---
dataset_info:
- config_name: audiobooks
  features:
  - name: audio
    dtype: audio
  - name: text
    dtype: string
  - name: duration
    dtype: float64
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: lang
    dtype: string
  splits:
  - name: train
- config_name: audiobooks_rus_translations
  features:
  - name: audio
    dtype: audio
  - name: text
    dtype: string
  - name: duration
    dtype: float64
  - name: id
    dtype: string
  - name: rus_title
    dtype: string
  - name: title
    dtype: string
  - name: lang
    dtype: string
  splits:
  - name: train
configs:
- config_name: audiobooks
  data_files:
  - split: train
    path: train*.parquet
- config_name: audiobooks_rus_translations
  data_files:
  - split: train
    path: rus_train*.parquet
language:
- tt
- cv
- udm
- mdf
- myv
- mhr
- ru
pretty_name: 'Volga Fairytales TTS '
task_categories:
- text-to-speech
- automatic-speech-recognition
- audio-to-audio
tags:
- audio
- tts
- speech
- speech-to-speech
- translation
size_categories:
- 1K<n<10K
---

# Volga Fairytales TTS Dataset

## Dataset Summary

Volga Fairytales TTS is a speech dataset containing audio recordings of traditional fairytales in several languages of the Volga region. 

The original recordings were sourced from [this site](https://xn--80aemcfjckzbis8msb.xn--p1ai/library).

The dataset comprises recordings of fairytale texts read by professional actors. The dataset is intended for text-to-speech (TTS) research and development in Turkic and other regional languages including Tatar, Chuvash, Mari, Udmurt, Erzya and Moksha. The dataset also includes Russian translations/recordings, but they are not aligned on a sentence level.

## Dataset Structure

**Parts:**  
The dataset contains two main configurations:
- `audiobooks`: Contains the original fairytale recordings in their native languages.
- `audiobooks_rus_translations`: Contains the recordings of Russian translations for original fairytales.

**Data Fields:**
- `audio`: The audio file containing the spoken fairytale segment.
- `text`: The corresponding transcription.
- `duration`: The duration of the audio segment in seconds.
- `id`: A unique identifier for each segment.
- `title`: The title of the fairytale.
- `lang`: The language code of the recording (e.g., "tat" for Tatar).
- `rus_title`: (Only in audiobooks_rus_translations) The Russian title of the fairytale.


## Data Processing

**Text Processing:**  
- All the text was splited, using razdel library.

**Audio Processing:**  
- All audio was aligned to the text as each word had timestamps.

**Technical Details:**
|                       |                                    |
|-----------------------|------------------------------------|
| Dataset Type          | speech corpus for TTS              |
| Language              | Tatar, Chuvash, Meadow–Eastern Mari, Udmurt, Erzya, Moksha, Russian    |
| Speech Style          | scripted monologue                 |
| Content               | fairytales     |
| Audio Parameters      | 22.05 kHz, 32 bits, mono              |
| File Format           | WAV (PCM)  TXT (UTF-8)             |
| Recording Environment | quiet indoor environment + background music                |



**Durations:**  
|    Language           |                 Duration           |   Utterances                       |
|-----------------------|------------------------------------|------------------------------------|
| Tatar          |0.74 hours             | 355             |        
| Chuvash         | 0.82 hours             |425            |
| Meadow–Eastern Mari          | 0.57 hours             |384             |
| Udmurt          | 0.23 hours        |147             |
| Erzya          | 0.20  hours         |146            |
| Moksha          | 0.36 hours           |164             |
| Russian          | 2.46 hours           |1464            |


## Usage Considerations

- The dataset includes a variety of fairytales that showcase cultural and linguistic aspects of the Volga region.
- The dataset is suitable for training text-to-speech systems, automatic speech recognition, and speech-to-speech translation systems.
- Researchers should be aware of potential variations in speaking styles and background audio conditions.