HarrisDePerceptron/xls-r-300m-ur-cv8-hi

This model is a fine-tuned version of DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset. It achieves the following results on the evaluation set:

• Loss: 1.5443
• Wer: 0.7030

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: 0.000388
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 750
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
10.7052	1.96	100	3.4683	1.0
3.2395	3.92	200	3.1489	1.0
2.9951	5.88	300	2.9823	1.0007
2.3574	7.84	400	1.2614	0.7598
1.7287	9.8	500	1.1817	0.7421
1.6144	11.76	600	1.1315	0.7321
1.5598	13.73	700	1.2322	0.7550
1.5418	15.69	800	1.2721	0.7819
1.4578	17.65	900	1.1710	0.7531
1.4311	19.61	1000	1.2042	0.7491
1.3483	21.57	1100	1.1702	0.7465
1.3078	23.53	1200	1.1963	0.7421
1.2576	25.49	1300	1.1501	0.7280
1.2173	27.45	1400	1.2526	0.7299
1.2217	29.41	1500	1.2479	0.7310
1.1536	31.37	1600	1.2567	0.7432
1.0939	33.33	1700	1.2801	0.7247
1.0745	35.29	1800	1.2340	0.7151
1.0454	37.25	1900	1.2372	0.7151
1.0101	39.22	2000	1.2461	0.7376
0.9833	41.18	2100	1.2553	0.7269
0.9314	43.14	2200	1.2372	0.7015
0.9147	45.1	2300	1.3035	0.7358
0.8758	47.06	2400	1.2598	0.7092
0.8356	49.02	2500	1.2557	0.7144
0.8105	50.98	2600	1.2619	0.7236
0.7947	52.94	2700	1.3994	0.7491
0.7623	54.9	2800	1.2932	0.7133
0.7282	56.86	2900	1.2799	0.7089
0.7108	58.82	3000	1.3615	0.7148
0.6896	60.78	3100	1.3129	0.7041
0.6496	62.75	3200	1.4050	0.6934
0.6075	64.71	3300	1.3571	0.7026
0.6242	66.67	3400	1.3369	0.7063
0.5865	68.63	3500	1.4368	0.7140
0.5721	70.59	3600	1.4224	0.7066
0.5475	72.55	3700	1.4798	0.7118
0.5086	74.51	3800	1.5107	0.7232
0.4958	76.47	3900	1.4849	0.7089
0.5046	78.43	4000	1.4451	0.7114
0.4694	80.39	4100	1.4674	0.7089
0.4386	82.35	4200	1.5245	0.7103
0.4516	84.31	4300	1.5032	0.7103
0.4113	86.27	4400	1.5246	0.7196
0.3972	88.24	4500	1.5318	0.7114
0.4006	90.2	4600	1.5543	0.6982
0.4014	92.16	4700	1.5442	0.7048
0.3672	94.12	4800	1.5542	0.7137
0.3666	96.08	4900	1.5414	0.7018
0.3574	98.04	5000	1.5465	0.7059
0.3428	100.0	5100	1.5443	0.7030

Framework versions

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