---
license: mit
task_categories:
- text-to-speech
language:
- ru
---
# Russian TTS MOS Evaluation Dataset

A curated Russian synthesized speech dataset with crowdsourced quality ratings.

## Overview

This dataset contains Russian synthesized speech samples from multiple TTS systems and audio codec models, evaluated by human annotators via crowdsourcing. Quality ratings are provided in two dimensions: perceptual quality (MOS) and intelligibility (Int-MOS).

- **Language:** Russian only
- **Sources:** F5, FishSpeech, GPT-So-VITS, Tortoise, XTTS, EnCodec, WavTok, and internal encoder/decoder configurations
- **Format:** Single Parquet file with embedded audio bytes
- **Access:** Public

## Usage

### Load the dataset

```python
from datasets import load_dataset

ds = load_dataset("YOUR_ORG/YOUR_DATASET_NAME")
```
Or just use script load_data_tts.py

### Read audio from bytes

```python
import io
import soundfile as sf

sample = ds["train"][0]

audio_array, sr = sf.read(io.BytesIO(sample["audio"]))

print(f"File:     {sample['filename']}")
print(f"MOS:      {sample['mos_mean']}")
print(f"Int-MOS:  {sample['int_mos_mean']}")
```

## Data Structure

Each row corresponds to one audio file with aggregated scores from all annotators.

| Column | Type | Description |
|---|---|---|
| `inner_audio_id` | int | Internal audio identifier from the labeling system |
| `filename` | string | Relative path to the audio file (e.g. `F5/001234_RUSLAN.wav`) |
| `audio` | binary | Raw audio bytes (wav) |
| `mos_mean` | float or null | Mean perceptual quality score (1–5); null if not rated |
| `int_mos_mean` | float or null | Mean intelligibility score (1–5); null if not rated |
| `project` | string | Source labeling project name |.
## Evaluation Methodology

Ratings were collected via crowdsourcing across two independent evaluation tracks:
- **MOS** — perceptual quality assessment following ITU-T P.800 (1 = bad, 5 = excellent)
- **Int-MOS** — intelligibility assessment on the same 1–5 scale

Each sample may have one or both scores depending on which evaluation projects it was included in. A sample appearing in both tracks may carry two different `inner_audio_id` values — this is expected behavior from the labeling pipeline.

Model quality on unlabeled data was additionally estimated using [Proxymos](https://huggingface.co/lab260/ProxyMos) as a reference-free MOS predictor.

## Contact

- Email: kborodin.research@gmail.com
- Telegram: [@korallll_ai](https://t.me/korallll_ai)