Datasets:

lab260
/

deepspeech_balalaika

---
license: cc-by-nc-4.0
task_categories:
- text-to-speech
- automatic-speech-recognition
language:
- ru
pretty_name: Deepspeech annotate by Balalaika
---

# Deepspeech Annotated by Balalaika

**A curated Russian speech dataset for advanced speech generative tasks.**

## Overview

**Deepspeech Annotated by Balalaika** is a high-quality Russian speech corpus, meticulously filtered and annotated by the **lab260 team at MTUCI** with the latest version of our pipeline, **BALALAIKA**.

- **Language:** Russian only
- **Genres:** Podcasts, public speech, YouTube, audiobooks, phone calls, TTS, and more
- **Source:** Deepspeech ([GitHub link](https://github.com/GeorgeFedoseev/DeepSpeech))
- **License:** CC BY-NC 4.0 (same as original OpenSTT)
- **Total Duration After Filtering:** 278.6 hours (from over 6000 hours raw)
- **Format:** Parquet files with split-wise annotation

***

## Usage

**Primary Use Cases:**
- Text-to-Speech (TTS) generation
- Automatic Speech Recognition (ASR)
- Analysis of accent, stress, and prosody
- Russian speech technology research

### 1. Download the dataset

### 2. Extract the files

```basg
for archive in *.tar.gz; do
    dir="${archive%.tar.gz}"
    mkdir -p "$dir"
    tar -xzvf "$archive" -C "$dir"
    rm "$archive"
done
```

### 3. Load data in PyTorch

```python
from pathlib import Path
import pandas as pd
from torch.utils.data import Dataset
import torchaudio

class ParquetConcatDataset(Dataset):
    def __init__(self, parquet_dir, audio_root, parse_fn=None):
        self.parquet_dir = Path(parquet_dir)
        self.audio_root = Path(audio_root)

        parquet_files = list(self.parquet_dir.glob("*.parquet"))
        dfs = [pd.read_parquet(f) for f in parquet_files]
        self.df = pd.concat(dfs, ignore_index=True)

    def __len__(self):
        return len(self.df)

    def __getitem__(self, idx):
        row = self.df.iloc[idx]
        audio_path = self.audio_root / row["filepath"]
        waveform, sample_rate = torchaudio.load(audio_path)
        return {
            "audio_path": str(audio_path),
            "waveform": waveform,
            "sample_rate": sample_rate,
            "nisqa_mos": row["mos_pred"],
            "nisqa_noi": row["noi_pred"],
            "nisqa_dis": row["dis_pred"],
            "nisqa_col": row["col_pred"],
            "nisqa_loud": row["loud_pred"],
            "nisqa_model": row["model"],
            "is_single_speaker": bool(row["is_single_speaker"]),
            "accented_text": row["accent"],
            "asr_text": row["rover"],
            "punctuated_text": row["punct"],
            "silence_percent": row["silence_percent"],
            "total_duration": row["total_duration"],
            "max_silence_duration": row["max_silence_duration"]
        }

# Example usage
ds = ParquetConcatDataset(
    PATH_TO_PARQUETS_DIR,
    PATH_TO_AUDIO_ROOT
)
```

`PATH_TO_PARQUETS_DIR`: Path to the folder containing all .parquet files with metadata and annotations for the dataset.

`PATH_TO_AUDIO_ROOT`: Path to the root directory containing all audio subfolders and files referenced by filepath columns in the metadata.
***

## Data Processing & Annotation

Our pipeline applies **rigorous filtering and enrichment** steps:

1. **Removed speech segments** shorter than 3 seconds
2. **Filtered segments** with [NISQA](https://github.com/gabrielmittag/NISQA/tree/master/nisqa) MOS < 4.0 for quality assurance
3. **Excluded segments with multiple speakers** (via [pyannotate diarization](https://huggingface.co/pyannote/speaker-diarization-community-1))
4. **Filtered segments** with [VAD](https://github.com/snakers4/silero-vad) silence_percent > 30.0 % and max_silence_duration > 1,2 for quality assurance
5. **Filtered out speech with music background** (custom music detector)
6. **Revised transcriptions:** Crowd-sourced with multiple ASRs, fused via ROVER ([T-one](https://github.com/voicekit-team/T-one/tree/main), [GigaAMv3-rnnt, GigaAMv3-ctc, GigaAMv3-ctc-lm](https://github.com/salute-developers/GigaAM), [vosk](https://huggingface.co/alphacep/vosk-model-ru))
7. **Punctuation added** using [RuPunct](https://huggingface.co/RUPunct/RUPunct_big)
8. **Stress marks added** via [RuAccent](https://github.com/Den4ikAI/ruaccent)
9. **IPA phonemization** performed with our own neural model

All **annotation fields** are handled and provided separately for transparency and flexibility.

***

## Data Structure

- **Annotation storage:** Parquet files
- **Speech storage:** .tar.gz files with speech segments in .opus
- **Splitting:** Follows OpenSTT splits
- **Annotations:** Each sample includes separate fields for:
  - **Filepath**
  - **Quality metrics: MOS, NOI, DIS, COL, LOUD**
  - **Model for quality assesment**
  - **Transcript with stresses and pucntuation**
  - **Transcript after ROVER**
  - **Transcript with punctuation**
  - **IPA transcription**
  - **Speaker diarization flag**
  - **Information about silence**


***

## How to Cite

Please cite the following paper if you use this dataset in research:
```
@misc{borodin2025datacentricframeworkaddressingphonetic,
      title={A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models}, 
      author={Kirill Borodin and Nikita Vasiliev and Vasiliy Kudryavtsev and Maxim Maslov and Mikhail Gorodnichev and Oleg Rogov and Grach Mkrtchian},
      year={2025},
      eprint={2507.13563},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.13563}, 
}
```

***

## Contact

- Telegram: [@korallll_ai](https://t.me/korallll_ai)
- Email: [k.n.borodin@mtuci.ru](mailto:k.n.borodin@mtuci.ru)

***

## Links

- [Balalaka annotation pipeline](https://github.com/mtuciru/balalaika/tree/main/src)
- [Other datasets annotated by BALALAIKA](https://huggingface.co/collections/MTUCI/balalaika-dataset)
- [Custom models' inference implementaton](https://huggingface.co/collections/MTUCI/balalaika-models)
- [Paper (arXiv)](https://arxiv.org/pdf/2507.13563)
- [OpenSTT repository](https://github.com/snakers4/open_stt?tab=readme-ov-file)
- [NISQA](https://github.com/gabrielmittag/NISQA/tree/master/nisqa)
- [pyannotate diarization](https://huggingface.co/pyannote/speaker-diarization-community-1)
- [T-one](https://github.com/voicekit-team/T-one/tree/main)
- [GigaAM   v2-rnnt, GigaAMv2-ctc, GigaAMv2-ctc-lm](https://github.com/salute-developers/GigaAM)
- [vosk](https://huggingface.co/alphacep/vosk-model-ru)
- [RuPunct](https://huggingface.co/RUPunct/RUPunct_big)
- [RuAccent](https://github.com/Den4ikAI/ruaccent)

***

## License

Distributed under **CC BY-NC 4.0**, matching original OpenSTT terms.

***