ptah23's picture
update model card README.md
82812ca
metadata
license: mit
base_model: microsoft/speecht5_tts
tags:
  - generated_from_trainer
datasets:
  - voxpopuli
model-index:
  - name: speecht5_finetuned_voxpopuli_nl
    results: []

speecht5_finetuned_voxpopuli_nl

This model is a fine-tuned version of microsoft/speecht5_tts on the voxpopuli dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4591

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000

Training results

Training Loss Epoch Step Validation Loss
0.7034 0.43 100 0.6427
0.6812 0.86 200 0.5998
0.5928 1.29 300 0.5292
0.5662 1.72 400 0.5095
0.5547 2.15 500 0.5024
0.5356 2.58 600 0.4929
0.532 3.01 700 0.4902
0.5312 3.44 800 0.4835
0.5171 3.87 900 0.4800
0.5178 4.3 1000 0.4780
0.5152 4.73 1100 0.4777
0.5064 5.16 1200 0.4737
0.5122 5.59 1300 0.4726
0.5042 6.02 1400 0.4700
0.5075 6.45 1500 0.4712
0.5057 6.88 1600 0.4688
0.5037 7.31 1700 0.4674
0.504 7.74 1800 0.4665
0.4977 8.17 1900 0.4652
0.4976 8.6 2000 0.4653
0.5014 9.03 2100 0.4650
0.4951 9.46 2200 0.4632
0.493 9.89 2300 0.4626
0.4983 10.32 2400 0.4628
0.4952 10.75 2500 0.4627
0.4961 11.18 2600 0.4616
0.4965 11.61 2700 0.4618
0.4895 12.04 2800 0.4615
0.4898 12.47 2900 0.4600
0.5008 12.9 3000 0.4614
0.4896 13.33 3100 0.4603
0.4957 13.76 3200 0.4612
0.4878 14.19 3300 0.4598
0.4923 14.62 3400 0.4594
0.4939 15.05 3500 0.4596
0.4828 15.48 3600 0.4591
0.4865 15.91 3700 0.4588
0.4999 16.34 3800 0.4600
0.4895 16.77 3900 0.4587
0.4929 17.2 4000 0.4591

Framework versions

  • Transformers 4.31.0.dev0
  • Pytorch 1.12.1+cu116
  • Datasets 2.4.0
  • Tokenizers 0.12.1