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hippopotam/
wav2vec2-common_voice13

Name: wav2vec2-common_voice13
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

mozilla-foundation/common_voice_13_0

generated_from_trainer

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wav2vec2-common_voice13

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON_VOICE_13_0 - TR dataset. It achieves the following results on the evaluation set:

Loss: 0.3366
Wer: 0.2937

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: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.0301	0.08	100	4.0286	1.0
3.1668	0.15	200	3.2323	1.0
2.3413	0.23	300	2.1300	0.9986
1.4546	0.31	400	0.8731	0.7629
1.4595	0.38	500	0.7366	0.7386
1.1903	0.46	600	0.6131	0.6645
1.1586	0.53	700	0.5491	0.6195
0.8275	0.61	800	0.5159	0.5923
1.0042	0.69	900	0.5153	0.6040
0.9428	0.76	1000	0.4629	0.5602
0.7592	0.84	1100	0.4670	0.5520
0.8284	0.92	1200	0.4455	0.5760
0.7736	0.99	1300	0.4571	0.5480
0.4047	1.07	1400	0.3962	0.4940
0.3543	1.14	1500	0.4018	0.4969
0.3898	1.22	1600	0.3901	0.4862
0.3827	1.3	1700	0.3982	0.4954
0.3316	1.37	1800	0.4139	0.5032
0.3365	1.45	1900	0.3964	0.4878
0.251	1.53	2000	0.4028	0.4899
0.2419	1.6	2100	0.3991	0.5190
0.3094	1.68	2200	0.3700	0.4865
0.3459	1.75	2300	0.3652	0.4850
0.3085	1.83	2400	0.3806	0.4742
0.4463	1.91	2500	0.3804	0.4729
0.2359	1.98	2600	0.3696	0.4635
0.1502	2.06	2700	0.3764	0.4602
0.2819	2.14	2800	0.3740	0.4499
0.22	2.21	2900	0.3811	0.4597
0.287	2.29	3000	0.3562	0.4334
0.2531	2.36	3100	0.3700	0.4442
0.3143	2.44	3200	0.3548	0.4333
0.203	2.52	3300	0.3659	0.4558
0.2609	2.59	3400	0.3557	0.4468
0.191	2.67	3500	0.3476	0.4281
0.1354	2.75	3600	0.3650	0.4354
0.2345	2.82	3700	0.3479	0.4385
0.1951	2.9	3800	0.3508	0.4489
0.2991	2.97	3900	0.3585	0.4356
0.1579	3.05	4000	0.3603	0.4326
0.2319	3.13	4100	0.3442	0.4201
0.1941	3.2	4200	0.3344	0.4116
0.2561	3.28	4300	0.3475	0.4200
0.3208	3.36	4400	0.3505	0.4089
0.2555	3.43	4500	0.3593	0.4271
0.1927	3.51	4600	0.3536	0.4299
0.1994	3.59	4700	0.3672	0.4400
0.1357	3.66	4800	0.3433	0.4223
0.2043	3.74	4900	0.3471	0.4226
0.194	3.81	5000	0.3380	0.4230
0.1779	3.89	5100	0.3400	0.4130
0.1934	3.97	5200	0.3438	0.4104
0.1432	4.04	5300	0.3632	0.4254
0.1642	4.12	5400	0.3425	0.4237
0.2208	4.2	5500	0.3580	0.4132
0.1923	4.27	5600	0.3469	0.4143
0.2084	4.35	5700	0.3619	0.4252
0.2484	4.42	5800	0.3452	0.4210
0.1899	4.5	5900	0.3465	0.4136
0.1253	4.58	6000	0.3625	0.4150
0.1353	4.65	6100	0.3415	0.4182
0.2264	4.73	6200	0.3446	0.4153
0.2016	4.81	6300	0.3343	0.4087
0.1634	4.88	6400	0.3500	0.4253
0.2517	4.96	6500	0.3453	0.4291
0.1826	5.03	6600	0.3442	0.4106
0.174	5.11	6700	0.3478	0.3999
0.271	5.19	6800	0.3423	0.4023
0.1812	5.26	6900	0.3679	0.4200
0.3	5.34	7000	0.3583	0.4191
0.2678	5.42	7100	0.3534	0.4141
0.236	5.49	7200	0.3361	0.4041
0.1558	5.57	7300	0.3495	0.4126
0.2603	5.64	7400	0.3359	0.3969
0.1285	5.72	7500	0.3296	0.3994
0.4608	5.8	7600	0.3453	0.3933
0.1516	5.87	7700	0.3509	0.4028
0.2655	5.95	7800	0.3607	0.4109
0.22	6.03	7900	0.3392	0.3850
0.0787	6.1	8000	0.3395	0.3842
0.1297	6.18	8100	0.3356	0.3822
0.1747	6.25	8200	0.3275	0.3874
0.1647	6.33	8300	0.3554	0.3941
0.1314	6.41	8400	0.3287	0.3826
0.1264	6.48	8500	0.3122	0.3876
0.1229	6.56	8600	0.3525	0.3994
0.108	6.64	8700	0.3387	0.3968
0.185	6.71	8800	0.3333	0.3840
0.0924	6.79	8900	0.3366	0.3827
0.1226	6.86	9000	0.3243	0.3788
0.2005	6.94	9100	0.3324	0.3765
0.133	7.02	9200	0.3294	0.3688
0.0633	7.09	9300	0.3279	0.3738
0.0593	7.17	9400	0.3311	0.3639
0.088	7.25	9500	0.3221	0.3765
0.1489	7.32	9600	0.3421	0.3788
0.1175	7.4	9700	0.3191	0.3786
0.0983	7.48	9800	0.3303	0.3764
0.1493	7.55	9900	0.3371	0.3836
0.1091	7.63	10000	0.3410	0.3739
0.1058	7.7	10100	0.3262	0.3730
0.0849	7.78	10200	0.3379	0.3812
0.1362	7.86	10300	0.3291	0.3781
0.1227	7.93	10400	0.3235	0.3760
0.1647	8.01	10500	0.3285	0.3686
0.1013	8.09	10600	0.3319	0.3729
0.1432	8.16	10700	0.3280	0.3731
0.1345	8.24	10800	0.3237	0.3707
0.0813	8.31	10900	0.3285	0.3748
0.1063	8.39	11000	0.3321	0.3748
0.1342	8.47	11100	0.3171	0.3647
0.1202	8.54	11200	0.3209	0.3636
0.0987	8.62	11300	0.3224	0.3625
0.1357	8.7	11400	0.3245	0.3646
0.1038	8.77	11500	0.3172	0.3702
0.0961	8.85	11600	0.3080	0.3611
0.1836	8.92	11700	0.3112	0.3681
0.0951	9.0	11800	0.3157	0.3649
0.1162	9.08	11900	0.3188	0.3714
0.1157	9.15	12000	0.3383	0.3775
0.1268	9.23	12100	0.3204	0.3752
0.1402	9.31	12200	0.3441	0.3707
0.1094	9.38	12300	0.3415	0.3675
0.1122	9.46	12400	0.3150	0.3596
0.0932	9.53	12500	0.3195	0.3561
0.1176	9.61	12600	0.3250	0.3675
0.1287	9.69	12700	0.3253	0.3615
0.0886	9.76	12800	0.3276	0.3636
0.1016	9.84	12900	0.3185	0.3592
0.0902	9.92	13000	0.3177	0.3643
0.1304	9.99	13100	0.3131	0.3530
0.099	10.07	13200	0.3094	0.3525
0.1142	10.14	13300	0.3298	0.3609
0.1836	10.22	13400	0.3213	0.3526
0.1533	10.3	13500	0.3163	0.3579
0.1436	10.37	13600	0.3352	0.3543
0.1215	10.45	13700	0.3355	0.3458
0.0971	10.53	13800	0.3232	0.3579
0.1215	10.6	13900	0.3168	0.3441
0.0906	10.68	14000	0.3266	0.3498
0.125	10.76	14100	0.3318	0.3414
0.0831	10.83	14200	0.3030	0.3480
0.1588	10.91	14300	0.3155	0.3455
0.1191	10.98	14400	0.3287	0.3487
0.074	11.06	14500	0.3176	0.3431
0.1075	11.14	14600	0.3219	0.3446
0.0679	11.21	14700	0.3158	0.3414
0.0789	11.29	14800	0.3305	0.3491
0.1426	11.37	14900	0.3281	0.3485
0.1154	11.44	15000	0.3368	0.3482
0.1313	11.52	15100	0.3285	0.3415
0.0786	11.59	15200	0.3138	0.3439
0.0595	11.67	15300	0.3135	0.3431
0.0868	11.75	15400	0.3049	0.3396
0.0812	11.82	15500	0.3050	0.3373
0.1199	11.9	15600	0.3238	0.3392
0.1243	11.98	15700	0.3123	0.3368
0.0663	12.05	15800	0.3226	0.3373
0.0285	12.13	15900	0.3260	0.3367
0.0607	12.2	16000	0.3236	0.3406
0.064	12.28	16100	0.3297	0.3357
0.0554	12.36	16200	0.3357	0.3383
0.0561	12.43	16300	0.3211	0.3387
0.0785	12.51	16400	0.3140	0.3386
0.0539	12.59	16500	0.3130	0.3361
0.0873	12.66	16600	0.3244	0.3344
0.0774	12.74	16700	0.3128	0.3274
0.0853	12.81	16800	0.3185	0.3395
0.0701	12.89	16900	0.3244	0.3327
0.0486	12.97	17000	0.3100	0.3317
0.1087	13.04	17100	0.3351	0.3327
0.0716	13.12	17200	0.3474	0.3383
0.0653	13.2	17300	0.3361	0.3364
0.0936	13.27	17400	0.3423	0.3352
0.0761	13.35	17500	0.3261	0.3304
0.0723	13.42	17600	0.3298	0.3333
0.0756	13.5	17700	0.3282	0.3367
0.058	13.58	17800	0.3386	0.3303
0.0619	13.65	17900	0.3354	0.3306
0.081	13.73	18000	0.3413	0.3317
0.0893	13.81	18100	0.3257	0.3278
0.0858	13.88	18200	0.3312	0.3255
0.0756	13.96	18300	0.3279	0.3326
0.0946	14.04	18400	0.3412	0.3272
0.1452	14.11	18500	0.3394	0.3266
0.0772	14.19	18600	0.3271	0.3261
0.0748	14.26	18700	0.3338	0.3272
0.0789	14.34	18800	0.3461	0.3254
0.0967	14.42	18900	0.3163	0.3250
0.0938	14.49	19000	0.3273	0.3261
0.1134	14.57	19100	0.3301	0.3284
0.1051	14.65	19200	0.3187	0.3215
0.0936	14.72	19300	0.3211	0.3197
0.0528	14.8	19400	0.3381	0.3270
0.1497	14.87	19500	0.3291	0.3235
0.1168	14.95	19600	0.3290	0.3238
0.028	15.03	19700	0.3333	0.3209
0.0773	15.1	19800	0.3359	0.3206
0.0972	15.18	19900	0.3262	0.3163
0.0391	15.26	20000	0.3335	0.3180
0.0571	15.33	20100	0.3445	0.3198
0.0365	15.41	20200	0.3318	0.3170
0.0535	15.48	20300	0.3257	0.3147
0.0739	15.56	20400	0.3359	0.3136
0.0753	15.64	20500	0.3216	0.3195
0.1507	15.71	20600	0.3326	0.3154
0.062	15.79	20700	0.3310	0.3164
0.0595	15.87	20800	0.3134	0.3162
0.0456	15.94	20900	0.3146	0.3127
0.0977	16.02	21000	0.3328	0.3117
0.036	16.09	21100	0.3266	0.3134
0.0308	16.17	21200	0.3306	0.3136
0.0612	16.25	21300	0.3207	0.3160
0.0269	16.32	21400	0.3429	0.3143
0.0897	16.4	21500	0.3355	0.3111
0.0458	16.48	21600	0.3238	0.3065
0.0155	16.55	21700	0.3167	0.3042
0.0519	16.63	21800	0.3296	0.3099
0.0807	16.7	21900	0.3250	0.3048
0.0406	16.78	22000	0.3283	0.3087
0.0773	16.86	22100	0.3217	0.3047
0.1027	16.93	22200	0.3279	0.3108
0.0315	17.01	22300	0.3173	0.3058
0.0457	17.09	22400	0.3387	0.3085
0.0516	17.16	22500	0.3309	0.3050
0.0413	17.24	22600	0.3363	0.3067
0.0601	17.32	22700	0.3325	0.3048
0.0435	17.39	22800	0.3298	0.3058
0.0571	17.47	22900	0.3244	0.3033
0.0656	17.54	23000	0.3350	0.3056
0.0485	17.62	23100	0.3406	0.3051
0.0619	17.7	23200	0.3268	0.3033
0.0495	17.77	23300	0.3268	0.3031
0.0416	17.85	23400	0.3268	0.3038
0.0646	17.93	23500	0.3314	0.3009
0.0294	18.0	23600	0.3251	0.3028
0.0372	18.08	23700	0.3364	0.2962
0.04	18.15	23800	0.3358	0.2967
0.0367	18.23	23900	0.3317	0.3031
0.0312	18.31	24000	0.3272	0.2998
0.0419	18.38	24100	0.3358	0.2996
0.0477	18.46	24200	0.3283	0.2996
0.0256	18.54	24300	0.3310	0.2995
0.0269	18.61	24400	0.3325	0.2997
0.0309	18.69	24500	0.3345	0.2974
0.0441	18.76	24600	0.3345	0.3003
0.0496	18.84	24700	0.3396	0.2985
0.0425	18.92	24800	0.3425	0.2965
0.0196	18.99	24900	0.3373	0.2964
0.0348	19.07	25000	0.3361	0.2955
0.0466	19.15	25100	0.3328	0.2959
0.0422	19.22	25200	0.3343	0.2964
0.0271	19.3	25300	0.3369	0.2945
0.053	19.37	25400	0.3330	0.2953
0.0662	19.45	25500	0.3343	0.2958
0.0718	19.53	25600	0.3330	0.2952
0.0212	19.6	25700	0.3352	0.2940
0.0971	19.68	25800	0.3374	0.2935
0.0413	19.76	25900	0.3362	0.2933
0.0477	19.83	26000	0.3356	0.2940
0.1068	19.91	26100	0.3365	0.2937
0.108	19.98	26200	0.3366	0.2935

Framework versions

Transformers 4.29.2
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.2

hippopotam/wav2vec2-common_voice13

wav2vec2-common_voice13

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hippopotam/
wav2vec2-common_voice13