sil-ai / dgo-tts-training-data-a-speecht5

huggingface.co
Total runs: 3
24-hour runs: 0
7-day runs: 0
30-day runs: 1
Model's Last Updated: October 08 2025
text-to-audio

Introduction of dgo-tts-training-data-a-speecht5

Model Details of dgo-tts-training-data-a-speecht5

dgo-tts-training-data-a-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: 0.5171
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: 42
  • 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: linear
  • lr_scheduler_warmup_steps: 4000
  • training_steps: 40000
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss
0.6529 5.3763 1000 0.6011
0.5724 10.7527 2000 0.5522
0.5504 16.1290 3000 0.5309
0.523 21.5054 4000 0.5219
0.517 26.8817 5000 0.5117
0.5031 32.2581 6000 0.5048
0.4871 37.6344 7000 0.5053
0.4832 43.0108 8000 0.5026
0.4765 48.3871 9000 0.5007
0.4623 53.7634 10000 0.5046
0.451 59.1398 11000 0.5005
0.4437 64.5161 12000 0.5025
0.4397 69.8925 13000 0.5031
0.4266 75.2688 14000 0.5074
0.4269 80.6452 15000 0.5064
0.4197 86.0215 16000 0.5064
0.4225 91.3978 17000 0.5100
0.411 96.7742 18000 0.5100
0.41 102.1505 19000 0.5105
0.4159 107.5269 20000 0.5122
0.4151 112.9032 21000 0.5091
0.4137 118.2796 22000 0.5141
0.4106 123.6559 23000 0.5118
0.4096 129.0323 24000 0.5141
0.4183 134.4086 25000 0.5154
0.407 139.7849 26000 0.5154
0.4077 145.1613 27000 0.5124
0.4107 150.5376 28000 0.5113
0.4079 155.9140 29000 0.5109
0.412 161.2903 30000 0.5161
0.4107 166.6667 31000 0.5154
0.4015 172.0430 32000 0.5153
0.4038 177.4194 33000 0.5137
0.4053 182.7957 34000 0.5161
0.403 188.1720 35000 0.5156
0.4052 193.5484 36000 0.5159
0.402 198.9247 37000 0.5178
0.4048 204.3011 38000 0.5148
0.4043 209.6774 39000 0.5163
0.4004 215.0538 40000 0.5171
Framework versions
  • Transformers 4.57.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.1.1
  • Tokenizers 0.22.1

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