sil-ai / senga-LUK-aligned-speecht5

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Total runs: 141
24-hour runs: 0
7-day runs: 1
30-day runs: 3
Model's Last Updated: April 25 2026
text-to-audio

Introduction of senga-LUK-aligned-speecht5

Model Details of senga-LUK-aligned-speecht5

senga-LUK-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: 0.1379
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
0.112 90.9091 1000 0.0942
0.0891 181.8182 2000 0.0886
0.077 272.7273 3000 0.0871
0.071 363.6364 4000 0.0907
0.0644 454.5455 5000 0.0919
0.0623 545.4545 6000 0.1022
0.0556 636.3636 7000 0.1009
0.0516 727.2727 8000 0.1042
0.0519 818.1818 9000 0.1083
0.0478 909.0909 10000 0.1100
0.0448 1000.0 11000 0.1130
0.0453 1090.9091 12000 0.1121
0.0416 1181.8182 13000 0.1163
0.043 1272.7273 14000 0.1197
0.0391 1363.6364 15000 0.1215
0.0397 1454.5455 16000 0.1206
0.0387 1545.4545 17000 0.1236
0.0488 1636.3636 18000 0.1256
0.0373 1727.2727 19000 0.1260
0.0387 1818.1818 20000 0.1279
0.0336 1909.0909 21000 0.1279
0.0349 2000.0 22000 0.1297
0.0343 2090.9091 23000 0.1296
0.0342 2181.8182 24000 0.1328
0.0335 2272.7273 25000 0.1343
0.0329 2363.6364 26000 0.1328
0.0307 2454.5455 27000 0.1357
0.03 2545.4545 28000 0.1346
0.0318 2636.3636 29000 0.1357
0.0325 2727.2727 30000 0.1368
0.0405 2818.1818 31000 0.1364
0.0299 2909.0909 32000 0.1370
0.029 3000.0 33000 0.1357
0.0312 3090.9091 34000 0.1369
0.0286 3181.8182 35000 0.1372
0.0281 3272.7273 36000 0.1377
0.0306 3363.6364 37000 0.1382
0.0295 3454.5455 38000 0.1371
0.0306 3545.4545 39000 0.1379
0.0302 3636.3636 40000 0.1379
Framework versions
  • Transformers 4.57.1
  • Pytorch 2.8.0+cu128
  • Datasets 4.2.0
  • Tokenizers 0.22.2

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