Modelpublic

hf-test/xls-r-300m-sv-cv8

sourceHugging Faceapache-2.0updated 5y agoView on Hugging Face

1likes154downloads

Model Card

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMONVOICE8_0 - SV-SE dataset. It achieves the following results on the evaluation set:

Without LM:

—Wer: 0.2465
—Cer: 0.0717

With LM:

—Wer: 0.1710
—Cer: 0.0569

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
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 32
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 2000
—num_epochs: 50.0
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.3224	1.37	500	3.2676	1.0
2.9319	2.74	1000	2.9287	1.0000
2.1173	4.11	1500	1.1478	0.8788
1.6973	5.48	2000	0.6749	0.6547
1.5865	6.85	2500	0.5500	0.5634
1.5094	8.22	3000	0.4840	0.5430
1.4644	9.59	3500	0.4844	0.4142
1.4061	10.96	4000	0.4356	0.3808
1.3584	12.33	4500	0.4192	0.3698
1.3438	13.7	5000	0.3980	0.3584
1.3332	15.07	5500	0.3896	0.3572
1.3025	16.44	6000	0.3835	0.3487
1.2979	17.81	6500	0.3781	0.3417
1.2736	19.18	7000	0.3734	0.3270
1.2415	20.55	7500	0.3637	0.3316
1.2255	21.92	8000	0.3546	0.3147
1.2193	23.29	8500	0.3524	0.3196
1.2104	24.66	9000	0.3403	0.3097
1.1965	26.03	9500	0.3508	0.3093
1.1976	27.4	10000	0.3419	0.3071
1.182	28.77	10500	0.3364	0.2963
1.158	30.14	11000	0.3338	0.2932
1.1414	31.51	11500	0.3376	0.2940
1.1402	32.88	12000	0.3370	0.2891
1.1213	34.25	12500	0.3201	0.2874
1.1207	35.62	13000	0.3261	0.2826
1.1074	36.98	13500	0.3117	0.2786
1.0818	38.36	14000	0.3194	0.2776
1.0889	39.73	14500	0.3188	0.2738
1.0672	41.1	15000	0.3196	0.2773
1.0838	42.47	15500	0.3130	0.2739
1.0553	43.83	16000	0.3165	0.2704
1.0786	45.21	16500	0.3108	0.2706
1.0546	46.57	17000	0.3102	0.2677
1.0425	47.94	17500	0.3115	0.2679
1.0398	49.31	18000	0.3131	0.2666

Framework versions

—Transformers 4.16.0.dev0
—Pytorch 1.10.1+cu113
—Datasets 1.18.1.dev0
—Tokenizers 0.10.3