sil-ai / bez-chapter-audio-dataset-force-aligned-speecht5

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7-day runs: 176
30-day runs: 176
Model's Last Updated: October 04 2025
text-to-audio

Introduction of bez-chapter-audio-dataset-force-aligned-speecht5

Model Details of bez-chapter-audio-dataset-force-aligned-speecht5

bez-chapter-audio-dataset-force-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: 0.2450
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: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss
0.3382 11.1114 1000 0.3121
0.3168 22.2228 2000 0.2871
0.3047 33.3343 3000 0.2826
0.2891 44.4457 4000 0.2639
0.2875 55.5571 5000 0.2615
0.2824 66.6685 6000 0.2610
0.2749 77.7799 7000 0.2534
0.2767 88.8914 8000 0.2553
0.271 100.0 9000 0.2529
0.273 111.1114 10000 0.2489
0.266 122.2228 11000 0.2500
0.2674 133.3343 12000 0.2478
0.2642 144.4457 13000 0.2485
0.2625 155.5571 14000 0.2485
0.2668 166.6685 15000 0.2447
0.2611 177.7799 16000 0.2451
0.2601 188.8914 17000 0.2475
0.2574 200.0 18000 0.2456
0.26 211.1114 19000 0.2462
0.2572 222.2228 20000 0.2450
Framework versions
  • Transformers 4.56.2
  • Pytorch 2.8.0+cu128
  • Datasets 4.1.1
  • Tokenizers 0.22.1

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