wav2vec2-large-xls-r-300m-ipa

This model was trained from scratch on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7309

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: 6
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 24
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 240
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.1677	3.6866	200	1.0381
0.1214	7.3733	400	0.5607
0.1272	11.0599	600	0.5442
0.135	14.7465	800	0.5933
0.0824	18.4332	1000	0.6316
0.0711	22.1198	1200	0.5971
0.0653	25.8065	1400	0.6050
0.0499	29.4931	1600	0.6699
0.0516	33.1797	1800	0.6940
0.0507	36.8664	2000	0.7045
0.0478	40.5530	2200	0.7603
0.045	44.2396	2400	0.7415
0.0419	47.9263	2600	0.7341
0.0344	51.6129	2800	0.7328
0.0354	55.2995	3000	0.8550
0.0268	58.9862	3200	0.7838
0.0383	62.6728	3400	0.7995
0.0371	66.3594	3600	0.7765
0.0264	70.0461	3800	0.8186
0.0212	73.7327	4000	0.7439
0.0177	77.4194	4200	0.7830
0.0204	81.1060	4400	0.8145
0.0254	84.7926	4600	0.8149
0.0257	88.4793	4800	0.7663
0.0126	92.1659	5000	0.7704
0.0196	95.8525	5200	0.7660
0.0185	99.5392	5400	0.8580
0.0236	103.2258	5600	0.8169
0.0141	106.9124	5800	0.8222
0.0142	110.5991	6000	0.9001
0.0098	114.2857	6200	0.8509
0.0372	117.9724	6400	0.7734
0.0075	121.6590	6600	0.8911
0.0118	125.3456	6800	0.8347
0.0115	129.0323	7000	0.8926
0.0164	132.7189	7200	0.7985
0.006	136.4055	7400	0.7571
0.0124	140.0922	7600	0.8476
0.0141	143.7788	7800	0.8071
0.0065	147.4654	8000	0.7630
0.0095	151.1521	8200	0.7161
0.0063	154.8387	8400	0.8165
0.0107	158.5253	8600	0.7411
0.0037	162.2120	8800	0.7424
0.0045	165.8986	9000	0.7611
0.0044	169.5853	9200	0.7278
0.0043	173.2719	9400	0.7396
0.0025	176.9585	9600	0.7215
0.0029	180.6452	9800	0.7551
0.0067	184.3318	10000	0.7518
0.0062	188.0184	10200	0.7668
0.0065	191.7051	10400	0.7433
0.0024	195.3917	10600	0.7942
0.0039	199.0783	10800	0.7448
0.0024	202.7650	11000	0.7290
0.0036	206.4516	11200	0.7678
0.0001	210.1382	11400	0.7390
0.0009	213.8249	11600	0.7292
0.0008	217.5115	11800	0.7383
0.0009	221.1982	12000	0.7435
0.0009	224.8848	12200	0.7324
0.0007	228.5714	12400	0.7444
0.0002	232.2581	12600	0.7228
0.0005	235.9447	12800	0.7309

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1