yesj1234/xlsr_mid1_ja-ko

ja-xlsr

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the ./SAMPLE_SPEECH.PY - NA dataset. It achieves the following results on the evaluation set:

• Loss: 2.5952
• Cer: 0.3240

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: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 16
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
4.9138	6.52	150	4.7965	1.0
4.7484	13.04	300	4.6081	1.0
4.5894	19.57	450	4.4697	0.9851
4.2024	26.09	600	4.0373	0.9077
2.7314	32.61	750	2.5507	0.5341
1.2293	39.13	900	2.0146	0.4139
0.5544	45.65	1050	1.9821	0.3556
0.3224	52.17	1200	2.0190	0.3587
0.1951	58.7	1350	2.1229	0.3612
0.1539	65.22	1500	2.1114	0.3470
0.1165	71.74	1650	2.2748	0.3315
0.1119	78.26	1800	2.2391	0.3488
0.0989	84.78	1950	2.3438	0.3383
0.0915	91.3	2100	2.1218	0.3587
0.0721	97.83	2250	2.2428	0.3519
0.0742	104.35	2400	2.2293	0.3364
0.0629	110.87	2550	2.2878	0.3371
0.0495	117.39	2700	2.2672	0.3408
0.0466	123.91	2850	2.2532	0.3525
0.0424	130.43	3000	2.2844	0.3259
0.0446	136.96	3150	2.2763	0.3253
0.0411	143.48	3300	2.3011	0.3302
0.0419	150.0	3450	2.3201	0.3420
0.0333	156.52	3600	2.3644	0.3439
0.0384	163.04	3750	2.3685	0.3532
0.0367	169.57	3900	2.3970	0.3470
0.0307	176.09	4050	2.3530	0.3309
0.0328	182.61	4200	2.3415	0.3315
0.0271	189.13	4350	2.4165	0.3309
0.0213	195.65	4500	2.4478	0.3451
0.0193	202.17	4650	2.5241	0.3556
0.0204	208.7	4800	2.5700	0.3463
0.0185	215.22	4950	2.5837	0.3178
0.0161	221.74	5100	2.5139	0.3377
0.0167	228.26	5250	2.5288	0.3352
0.0148	234.78	5400	2.5741	0.3389
0.0141	241.3	5550	2.5174	0.3389
0.0122	247.83	5700	2.5573	0.3352
0.0115	254.35	5850	2.5790	0.3296
0.0141	260.87	6000	2.5774	0.3203
0.0123	267.39	6150	2.6147	0.3309
0.0214	273.91	6300	2.6202	0.3302
0.0107	280.43	6450	2.6264	0.3234
0.0086	286.96	6600	2.6075	0.3216
0.0106	293.48	6750	2.5960	0.3247
0.0085	300.0	6900	2.5952	0.3240

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1