Helldivers2ASR

This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 45.7976
• Wer: 0.0486
• Cer: 0.0203

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.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_ratio: 0.1
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1069.0904	1.0	111	690.8810	0.5789	0.3204
812.3311	2.0	222	524.6868	0.4769	0.2529
677.891	3.0	333	423.8824	0.4040	0.2043
584.6176	4.0	444	338.5651	0.3429	0.1709
510.1064	5.0	555	284.0197	0.3016	0.1467
462.5595	6.0	666	245.5092	0.2615	0.1233
398.9107	7.0	777	205.1819	0.2198	0.1036
375.3196	8.0	888	183.1224	0.2049	0.0943
338.0957	9.0	999	163.4431	0.1834	0.0832
305.4381	10.0	1110	158.5278	0.1636	0.0759
297.0845	11.0	1221	140.0732	0.1603	0.0715
280.9225	12.0	1332	128.1153	0.1453	0.0633
274.2178	13.0	1443	115.9890	0.1283	0.0581
238.5611	14.0	1554	112.8672	0.1271	0.0572
233.9152	15.0	1665	105.2606	0.1178	0.0517
222.8375	16.0	1776	105.7286	0.1093	0.0522
218.3437	17.0	1887	100.8668	0.1089	0.0480
208.0329	18.0	1998	96.2257	0.1020	0.0457
199.382	19.0	2109	85.2498	0.0939	0.0433
198.6175	20.0	2220	82.4100	0.0927	0.0425
173.409	21.0	2331	78.6151	0.0842	0.0377
168.3968	22.0	2442	79.7964	0.0830	0.0382
171.8005	23.0	2553	70.0593	0.0773	0.0336
157.5571	24.0	2664	67.0374	0.0713	0.0308
157.863	25.0	2775	61.6305	0.0684	0.0299
153.1922	26.0	2886	64.7148	0.0676	0.0305
158.7052	27.0	2997	59.4825	0.0676	0.0286
149.8696	28.0	3108	59.3228	0.0615	0.0290
145.0604	29.0	3219	57.5999	0.0595	0.0273
138.6555	30.0	3330	56.0322	0.0587	0.0273
145.2049	31.0	3441	51.0521	0.0559	0.0262
137.6945	32.0	3552	52.0388	0.0510	0.0236
130.1284	33.0	3663	51.2634	0.0587	0.0256
127.2604	34.0	3774	47.7200	0.0510	0.0239
124.2158	35.0	3885	47.9998	0.0530	0.0233
116.4625	36.0	3996	50.8454	0.0547	0.0236
125.1983	37.0	4107	47.3148	0.0490	0.0212
110.8833	38.0	4218	47.1674	0.0522	0.0229
109.9017	39.0	4329	46.9405	0.0506	0.0224
116.4361	40.0	4440	49.4927	0.0482	0.0221
117.1769	41.0	4551	46.7733	0.0474	0.0212
108.7644	42.0	4662	45.9081	0.0490	0.0209
117.5977	43.0	4773	43.4770	0.0462	0.0202
125.1272	44.0	4884	41.2925	0.0482	0.0206
110.6224	45.0	4995	47.2408	0.0498	0.0211
110.3162	46.0	5106	46.2208	0.0437	0.0196
107.2695	47.0	5217	44.8825	0.0433	0.0199
111.1748	48.0	5328	45.3059	0.0425	0.0191
108.1643	49.0	5439	45.5355	0.0449	0.0199
107.1299	50.0	5550	45.7976	0.0486	0.0203

Framework versions

• Transformers 4.57.3
• Pytorch 2.5.1+cu121
• Datasets 3.6.0
• Tokenizers 0.22.1