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

sourceHugging Facemitupdated 3mo agoView on Hugging Face
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Model Card

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senga-LUK-aligned-stoploss-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: 0.1300

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
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 3407
  • —gradientaccumulationsteps: 4
  • —totaltrainbatch_size: 32
  • —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 4000
  • —training_steps: 40000
  • —mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation Loss
0.104990.909110000.0889
0.0821181.818220000.0786
0.071272.727330000.0832
0.0661363.636440000.0827
0.0585454.545550000.0884
0.0573545.454560000.0928
0.0527636.363670000.0945
0.0463727.272780000.0995
0.0472818.181890000.1012
0.0434909.0909100000.1027
0.0411000.0110000.1036
0.04241090.9091120000.1060
0.03891181.8182130000.1095
0.03891272.7273140000.1109
0.03661363.6364150000.1121
0.0371454.5455160000.1157
0.03571545.4545170000.1140
0.04671636.3636180000.1234
0.03431727.2727190000.1210
0.03581818.1818200000.1192
0.03131909.0909210000.1212
0.03282000.0220000.1213
0.03172090.9091230000.1212
0.03192181.8182240000.1236
0.03112272.7273250000.1252
0.03062363.6364260000.1237
0.02892454.5455270000.1270
0.0282545.4545280000.1270
0.02952636.3636290000.1262
0.03022727.2727300000.1272
0.03752818.1818310000.1275
0.0282909.0909320000.1289
0.02723000.0330000.1265
0.02923090.9091340000.1280
0.02693181.8182350000.1278
0.02643272.7273360000.1289
0.02853363.6364370000.1300
0.02753454.5455380000.1287
0.02853545.4545390000.1293
0.02823636.3636400000.1300

Framework versions

  • —Transformers 4.57.1
  • —Pytorch 2.8.0+cu128
  • —Datasets 4.2.0
  • —Tokenizers 0.22.2