xtreme_s_xlsr_300m_mls

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

• Loss: 0.6215
• Wer: 0.3033
• Cer: 0.0951

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: 4
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 3000
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.0446	1.91	500	2.9866	1.0	1.0
0.8789	3.82	1000	0.8574	0.7225	0.2355
0.4766	5.72	1500	0.4813	0.4624	0.1394
0.3779	7.63	2000	0.4465	0.4154	0.1309
0.3244	9.54	2500	0.4213	0.3683	0.1163
0.346	11.45	3000	0.4606	0.4033	0.1299
0.3092	13.36	3500	0.4160	0.3585	0.1115
0.3287	15.27	4000	0.4364	0.3631	0.1165
0.3165	17.18	4500	0.4218	0.3451	0.1056
0.2874	19.08	5000	0.4583	0.3650	0.1151
0.3089	20.99	5500	0.4424	0.3485	0.1137
0.2689	22.9	6000	0.4427	0.3542	0.1128
0.234	24.81	6500	0.4204	0.3431	0.1069
0.2363	26.72	7000	0.4792	0.3689	0.1191
0.2796	28.62	7500	0.4867	0.3662	0.1154
0.2447	30.53	8000	0.4908	0.3584	0.1160
0.22	32.44	8500	0.5315	0.3626	0.1240
0.1961	34.35	9000	0.5121	0.3610	0.1168
0.1959	36.26	9500	0.5140	0.3648	0.1179
0.1748	38.17	10000	0.5464	0.3763	0.1206
0.197	40.08	10500	0.5199	0.3515	0.1128
0.2166	41.98	11000	0.5336	0.3607	0.1191
0.2078	43.89	11500	0.5389	0.3518	0.1136
0.1827	45.8	12000	0.5014	0.3287	0.1053
0.1783	47.71	12500	0.5408	0.3545	0.1121
0.1489	49.62	13000	0.5292	0.3472	0.1098
0.1665	51.53	13500	0.5052	0.3300	0.1033
0.1631	53.43	14000	0.5241	0.3362	0.1081
0.1943	55.34	14500	0.5453	0.3373	0.1076
0.1504	57.25	15000	0.5958	0.3594	0.1149
0.136	59.16	15500	0.5645	0.3367	0.1082
0.1224	61.07	16000	0.5322	0.3302	0.1039
0.1156	62.98	16500	0.5728	0.3332	0.1061
0.114	64.88	17000	0.5994	0.3410	0.1125
0.1445	66.79	17500	0.6048	0.3471	0.1098
0.1281	68.7	18000	0.5747	0.3278	0.1042
0.1233	70.61	18500	0.6021	0.3375	0.1082
0.1109	72.52	19000	0.5851	0.3188	0.1021
0.0943	74.43	19500	0.5944	0.3238	0.1033
0.1418	76.34	20000	0.5904	0.3143	0.0997
0.1317	78.24	20500	0.6291	0.3283	0.1047
0.1177	80.15	21000	0.6114	0.3190	0.1000
0.1138	82.06	21500	0.6155	0.3245	0.1023
0.1074	83.97	22000	0.6094	0.3153	0.1004
0.11	85.88	22500	0.6041	0.3141	0.0988
0.1096	87.78	23000	0.6243	0.3110	0.0986
0.1017	89.69	23500	0.6110	0.3121	0.0984
0.1015	91.6	24000	0.6385	0.3093	0.0978
0.0952	93.51	24500	0.6155	0.3036	0.0953
0.0896	95.42	25000	0.6215	0.3033	0.0951
0.0953	97.33	25500	0.6293	0.3037	0.0953
0.0834	99.24	26000	0.6302	0.3036	0.0952

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 1.18.4.dev0
• Tokenizers 0.11.6