inf	0.3719
0.2518	5.26	19200	inf	0.3822
0.2591	5.37	19600	inf	0.3837
0.2491	5.48	20000	inf	0.3871
0.2466	5.59	20400	inf	0.3747
0.2519	5.69	20800	inf	0.3788
0.2516	5.8	21200	inf	0.3781
0.2422	5.91	21600	inf	0.3751
0.2439	6.02	22000	inf	0.3693
0.2327	6.13	22400	inf	0.3752
0.2327	6.24	22800	inf	0.3706
0.2302	6.35	23200	inf	0.3687
0.2313	6.46	23600	inf	0.3690
0.2363	6.57	24000	inf	0.3686
0.2329	6.68	24400	inf	0.3681
0.2328	6.79	24800	inf	0.3626
0.2329	6.9	25200	inf	0.3652
0.2254	7.01	25600	inf	0.3606
0.2124	7.12	26000	inf	0.3648
0.2206	7.23	26400	inf	0.3686
0.2151	7.34	26800	inf	0.3646
0.2167	7.45	27200	inf	0.3630
0.2196	7.56	27600	inf	0.3597
0.2089	7.67	28000	inf	0.3561
0.2183	7.78	28400	inf	0.3593
0.2148	7.89	28800	inf	0.3580
0.2232	7.99	29200	inf	0.3597
0.2002	8.1	29600	inf	0.3581
0.1924	8.21	30000	inf	0.3585
0.2046	8.32	30400	inf	0.3606
0.2057	8.43	30800	inf	0.3611
0.2042	8.54	31200	inf	0.3618
0.21	8.65	31600	inf	0.3599
0.2076	8.76	32000	inf	0.3568
0.208	8.87	32400	inf	0.3564
0.2154	8.98	32800	inf	0.3566
0.1991	9.09	33200	inf	0.3621
0.1986	9.2	33600	inf	0.3571
0.1898	9.31	34000	inf	0.3515
0.1961	9.42	34400	inf	0.3559
0.1947	9.53	34800	inf	0.3521
0.1886	9.64	35200	inf	0.3500
0.1901	9.75	35600	inf	0.3557
0.1998	9.86	36000	inf	0.3547
0.1873	9.97	36400	inf	0.3498
0.1858	10.08	36800	inf	0.3552
0.1804	10.18	37200	inf	0.3518
0.18	10.29	37600	inf	0.3504
0.1777	10.4	38000	inf	0.3532
0.1777	10.51	38400	inf	0.3530
0.1801	10.62	38800	inf	0.3515
0.1839	10.73	39200	inf	0.3538
0.1913	10.84	39600	inf	0.3554
0.1909	10.95	40000	inf	0.3479
0.1812	11.06	40400	inf	0.3467
0.1664	11.17	40800	inf	0.3491
0.175	11.28	41200	inf	0.3446
0.1733	11.39	41600	inf	0.3464
0.1709	11.5	42000	inf	0.3467
0.1777	11.61	42400	inf	0.3469
0.1735	11.72	42800	inf	0.3452
0.1765	11.83	43200	inf	0.3471
0.1738	11.94	43600	inf	0.3496
0.1649	12.05	44000	inf	0.3445
0.1601	12.16	44400	inf	0.3464
0.1603	12.27	44800	inf	0.3416
0.1634	12.38	45200	inf	0.3445
0.1628	12.48	45600	inf	0.3452
0.1621	12.59	46000	inf	0.3403
0.1596	12.7	46400	inf	0.3394
0.1589	12.81	46800	inf	0.3401
0.1632	12.92	47200	inf	0.3403
0.163	13.03	47600	inf	0.3429
0.1516	13.14	48000	inf	0.3417
0.1506	13.25	48400	inf	0.3417
0.1568	13.36	48800	inf	0.3410
0.1543	13.47	49200	inf	0.3409
0.1574	13.58	49600	inf	0.3408
0.1555	13.69	50000	inf	0.3424
0.1535	13.8	50400	inf	0.3395
0.1539	13.91	50800	inf	0.3409
0.1528	14.02	51200	inf	0.3406
0.1411	14.13	51600	inf	0.3366
0.1413	14.24	52000	inf	0.3402
0.1477	14.35	52400	inf	0.3386
0.1433	14.46	52800	inf	0.3356
0.1446	14.57	53200	inf	0.3357
0.1427	14.67	53600	inf	0.3378
0.1462	14.78	54000	inf	0.3328
0.1436	14.89	54400	inf	0.3358
0.1434	15.0	54800	inf	0.3366
0.135	15.11	55200	inf	0.3354
0.1375	15.22	55600	inf	0.3355
0.1366	15.33	56000	inf	0.3356
0.1389	15.44	56400	inf	0.3336
0.1378	15.55	56800	inf	0.3364
0.1362	15.66	57200	inf	0.3325
0.1376	15.77	57600	inf	0.3361
0.1323	15.88	58000	inf	0.3364
0.1343	15.99	58400	inf	0.3332
0.1257	16.1	58800	inf	0.3339
0.1239	16.21	59200	inf	0.3316
0.1292	16.32	59600	inf	0.3313
0.1297	16.43	60000	inf	0.3332
0.1265	16.54	60400	inf	0.3313
0.1271	16.65	60800	inf	0.3310
0.1315	16.76	61200	inf	0.3307
0.1271	16.87	61600	inf	0.3337
0.1298	16.97	62000	inf	0.3318
0.1211	17.08	62400	inf	0.3326
0.1192	17.19	62800	inf	0.3290
0.1232	17.3	63200	inf	0.3291
0.1229	17.41	63600	inf	0.3349
0.1162	17.52	64000	inf	0.3281
0.1207	17.63	64400	inf	0.3308
0.1179	17.74	64800	inf	0.3257
0.1207	17.85	65200	inf	0.3290
0.1256	17.96	65600	inf	0.3297
0.119	18.07	66000	inf	0.3279
0.1111	18.18	66400	inf	0.3302
0.1086	18.29	66800	inf	0.3285
0.1179	18.4	67200	inf	0.3274
0.1099	18.51	67600	inf	0.3281
0.1141	18.62	68000	inf	0.3281
0.1091	18.73	68400	inf	0.3301
0.1147	18.84	68800	inf	0.3270
0.1158	18.95	69200	inf	0.3246
0.1111	19.06	69600	inf	0.3227
0.1075	19.16	70000	inf	0.3249
0.1051	19.27	70400	inf	0.3253
0.1029	19.38	70800	inf	0.3252
0.1039	19.49	71200	inf	0.3264
0.1063	19.6	71600	inf	0.3242
0.1071	19.71	72000	inf	0.3250
0.1063	19.82	72400	inf	0.3248
0.1085	19.93	72800	inf	0.3247
0.1038	20.04	73200	inf	0.3242
0.1017	20.15	73600	inf	0.3255
0.099	20.26	74000	inf	0.3247
0.0971	20.37	74400	inf	0.3258
0.1002	20.48	74800	inf	0.3223
0.1013	20.59	75200	inf	0.3230
0.1018	20.7	75600	inf	0.3232
0.0967	20.81	76000	inf	0.3215
0.1008	20.92	76400	inf	0.3212
0.0975	21.03	76800	inf	0.3191
0.0893	21.14	77200	inf	0.3210
0.0911	21.25	77600	inf	0.3206
0.0959	21.36	78000	inf	0.3211
0.094	21.46	78400	inf	0.3198
0.0939	21.57	78800	inf	0.3202
0.0936	21.68	79200	inf	0.3202
0.0938	21.79	79600	inf	0.3195
0.0938	21.9	80000	inf	0.3184
0.0916	22.01	80400	inf	0.3185
0.0858	22.12	80800	inf	0.3177
0.0909	22.23	81200	inf	0.3211
0.0915	22.34	81600	inf	0.3222
0.088	22.45	82000	inf	0.3194
0.0902	22.56	82400	inf	0.3199
0.0868	22.67	82800	inf	0.3174
0.0871	22.78	83200	inf	0.3201
0.0908	22.89	83600	inf	0.3177
0.0842	23.0	84000	inf	0.3187
0.0842	23.11	84400	inf	0.3168
0.0815	23.22	84800	inf	0.3187
0.084	23.33	85200	inf	0.3201
0.0835	23.44	85600	inf	0.3185
0.0821	23.55	86000	inf	0.3189
0.0836	23.66	86400	inf	0.3179
0.0816	23.76	86800	inf	0.3174
0.0847	23.87	87200	inf	0.3172
0.0828	23.98	87600	inf	0.3178
0.0796	24.09	88000	inf	0.3144
0.0793	24.2	88400	inf	0.3149
0.0773	24.31	88800	inf	0.3165
0.0808	24.42	89200	inf	0.3154
0.0743	24.53	89600	inf	0.3159
0.078	24.64	90000	inf	0.3145
0.0792	24.75	90400	inf	0.3170
0.0775	24.86	90800	inf	0.3134
0.0763	24.97	91200	inf	0.3144
0.0705	25.08	91600	inf	0.3138
0.0724	25.19	92000	inf	0.3156
0.0732	25.3	92400	inf	0.3158
0.0743	25.41	92800	inf	0.3144
0.0729	25.52	93200	inf	0.3133
0.071	25.63	93600	inf	0.3139
0.0764	25.74	94000	inf	0.3122
0.0726	25.85	94400	inf	0.3128
0.0714	25.95	94800	inf	0.3135
0.0725	26.06	95200	inf	0.3147
0.0711	26.17	95600	inf	0.3130
0.0684	26.28	96000	inf	0.3125
0.0683	26.39	96400	inf	0.3144
0.0698	26.5	96800	inf	0.3135
0.0687	26.61	97200	inf	0.3131
0.0675	26.72	97600	inf	0.3119
0.0678	26.83	98000	inf	0.3105
0.0677	26.94	98400	inf	0.3102
0.068	27.05	98800	inf	0.3128
0.0694	27.16	99200	inf	0.3111
0.0681	27.27	99600	inf	0.3118
0.0656	27.38	100000	inf	0.3110
0.065	27.49	100400	inf	0.3113
0.0649	27.6	100800	inf	0.3113
0.0643	27.71	101200	inf	0.3107
0.0651	27.82	101600	inf	0.3102
0.0643	27.93	102000	inf	0.3109
0.063	28.04	102400	inf	0.3110
0.0604	28.15	102800	inf	0.3108
0.062	28.25	103200	inf	0.3110
0.0623	28.36	103600	inf	0.3106
0.063	28.47	104000	inf	0.3102
0.0619	28.58	104400	inf	0.3101
0.0636	28.69	104800	inf	0.3108
0.0636	28.8	105200	inf	0.3099
0.0643	28.91	105600	inf	0.3089
0.0607	29.02	106000	inf	0.3094
0.0597	29.13	106400	inf	0.3091
0.0616	29.24	106800	inf	0.3087
0.0594	29.35	107200	inf	0.3087
0.0614	29.46	107600	inf	0.3087
0.06	29.57	108000	inf	0.3082
0.0617	29.68	108400	inf	0.3085
0.0574	29.79	108800	inf	0.3082
0.06	29.9	109200	inf	0.3082

Framework versions

Transformers 4.37.1
Pytorch 2.2.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

smutuvi/wav2vec2-large-xlsr-sw-common-voice-16

wav2vec2-large-xlsr-sw-common-voice-16

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

smutuvi/
wav2vec2-large-xlsr-sw-common-voice-16