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

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Total runs: 76
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7-day runs: 76
30-day runs: 76
Model's Last Updated: October 04 2025
text-to-audio

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

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

acf-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: 0.3971
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
0.4309 7.6963 1000 0.4144
0.4094 15.3868 2000 0.4037
0.3985 23.0774 3000 0.4015
0.3929 30.7737 4000 0.3987
0.386 38.4642 5000 0.3962
0.3838 46.1547 6000 0.3970
0.3771 53.8511 7000 0.3955
0.3739 61.5416 8000 0.3963
0.3698 69.2321 9000 0.3952
0.3738 76.9284 10000 0.3962
0.3638 84.6190 11000 0.3952
0.3634 92.3095 12000 0.3956
0.3619 100.0 13000 0.3949
0.3583 107.6963 14000 0.3966
0.3597 115.3868 15000 0.3963
0.358 123.0774 16000 0.3975
0.3541 130.7737 17000 0.3964
0.3543 138.4642 18000 0.3967
0.3528 146.1547 19000 0.3971
0.3536 153.8511 20000 0.3971
Framework versions
  • Transformers 4.57.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.1.1
  • Tokenizers 0.22.1

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