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--- |
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language: ru |
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license: mit |
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base_model: microsoft/speecht5_tts |
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task: text-to-speech |
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tags: |
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- generated_from_trainer |
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- audio |
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- text-to-speech |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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model-index: |
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- name: SpeechT5 - Russian translit |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# SpeechT5 - Russian translit |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice 13 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4853 |
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## Model description |
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Input should be a russian text in transliterated form (use transliterate package). |
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This is just a test for the hands-on excercise of HF Audio Course! Not intended for actual use! |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 400 |
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- training_steps: 2000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.0359 | 0.6 | 50 | 0.8176 | |
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| 0.8866 | 1.19 | 100 | 0.6899 | |
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| 0.787 | 1.79 | 150 | 0.6478 | |
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| 0.7477 | 2.38 | 200 | 0.6233 | |
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| 0.6734 | 2.98 | 250 | 0.5630 | |
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| 0.6216 | 3.58 | 300 | 0.5429 | |
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| 0.593 | 4.17 | 350 | 0.5304 | |
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| 0.5817 | 4.77 | 400 | 0.5282 | |
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| 0.5734 | 5.37 | 450 | 0.5167 | |
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| 0.5688 | 5.96 | 500 | 0.5209 | |
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| 0.5662 | 6.56 | 550 | 0.5095 | |
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| 0.5609 | 7.15 | 600 | 0.5127 | |
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| 0.554 | 7.75 | 650 | 0.5041 | |
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| 0.5522 | 8.35 | 700 | 0.5038 | |
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| 0.5372 | 8.94 | 750 | 0.4984 | |
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| 0.5432 | 9.54 | 800 | 0.4995 | |
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| 0.5384 | 10.13 | 850 | 0.4971 | |
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| 0.5345 | 10.73 | 900 | 0.4981 | |
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| 0.5358 | 11.33 | 950 | 0.4942 | |
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| 0.5332 | 11.92 | 1000 | 0.4906 | |
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| 0.5334 | 12.52 | 1050 | 0.4897 | |
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| 0.5301 | 13.11 | 1100 | 0.4914 | |
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| 0.5298 | 13.71 | 1150 | 0.4894 | |
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| 0.524 | 14.31 | 1200 | 0.4871 | |
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| 0.5221 | 14.9 | 1250 | 0.4884 | |
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| 0.525 | 15.5 | 1300 | 0.4883 | |
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| 0.5232 | 16.1 | 1350 | 0.4866 | |
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| 0.5261 | 16.69 | 1400 | 0.4858 | |
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| 0.521 | 17.29 | 1450 | 0.4852 | |
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| 0.5225 | 17.88 | 1500 | 0.4849 | |
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| 0.5219 | 18.48 | 1550 | 0.4860 | |
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| 0.5207 | 19.08 | 1600 | 0.4839 | |
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| 0.5192 | 19.67 | 1650 | 0.4851 | |
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| 0.516 | 20.27 | 1700 | 0.4860 | |
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| 0.5186 | 20.86 | 1750 | 0.4811 | |
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| 0.5233 | 21.46 | 1800 | 0.4841 | |
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| 0.5145 | 22.06 | 1850 | 0.4819 | |
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| 0.5159 | 22.65 | 1900 | 0.4822 | |
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| 0.5146 | 23.25 | 1950 | 0.4831 | |
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| 0.5175 | 23.85 | 2000 | 0.4853 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
