This model is a fine-tuned version of facebook/mms-1b-all on the /MNT/MD0/SYNVOICES/DATA/HAUSA_50_450H - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.3582
Wer: 0.3740
Cer: 0.0953

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: 0.001
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 2.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.2783	0.0363	500	0.4821	0.4350	0.1141
0.2249	0.0727	1000	0.4596	0.4268	0.1127
0.201	0.1090	1500	0.4493	0.4236	0.1122
0.2836	0.1453	2000	0.4501	0.4269	0.1122
0.294	0.1816	2500	0.4369	0.4279	0.1121
0.1831	0.2180	3000	0.4406	0.4227	0.1094
0.2472	0.2543	3500	0.4310	0.4306	0.1119
0.2312	0.2906	4000	0.4182	0.4116	0.1065
0.1278	0.3269	4500	0.4221	0.4164	0.1093
0.2569	0.3633	5000	0.4207	0.4193	0.1093
0.268	0.3996	5500	0.4085	0.4223	0.1083
0.1984	0.4359	6000	0.4078	0.4166	0.1082
0.1848	0.4722	6500	0.4134	0.4102	0.1065
0.1408	0.5086	7000	0.4032	0.4076	0.1052
0.1363	0.5449	7500	0.3993	0.4100	0.1054
0.1362	0.5812	8000	0.4061	0.4023	0.1038
0.12	0.6175	8500	0.3991	0.4084	0.1043
0.1341	0.6539	9000	0.4010	0.4113	0.1050
0.1619	0.6902	9500	0.3966	0.4132	0.1053
0.1958	0.7265	10000	0.3942	0.4064	0.1037
0.1029	0.7628	10500	0.4000	0.4070	0.1053
0.1623	0.7992	11000	0.3934	0.4167	0.1066
0.1601	0.8355	11500	0.3878	0.3950	0.1016
0.1164	0.8718	12000	0.3897	0.3962	0.1020
0.1425	0.9081	12500	0.3844	0.3936	0.1011
0.1415	0.9445	13000	0.3891	0.3991	0.1027
0.1581	0.9808	13500	0.3834	0.3967	0.1024
0.0911	1.0171	14000	0.3841	0.3966	0.1020
0.1681	1.0534	14500	0.3833	0.3936	0.1013
0.0936	1.0897	15000	0.3760	0.3928	0.1007
0.153	1.1260	15500	0.3785	0.4009	0.1019
0.1341	1.1624	16000	0.3754	0.4000	0.1021
0.1054	1.1987	16500	0.3736	0.3971	0.1013
0.1736	1.2350	17000	0.3765	0.3990	0.1019
0.1225	1.2714	17500	0.3725	0.3892	0.0998
0.1278	1.3077	18000	0.3712	0.3830	0.0982
0.1387	1.3440	18500	0.3734	0.3904	0.0997
0.1316	1.3803	19000	0.3707	0.3889	0.0990
0.1508	1.4167	19500	0.3728	0.3784	0.0969
0.1016	1.4530	20000	0.3710	0.3991	0.1003
0.1331	1.4893	20500	0.3667	0.3844	0.0982
0.1565	1.5256	21000	0.3638	0.3848	0.0979
0.2197	1.5620	21500	0.3638	0.3801	0.0967
0.1962	1.5983	22000	0.3653	0.3804	0.0968
0.1322	1.6346	22500	0.3637	0.3808	0.0967
0.0772	1.6709	23000	0.3619	0.3788	0.0962
0.1107	1.7073	23500	0.3637	0.3847	0.0972
0.1919	1.7436	24000	0.3645	0.3778	0.0962
0.0596	1.7799	24500	0.3619	0.3743	0.0953
0.1335	1.8162	25000	0.3598	0.3728	0.0951
0.1303	1.8526	25500	0.3603	0.3724	0.0950
0.1777	1.8889	26000	0.3591	0.3749	0.0955
0.1977	1.9252	26500	0.3586	0.3739	0.0953
0.1497	1.9615	27000	0.3577	0.3741	0.0953
0.1043	1.9979	27500	0.3582	0.3740	0.0953

Framework versions

Transformers 4.48.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

CLEAR-Global/mms-1b-50_450h-hau-ft

mms-1b-50_450h-hau-ft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

CLEAR-Global/
mms-1b-50_450h-hau-ft