StevenLimcorn/wav2vec2-xls-r-300m-zh-TW

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

• Loss: 1.1786
• Wer: 0.8594
• Cer: 0.2964

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: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
64.6189	2.51	500	63.8077	1.0	1.0
8.0561	5.03	1000	6.8014	1.0	1.0
6.0427	7.54	1500	6.0745	1.0	1.0
5.9357	10.05	2000	5.8682	1.0	1.0
5.0489	12.56	2500	4.4032	0.9990	0.7750
4.6184	15.08	3000	3.8383	0.9983	0.6768
4.365	17.59	3500	3.4633	0.9959	0.6299
4.1026	20.1	4000	3.0732	0.9902	0.5814
3.8655	22.61	4500	2.7638	0.9868	0.5465
3.6991	25.13	5000	2.4759	0.9811	0.5088
3.4894	27.64	5500	2.2937	0.9746	0.4852
3.3983	30.15	6000	2.1684	0.9733	0.4674
3.2736	32.66	6500	2.0372	0.9659	0.4458
3.1884	35.18	7000	1.9267	0.9648	0.4329
3.1248	37.69	7500	1.8408	0.9591	0.4217
3.0381	40.2	8000	1.7531	0.9503	0.4074
2.9515	42.71	8500	1.6880	0.9459	0.3967
2.8704	45.23	9000	1.6264	0.9378	0.3884
2.8128	47.74	9500	1.5621	0.9341	0.3782
2.7386	50.25	10000	1.5011	0.9243	0.3664
2.6646	52.76	10500	1.4608	0.9192	0.3575
2.6072	55.28	11000	1.4251	0.9148	0.3501
2.569	57.79	11500	1.3837	0.9060	0.3462
2.5091	60.3	12000	1.3589	0.9070	0.3392
2.4588	62.81	12500	1.3261	0.8966	0.3284
2.4083	65.33	13000	1.3052	0.8982	0.3265
2.3787	67.84	13500	1.2997	0.8908	0.3243
2.3457	70.35	14000	1.2778	0.8898	0.3187
2.3099	72.86	14500	1.2661	0.8830	0.3172
2.2559	75.38	15000	1.2475	0.8851	0.3143
2.2264	77.89	15500	1.2319	0.8739	0.3085
2.196	80.4	16000	1.2218	0.8722	0.3049
2.1613	82.91	16500	1.2093	0.8719	0.3051
2.1455	85.43	17000	1.2055	0.8624	0.3005
2.1193	87.94	17500	1.1975	0.8600	0.2982
2.0911	90.45	18000	1.1960	0.8648	0.3003
2.0884	92.96	18500	1.1871	0.8638	0.2971
2.0766	95.48	19000	1.1814	0.8617	0.2967
2.0735	97.99	19500	1.1801	0.8621	0.2969

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0