---
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