yerevann/x-r-hy

wav2vec2-large-xls-r-2b-armenian-colab

This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 1.5166
• Wer: 0.7397

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: 0.0001
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 120
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.7057	2.38	200	0.7731	0.8091
0.5797	4.76	400	0.8279	0.7804
0.4341	7.14	600	1.0343	0.8285
0.3135	9.52	800	1.0551	0.8066
0.2409	11.9	1000	1.0686	0.7897
0.1998	14.29	1200	1.1329	0.7766
0.1729	16.67	1400	1.3234	0.8567
0.1533	19.05	1600	1.2432	0.8160
0.1354	21.43	1800	1.2780	0.7954
0.12	23.81	2000	1.2228	0.8054
0.1175	26.19	2200	1.3484	0.8129
0.1141	28.57	2400	1.2881	0.9130
0.1053	30.95	2600	1.1972	0.7910
0.0954	33.33	2800	1.3702	0.8048
0.0842	35.71	3000	1.3963	0.7960
0.0793	38.1	3200	1.4690	0.7991
0.0707	40.48	3400	1.5045	0.8085
0.0745	42.86	3600	1.4749	0.8004
0.0693	45.24	3800	1.5047	0.7960
0.0646	47.62	4000	1.4216	0.7997
0.0555	50.0	4200	1.4676	0.8029
0.056	52.38	4400	1.4273	0.8104
0.0465	54.76	4600	1.3999	0.7841
0.046	57.14	4800	1.6130	0.8473
0.0404	59.52	5000	1.5586	0.7841
0.0403	61.9	5200	1.3959	0.7653
0.0404	64.29	5400	1.5318	0.8041
0.0365	66.67	5600	1.5300	0.7854
0.0338	69.05	5800	1.5051	0.7885
0.0307	71.43	6000	1.5647	0.7935
0.0235	73.81	6200	1.4919	0.8154
0.0268	76.19	6400	1.5259	0.8060
0.0275	78.57	6600	1.3985	0.7897
0.022	80.95	6800	1.5515	0.8154
0.017	83.33	7000	1.5737	0.7647
0.0205	85.71	7200	1.4876	0.7572
0.0174	88.1	7400	1.6331	0.7829
0.0188	90.48	7600	1.5108	0.7685
0.0134	92.86	7800	1.7125	0.7866
0.0125	95.24	8000	1.6042	0.7635
0.0133	97.62	8200	1.4608	0.7478
0.0272	100.0	8400	1.4784	0.7309
0.0133	102.38	8600	1.4471	0.7459
0.0094	104.76	8800	1.4852	0.7272
0.0103	107.14	9000	1.5679	0.7409
0.0088	109.52	9200	1.5090	0.7309
0.0077	111.9	9400	1.4994	0.7290
0.0068	114.29	9600	1.5008	0.7340
0.0054	116.67	9800	1.5166	0.7390
0.0052	119.05	10000	1.5166	0.7397

Framework versions

• Transformers 4.14.1
• Pytorch 1.10.0
• Datasets 1.16.1
• Tokenizers 0.10.3