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speecht5_finetuned_commonvoice_ru_translit

Text-to-Speech
Transformers
PyTorch
TensorBoard
Russian
speecht5
text-to-audio
Generated from Trainer
audio
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metadata
language: ru
license: mit
base_model: microsoft/speecht5_tts
task: text-to-speech
tags:
  - generated_from_trainer
  - audio
  - text-to-speech
datasets:
  - mozilla-foundation/common_voice_13_0
model-index:
  - name: SpeechT5 - Russian translit
    results: []

SpeechT5 - Russian translit

This model is a fine-tuned version of microsoft/speecht5_tts on the Common Voice 13 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4853

Model description

Input should be a russian text in transliterated form (use transliterate package). This is just a test for the hands-on excercise of HF Audio Course! Not intended for actual use!

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: 8
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 400
  • training_steps: 2000

Training results

Training Loss Epoch Step Validation Loss
1.0359 0.6 50 0.8176
0.8866 1.19 100 0.6899
0.787 1.79 150 0.6478
0.7477 2.38 200 0.6233
0.6734 2.98 250 0.5630
0.6216 3.58 300 0.5429
0.593 4.17 350 0.5304
0.5817 4.77 400 0.5282
0.5734 5.37 450 0.5167
0.5688 5.96 500 0.5209
0.5662 6.56 550 0.5095
0.5609 7.15 600 0.5127
0.554 7.75 650 0.5041
0.5522 8.35 700 0.5038
0.5372 8.94 750 0.4984
0.5432 9.54 800 0.4995
0.5384 10.13 850 0.4971
0.5345 10.73 900 0.4981
0.5358 11.33 950 0.4942
0.5332 11.92 1000 0.4906
0.5334 12.52 1050 0.4897
0.5301 13.11 1100 0.4914
0.5298 13.71 1150 0.4894
0.524 14.31 1200 0.4871
0.5221 14.9 1250 0.4884
0.525 15.5 1300 0.4883
0.5232 16.1 1350 0.4866
0.5261 16.69 1400 0.4858
0.521 17.29 1450 0.4852
0.5225 17.88 1500 0.4849
0.5219 18.48 1550 0.4860
0.5207 19.08 1600 0.4839
0.5192 19.67 1650 0.4851
0.516 20.27 1700 0.4860
0.5186 20.86 1750 0.4811
0.5233 21.46 1800 0.4841
0.5145 22.06 1850 0.4819
0.5159 22.65 1900 0.4822
0.5146 23.25 1950 0.4831
0.5175 23.85 2000 0.4853

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3