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: 1.6717
Wer: 0.9321
Cer: 0.2636

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: 5e-05
train_batch_size: 13
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 26
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
No log	1.9	400	44.1474	1.0	1.0
69.9134	3.81	800	6.6651	1.0	1.0
7.6107	5.71	1200	6.5059	1.0	1.0
6.4614	7.62	1600	6.4456	1.0	1.0
6.3268	9.52	2000	6.3535	1.0	1.0000
6.3268	11.43	2400	6.1350	1.0063	0.9689
6.0793	13.33	2800	5.1209	1.0325	0.8288
5.2214	15.24	3200	4.3109	1.0148	0.6910
4.1439	17.14	3600	3.8123	1.0280	0.6100
3.558	19.05	4000	3.4451	1.0091	0.5639
3.558	20.95	4400	3.0901	1.0091	0.5271
3.0826	22.86	4800	2.7863	1.0080	0.4950
2.6891	24.76	5200	2.5825	1.0	0.4749
2.3535	26.67	5600	2.3098	1.0029	0.4420
2.0582	28.57	6000	2.1619	0.9960	0.4246
2.0582	30.48	6400	2.0340	0.9960	0.4097
1.8555	32.38	6800	1.9168	0.9874	0.3966
1.6825	34.29	7200	1.8196	0.9806	0.3849
1.4992	36.19	7600	1.7298	0.9863	0.3751
1.3534	38.1	8000	1.6588	0.9897	0.3674
1.3534	40.0	8400	1.6227	0.9823	0.3629
1.2413	41.9	8800	1.5783	0.9914	0.3556
1.1372	43.81	9200	1.5328	0.9755	0.3453
1.029	45.71	9600	1.5342	0.9726	0.3448
0.9409	47.62	10000	1.4863	0.9760	0.3362
0.9409	49.52	10400	1.4664	0.9715	0.3313
0.8615	51.43	10800	1.4799	0.9669	0.3337
0.7901	53.33	11200	1.4427	0.9635	0.3200
0.7302	55.24	11600	1.4679	0.9561	0.3244
0.6738	57.14	12000	1.4386	0.9521	0.3163
0.6738	59.05	12400	1.4515	0.9492	0.3139
0.6116	60.95	12800	1.4543	0.9526	0.3132
0.5558	62.86	13200	1.4303	0.9635	0.3079
0.504	64.76	13600	1.4425	0.9503	0.3045
0.4573	66.67	14000	1.4330	0.9515	0.3039
0.4573	68.57	14400	1.4553	0.9521	0.3017
0.429	70.48	14800	1.4727	0.9492	0.3031
0.3906	72.38	15200	1.4562	0.9458	0.3001
0.3664	74.29	15600	1.4699	0.9469	0.3007
0.3331	76.19	16000	1.4874	0.9515	0.3019
0.3331	78.1	16400	1.4866	0.9395	0.2999
0.3189	80.0	16800	1.4830	0.9412	0.2990
0.2963	81.9	17200	1.5135	0.9486	0.2984
0.2839	83.81	17600	1.5121	0.9475	0.2953
0.2602	85.71	18000	1.5313	0.9401	0.2990
0.2602	87.62	18400	1.5082	0.9418	0.2902
0.2515	89.52	18800	1.5320	0.9458	0.2969
0.2371	91.43	19200	1.5632	0.9446	0.2974
0.2327	93.33	19600	1.5268	0.9441	0.2942
0.2097	95.24	20000	1.5531	0.9406	0.2986
0.2097	97.14	20400	1.5443	0.9412	0.2923
0.1975	99.05	20800	1.5483	0.9418	0.2918
0.1886	100.95	21200	1.5669	0.9401	0.2909
0.1831	102.86	21600	1.5583	0.9389	0.2897
0.1762	104.76	22000	1.5557	0.9441	0.2904
0.1762	106.67	22400	1.5734	0.9366	0.2877
0.1681	108.57	22800	1.5873	0.9418	0.2917
0.1681	110.48	23200	1.5834	0.9395	0.2898
0.1542	112.38	23600	1.5941	0.9395	0.2861
0.1532	114.29	24000	1.5816	0.9424	0.2862
0.1532	116.19	24400	1.5806	0.9384	0.2850
0.1471	118.1	24800	1.5898	0.9418	0.2859
0.138	120.0	25200	1.6176	0.9412	0.2895
0.1361	121.9	25600	1.5888	0.9395	0.2865
0.1328	123.81	26000	1.6248	0.9418	0.2855
0.1328	125.71	26400	1.5954	0.9424	0.2864
0.123	127.62	26800	1.6179	0.9389	0.2844
0.1213	129.52	27200	1.6266	0.9418	0.2847
0.115	131.43	27600	1.6193	0.9406	0.2815
0.1137	133.33	28000	1.6255	0.9481	0.2868
0.1137	135.24	28400	1.6178	0.9378	0.2818
0.1089	137.14	28800	1.6339	0.9401	0.2827
0.1107	139.05	29200	1.6422	0.9378	0.2837
0.101	140.95	29600	1.6294	0.9418	0.2815
0.1006	142.86	30000	1.6290	0.9395	0.2843
0.1006	144.76	30400	1.6260	0.9384	0.2816
0.0991	146.67	30800	1.6283	0.9395	0.2801
0.0988	148.57	31200	1.6348	0.9435	0.2818
0.1	150.48	31600	1.6505	0.9435	0.2784
0.0927	152.38	32000	1.6468	0.9441	0.2798
0.0927	154.29	32400	1.6486	0.9424	0.2769
0.0851	156.19	32800	1.6455	0.9452	0.2811
0.089	158.1	33200	1.6307	0.9378	0.2776
0.0863	160.0	33600	1.6386	0.9355	0.2792
0.0852	161.9	34000	1.6244	0.9361	0.2757
0.0852	163.81	34400	1.6420	0.9389	0.2762
0.0835	165.71	34800	1.6474	0.9395	0.2749
0.0802	167.62	35200	1.6536	0.9406	0.2782
0.0787	169.52	35600	1.6594	0.9452	0.2796
0.0798	171.43	36000	1.6528	0.9366	0.2757
0.0798	173.33	36400	1.6518	0.9389	0.2747
0.0731	175.24	36800	1.6534	0.9406	0.2764
0.0745	177.14	37200	1.6638	0.9429	0.2770
0.0714	179.05	37600	1.6393	0.9406	0.2754
0.0694	180.95	38000	1.6421	0.9406	0.2746
0.0694	182.86	38400	1.6625	0.9378	0.2755
0.0727	184.76	38800	1.6549	0.9389	0.2753
0.0673	186.67	39200	1.6575	0.9406	0.2756
0.0695	188.57	39600	1.6684	0.9389	0.2737
0.0664	190.48	40000	1.6717	0.9429	0.2757
0.0664	192.38	40400	1.6636	0.9406	0.2751
0.0643	194.29	40800	1.6702	0.9441	0.2732
0.0627	196.19	41200	1.6517	0.9338	0.2735
0.0617	198.1	41600	1.6519	0.9378	0.2711
0.059	200.0	42000	1.6579	0.9378	0.2732
0.059	201.9	42400	1.6542	0.9315	0.2701
0.0595	203.81	42800	1.6607	0.9361	0.2713
0.0602	205.71	43200	1.6488	0.9378	0.2717
0.0581	207.62	43600	1.6651	0.9384	0.2693
0.0573	209.52	44000	1.6697	0.9384	0.2715
0.0573	211.43	44400	1.6585	0.9384	0.2708
0.0523	213.33	44800	1.6599	0.9372	0.2723
0.0541	215.24	45200	1.6683	0.9384	0.2732
0.0607	217.14	45600	1.6696	0.9355	0.2718
0.0512	219.05	46000	1.6733	0.9384	0.2740
0.0512	220.95	46400	1.6803	0.9372	0.2709
0.0515	222.86	46800	1.6684	0.9366	0.2692
0.0507	224.76	47200	1.6695	0.9315	0.2710
0.0502	226.67	47600	1.6789	0.9372	0.2692
0.0496	228.57	48000	1.6619	0.9326	0.2705
0.0496	230.48	48400	1.6707	0.9355	0.2705
0.049	232.38	48800	1.6655	0.9361	0.2688
0.0482	234.29	49200	1.6643	0.9389	0.2706
0.047	236.19	49600	1.6750	0.9355	0.2706
0.0474	238.1	50000	1.6913	0.9395	0.2719
0.0474	240.0	50400	1.6857	0.9384	0.2702
0.0458	241.9	50800	1.6815	0.9349	0.2664
0.0456	243.81	51200	1.6596	0.9378	0.2672
0.0423	245.71	51600	1.6623	0.9315	0.2664
0.0436	247.62	52000	1.6647	0.9344	0.2680
0.0436	249.52	52400	1.6706	0.9326	0.2671
0.043	251.43	52800	1.6751	0.9321	0.2687
0.043	253.33	53200	1.6706	0.9292	0.2688
0.0424	255.24	53600	1.6663	0.9304	0.2686
0.0431	257.14	54000	1.6684	0.9321	0.2674
0.0431	259.05	54400	1.6758	0.9309	0.2679
0.0405	260.95	54800	1.6666	0.9298	0.2667
0.0413	262.86	55200	1.6773	0.9315	0.2661
0.0393	264.76	55600	1.6702	0.9287	0.2650
0.0408	266.67	56000	1.6664	0.9332	0.2658
0.0408	268.57	56400	1.6738	0.9326	0.2660
0.0381	270.48	56800	1.6668	0.9332	0.2651
0.0383	272.38	57200	1.6747	0.9338	0.2651
0.0367	274.29	57600	1.6667	0.9304	0.2639
0.0393	276.19	58000	1.6743	0.9321	0.2643
0.0393	278.1	58400	1.6712	0.9326	0.2644
0.0377	280.0	58800	1.6759	0.9321	0.2637
0.0379	281.9	59200	1.6714	0.9315	0.2635
0.0376	283.81	59600	1.6798	0.9326	0.2643
0.0374	285.71	60000	1.6803	0.9338	0.2638
0.0374	287.62	60400	1.6764	0.9332	0.2645
0.0372	289.52	60800	1.6796	0.9326	0.2634
0.0354	291.43	61200	1.6762	0.9309	0.2628
0.0365	293.33	61600	1.6761	0.9315	0.2627
0.0349	295.24	62000	1.6746	0.9321	0.2631
0.0349	297.14	62400	1.6716	0.9321	0.2636
0.0345	299.05	62800	1.6717	0.9321	0.2636

Framework versions

Transformers 4.31.0
Pytorch 1.13.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

scarlett623/wav2vec2-commonvoice-20subset-xls-r-300m-gpu1

wav2vec2-commonvoice-20subset-xls-r-300m-gpu1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

scarlett623/
wav2vec2-commonvoice-20subset-xls-r-300m-gpu1