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

Loss: 0.2855
Wer: 0.2665

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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: 500
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.3386	0.66	400	1.1411	0.9432
0.6543	1.33	800	0.5099	0.6749
0.4646	1.99	1200	0.4133	0.5968
0.3748	2.65	1600	0.3534	0.5515
0.3323	3.32	2000	0.3635	0.5527
0.3269	3.98	2400	0.3587	0.5423
0.2984	4.64	2800	0.3340	0.5073
0.2841	5.31	3200	0.3279	0.5004
0.2664	5.97	3600	0.3114	0.4845
0.2397	6.63	4000	0.3174	0.4920
0.2332	7.3	4400	0.3110	0.4911
0.2304	7.96	4800	0.3123	0.4785
0.2134	8.62	5200	0.2984	0.4557
0.2066	9.29	5600	0.3013	0.4723
0.1951	9.95	6000	0.2934	0.4487
0.1806	10.61	6400	0.2802	0.4547
0.1727	11.28	6800	0.2842	0.4333
0.1666	11.94	7200	0.2873	0.4272
0.1562	12.6	7600	0.3042	0.4373
0.1483	13.27	8000	0.3122	0.4313
0.1465	13.93	8400	0.2760	0.4226
0.1335	14.59	8800	0.3112	0.4243
0.1293	15.26	9200	0.3002	0.4133
0.1264	15.92	9600	0.2985	0.4145
0.1179	16.58	10000	0.2925	0.4012
0.1171	17.25	10400	0.3127	0.4012
0.1141	17.91	10800	0.2980	0.3908
0.108	18.57	11200	0.3108	0.3951
0.1045	19.24	11600	0.3269	0.3908
0.1047	19.9	12000	0.2998	0.3868
0.0937	20.56	12400	0.2918	0.3875
0.0949	21.23	12800	0.2906	0.3657
0.0879	21.89	13200	0.2974	0.3731
0.0854	22.55	13600	0.2943	0.3711
0.0851	23.22	14000	0.2919	0.3580
0.0789	23.88	14400	0.2983	0.3560
0.0796	24.54	14800	0.3131	0.3544
0.0761	25.21	15200	0.2996	0.3616
0.0755	25.87	15600	0.2972	0.3506
0.0726	26.53	16000	0.2902	0.3474
0.0707	27.2	16400	0.3083	0.3480
0.0669	27.86	16800	0.3035	0.3330
0.0637	28.52	17200	0.2963	0.3370
0.0596	29.19	17600	0.2830	0.3326
0.0583	29.85	18000	0.2969	0.3287
0.0566	30.51	18400	0.3002	0.3480
0.0574	31.18	18800	0.2916	0.3296
0.0536	31.84	19200	0.2933	0.3225
0.0548	32.5	19600	0.2900	0.3179
0.0506	33.17	20000	0.3073	0.3225
0.0511	33.83	20400	0.2925	0.3275
0.0483	34.49	20800	0.2919	0.3245
0.0456	35.16	21200	0.2859	0.3105
0.0445	35.82	21600	0.2864	0.3080
0.0437	36.48	22000	0.2989	0.3084
0.04	37.15	22400	0.2887	0.3060
0.0406	37.81	22800	0.2870	0.3013
0.0397	38.47	23200	0.2793	0.3020
0.0383	39.14	23600	0.2955	0.2943
0.0345	39.8	24000	0.2813	0.2905
0.0331	40.46	24400	0.2845	0.2845
0.0338	41.13	24800	0.2832	0.2925
0.0333	41.79	25200	0.2889	0.2849
0.0325	42.45	25600	0.2808	0.2847
0.0314	43.12	26000	0.2867	0.2801
0.0288	43.78	26400	0.2865	0.2834
0.0291	44.44	26800	0.2863	0.2806
0.0269	45.11	27200	0.2941	0.2736
0.0275	45.77	27600	0.2897	0.2736
0.0271	46.43	28000	0.2857	0.2695
0.0251	47.1	28400	0.2881	0.2702
0.0243	47.76	28800	0.2901	0.2684
0.0244	48.42	29200	0.2849	0.2679
0.0232	49.09	29600	0.2849	0.2677
0.0224	49.75	30000	0.2855	0.2665

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.0

shiimi/xls-r-300m-dv

xls-r-300m-dv

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

shiimi/
xls-r-300m-dv