MEHDI_Assamese_TTS

This model is a fine-tuned version of microsoft/speecht5_tts on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5555

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: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use 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: 200
• num_epochs: 155
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.2762	3.4561	100	0.5795
1.1771	6.9123	200	0.5489
1.1979	10.3509	300	0.5573
1.1083	13.8070	400	0.5319
1.0821	17.2456	500	0.5273
1.0297	20.7018	600	0.5075
1.0778	24.1404	700	0.5064
0.9818	27.5965	800	0.5098
0.9574	31.0351	900	0.4986
0.9571	34.4912	1000	0.5123
0.9955	37.9474	1100	0.5217
0.9455	41.3860	1200	0.5223
0.9084	44.8421	1300	0.5150
0.9057	48.2807	1400	0.5073
0.9038	51.7368	1500	0.5103
0.8802	55.1754	1600	0.5163
0.9033	58.6316	1700	0.5058
0.8507	62.0702	1800	0.5098
0.8549	65.5263	1900	0.5170
0.8795	68.9825	2000	0.5167
0.8629	72.4211	2100	0.5306
0.8577	75.8772	2200	0.5180
0.8197	79.3158	2300	0.5187
0.8278	82.7719	2400	0.5213
0.8016	86.2105	2500	0.5169
0.7927	89.6667	2600	0.5281
0.7815	93.1053	2700	0.5287
0.778	96.5614	2800	0.5288
0.7894	100.0	2900	0.5326
0.7521	103.4561	3000	0.5343
0.7705	106.9123	3100	0.5371
0.7652	110.3509	3200	0.5344
0.7458	113.8070	3300	0.5359
0.7583	117.2456	3400	0.5407
0.7479	120.7018	3500	0.5369
0.7338	124.1404	3600	0.5512
0.7662	127.5965	3700	0.5460
0.7212	131.0351	3800	0.5484
0.736	134.4912	3900	0.5447
0.7367	137.9474	4000	0.5536
0.7065	141.3860	4100	0.5566
0.711	144.8421	4200	0.5550
0.718	148.2807	4300	0.5555

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0