learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 2
seed: 42
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_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.9692	0.1074	100	0.8755
0.7909	0.2148	200	0.7229
0.6814	0.3222	300	0.6576
0.7051	0.4296	400	0.6267
0.6291	0.5371	500	0.6020
0.6234	0.6445	600	0.5874
0.6015	0.7519	700	0.5753
0.5888	0.8593	800	0.5715
0.6018	0.9667	900	0.5699
0.5807	1.0741	1000	0.5620
0.5711	1.1815	1100	0.5572
0.5634	1.2889	1200	0.5525
0.5703	1.3963	1300	0.5487
0.5549	1.5038	1400	0.5457
0.5441	1.6112	1500	0.5422
0.5578	1.7186	1600	0.5420
0.5779	1.8260	1700	0.5395
0.5608	1.9334	1800	0.5370
0.5404	2.0408	1900	0.5352
0.5494	2.1482	2000	0.5357
0.539	2.2556	2100	0.5333
0.5471	2.3631	2200	0.5302
0.537	2.4705	2300	0.5274
0.5429	2.5779	2400	0.5287
0.5348	2.6853	2500	0.5293
0.546	2.7927	2600	0.5291
0.5322	2.9001	2700	0.5231
0.5377	3.0075	2800	0.5232
0.5282	3.1149	2900	0.5259
0.5346	3.2223	3000	0.5204
0.5244	3.3298	3100	0.5179
0.5297	3.4372	3200	0.5228
0.5274	3.5446	3300	0.5161
0.53	3.6520	3400	0.5159
0.5253	3.7594	3500	0.5136
0.5338	3.8668	3600	0.5134
0.5445	3.9742	3700	0.5204
0.5284	4.0816	3800	0.5132
0.5333	4.1890	3900	0.5140
0.5209	4.2965	4000	0.5106
0.5311	4.4039	4100	0.5126
0.5174	4.5113	4200	0.5094
0.5263	4.6187	4300	0.5090
0.5195	4.7261	4400	0.5090
0.5212	4.8335	4500	0.5064
0.5211	4.9409	4600	0.5080
0.5379	5.0483	4700	0.5076
0.5284	5.1557	4800	0.5092
0.5164	5.2632	4900	0.5065
0.5244	5.3706	5000	0.5075
0.5292	5.4780	5100	0.5070
0.5116	5.5854	5200	0.5055
0.5444	5.6928	5300	0.5059
0.5126	5.8002	5400	0.5034
0.5174	5.9076	5500	0.5073
0.5086	6.0150	5600	0.5035
0.5098	6.1224	5700	0.5048
0.5147	6.2299	5800	0.5057
0.5085	6.3373	5900	0.5030
0.5155	6.4447	6000	0.5016
0.5273	6.5521	6100	0.5032
0.5154	6.6595	6200	0.5015
0.5168	6.7669	6300	0.5013
0.5199	6.8743	6400	0.5018
0.5299	6.9817	6500	0.5014
0.5166	7.0892	6600	0.5030
0.5092	7.1966	6700	0.5006
0.5117	7.3040	6800	0.5016
0.5132	7.4114	6900	0.5002
0.5196	7.5188	7000	0.4989
0.5085	7.6262	7100	0.4989
0.5097	7.7336	7200	0.4995
0.51	7.8410	7300	0.4982
0.5095	7.9484	7400	0.4978
0.5172	8.0559	7500	0.5002
0.5151	8.1633	7600	0.4991
0.512	8.2707	7700	0.4997
0.4991	8.3781	7800	0.4989
0.5113	8.4855	7900	0.4980
0.5134	8.5929	8000	0.4975
0.5148	8.7003	8100	0.4969
0.514	8.8077	8200	0.4980
0.5119	8.9151	8300	0.4989
0.5054	9.0226	8400	0.4967
0.5088	9.1300	8500	0.4968
0.5067	9.2374	8600	0.4961
0.5148	9.3448	8700	0.4976
0.5098	9.4522	8800	0.4965
0.5035	9.5596	8900	0.4965
0.5111	9.6670	9000	0.4973
0.507	9.7744	9100	0.4963
0.5048	9.8818	9200	0.4963
0.4992	9.9893	9300	0.4958

Framework versions

Transformers 4.48.2
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

avdeep/tts_3000

tts_3000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

avdeep/
tts_3000