---
license: mit
language:
- ru
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
multilinguality:
- monolingual
pretty_name: Russian Emotional Phonetic Voices
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
tags:
- emotion-recognition
- russian
- speech
---

![Russian Emotional Phonetic Voices](assets/banner.svg)

# Russian Emotional Phonetic Voices

Crowdsourced phonetic voice clips, five emotions.

## How it was collected

REPV was gathered by crowdsourcing rather than in a studio: around **200 different speakers** for the full set and about **50** for REPV-S. Recording conditions therefore vary from contributor to contributor, which makes it harder than RESD and closer to what a microphone in the wild actually receives.

## Splits

| Split | Rows | Hours | Mean clip |
|---|---:|---:|---:|
| `train` | 1628 | 1.65 | 3.6 s |
| `test` | 407 | 0.40 | 3.6 s |

<img src="assets/classes.svg" alt="Class distribution" width="760">

## Fields

| Column | Meaning |
|---|---|
| `path` | Original file path |
| `file` | Source file name |
| `gender` | Speaker gender as reported by the contributor |
| `emotion` | Emotion label of the recording |
| `speech` | Audio |

Gender is close to even in `train`: 883 f, 745 m.

> [!NOTE]
> The label set is **not** the seven-class one used by RESD and the Aniemore models. REPV has five: `anger`, `enthusiasm`, `happiness`, `sadness` and `tiredness`. `tiredness` appears nowhere else in the library, and `neutral`, `fear` and `disgust` are absent here.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Aniemore/REPV")
print(ds["train"][0]["emotion"])
```

## Limitations

Crowdsourced audio varies in microphone, room and level, and the whole set is 2.1 hours — small enough that a single split can move a score by several points. `REPV-S` in particular holds 2035 clips in total and is meant for smoke tests rather than for measuring anything.

## Citation

```bibtex
@misc{Aniemore,
  author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
  title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
  year = {2022},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
  email = {hello@socialcode.ru}
}
```

## License

MIT.