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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 - Russian translit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/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 |