sil-ai / senga-LUK-ACT-aligned-speecht5

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Total runs: 159
24-hour runs: 0
7-day runs: 1
30-day runs: 8
Model's Last Updated: April 25 2026
text-to-audio

Introduction of senga-LUK-ACT-aligned-speecht5

Model Details of senga-LUK-ACT-aligned-speecht5

senga-LUK-ACT-aligned-speecht5

This model is a fine-tuned version of microsoft/speecht5_tts on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1216
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: 8
  • eval_batch_size: 8
  • seed: 3407
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 4000
  • training_steps: 40000
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss
0.1221 47.6190 1000 0.0995
0.1008 95.2381 2000 0.0894
0.0865 142.8571 3000 0.0885
0.0824 190.4762 4000 0.0897
0.0759 238.0952 5000 0.0912
0.0702 285.7143 6000 0.0907
0.065 333.3333 7000 0.0926
0.0612 380.9524 8000 0.0967
0.0638 428.5714 9000 0.1048
0.0579 476.1905 10000 0.0991
0.0549 523.8095 11000 0.1037
0.0552 571.4286 12000 0.1039
0.0508 619.0476 13000 0.1047
0.052 666.6667 14000 0.1055
0.0486 714.2857 15000 0.1090
0.0494 761.9048 16000 0.1088
0.0484 809.5238 17000 0.1104
0.0557 857.1429 18000 0.1107
0.0465 904.7619 19000 0.1122
0.0478 952.3810 20000 0.1116
0.043 1000.0 21000 0.1142
0.0447 1047.6190 22000 0.1153
0.0433 1095.2381 23000 0.1145
0.0434 1142.8571 24000 0.1171
0.0423 1190.4762 25000 0.1179
0.0418 1238.0952 26000 0.1162
0.0391 1285.7143 27000 0.1189
0.0385 1333.3333 28000 0.1198
0.0406 1380.9524 29000 0.1203
0.0411 1428.5714 30000 0.1207
0.0492 1476.1905 31000 0.1206
0.0384 1523.8095 32000 0.1212
0.0374 1571.4286 33000 0.1212
0.0397 1619.0476 34000 0.1218
0.0373 1666.6667 35000 0.1221
0.0369 1714.2857 36000 0.1227
0.0388 1761.9048 37000 0.1213
0.0379 1809.5238 38000 0.1218
0.0388 1857.1429 39000 0.1226
0.0387 1904.7619 40000 0.1216
Framework versions
  • Transformers 4.57.1
  • Pytorch 2.8.0+cu128
  • Datasets 4.2.0
  • Tokenizers 0.22.2

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