README.md

---
language:
- ru
license: cc-by-nc-nd-4.0
task_categories:
- text-to-speech
pretty_name: Balalaika
tags:
- russian
---

# A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models

[Paper](https://huggingface.co/papers/2507.13563) | [Code](https://github.com/mtuciru/balalaika)

Russian speech synthesis presents distinctive challenges, including vowel reduction, consonant devoicing, variable stress patterns, homograph ambiguity, and unnatural intonation. This paper introduces Balalaika, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, including punctuation and stress markings. Experimental results show that models trained on Balalaika significantly outperform those trained on existing datasets in both speech synthesis and enhancement tasks.

---

## Quick Start 👟
```bash
git clone https://github.com/mtuciru/balalaika && cd balalaika
bash create_user_env.sh        # sets up venv + pip deps
bash use_meta_500h.sh          # pick 100h / 500h / 1000h / 2000h as needed

```

## Table of Contents

1. [Prerequisites](#prerequisites)
2. [Installation](#installation)
3. [Data Preparation](#data-preparation)
   - [Quick Setup (Default Parameters)](#quick-setup)
   - [Custom Metadata Download](#custom-metadata-download)
4. [Running the Pipeline](#running-the-pipeline)
   - [Basic Scenario (Local Processing)](#basic-scenario-local-processing)
5. [Configuration](#configuration)
6. [Environment Variables](#environment-variables)
7. [Models](#models)
8. [Citation](#citation)
<!-- 9. [Acknowledgments](#acknowledgments) -->
9. [License](#license)

---

## Prerequisites

Ensure you have the following tools installed on your system:

```bash
sudo apt update && sudo apt install -y \
  ffmpeg \                 # video/audio toolkit
  python3 \                # Python
  python3-pip \            # Pip package manager
  python3-venv \           # std-lib virtual-env support
  python3-dev \            # headers for compiling native wheels
  python-is-python3
wget -qO- https://astral.sh/uv/install.sh | sh

```

---

## Installation

Clone the repository and set up the environment:

```bash
git clone https://github.com/mtuciru/balalaika
cd balalaika
# Use this if you want to annotate/modify the dataset
bash create_dev_env.sh
# Use this if you only want to use the pre-annotated dataset
bash create_user_env.sh 
```

---

## Data Preparation

### Quick Setup (Default Parameters)

To download and prepare the dataset with default settings, choose one of the preconfigured dataset sizes:

* **100-hour dataset**
  ```bash
  bash use_meta_100h.sh
  ```

* **500-hour dataset**
  ```bash
  bash use_meta_500h.sh
  ```

* **1000-hour dataset**
  ```bash
  bash use_meta_1000h.sh
  ```

* **2000-hour dataset**
  ```bash
  bash use_meta_2000h.sh
  ```

All metadata can also be downloaded from [Hugging Face – MTUCI](https://huggingface.co/MTUCI).

### Custom Metadata Download

If you already have generated metadata files (`balalaika.parquet` and `balalaika.pkl`), place them in the project root and run:

```bash
bash use_meta.sh
```

---

## Running the Pipeline


### Basic Scenario (Local Processing)


This scenario will:

1. Download datasets
2. Split audio into semantic chunks
3. Transcribe all segments
4. Perform speaker segmentation
5. Apply phonemization

To execute locally, run:

```bash
bash base.sh configs/config.yaml
```

All output metadata will be saved in `podcasts/result.csv`.

---

## Configuration

The main configuration file is located at `configs/config.yaml`. This file is organized into several sections, each corresponding to a specific stage of the podcast processing pipeline. Below is a detailed explanation of the key parameters within each section.

---

### Global Parameters

* `podcasts_path`:  It specifies the **absolute path** to the directory where all downloaded podcast files will be stored and where subsequent processing (preprocessing, separation, transcription, etc.) will look for and save its output.
---

### `download` Section

This section controls how podcast episodes are downloaded.

* `podcasts_path`: (As explained above) The directory where downloaded podcasts will be saved.
* `episodes_limit`: This sets a **limit on the number of episodes** to download from a single podcast playlist.
* `num_workers`: Specifies the **number of parallel processes** to use for downloading. A higher number can speed up downloads but will consume more system resources.
* `podcasts_urls_file`: This parameter points to the **path of a `.pkl` file** that contains a list of podcast URLs to be downloaded.

---

### `preprocess` Section

This section handles the initial processing of downloaded audio files, such as chopping them into smaller segments.

* `podcasts_path`: (As explained above) The directory containing the raw downloaded podcasts that need to be preprocessed.
* `duration`: Defines the **maximum length in seconds** for each audio sample (segment).
* `num_workers`: Specifies the **number of parallel processes** to use during preprocessing.
* `whisper_model`: Specifies the **name or path of the Faster-Whisper compatible model** to be used for initial audio processing.
* `compute_type`: Determines the **computation type** for the Whisper model, affecting performance and memory usage.
* `beam_size`: This parameter is related to the **beam search algorithm** used in the Whisper model's decoding process.

---

### `separation` Section

This section calculates metrics for each audio

* `podcasts_path`: (As explained above) The directory where the chopped podcasts (from the `preprocess` stage) are located.
* `num_workers`: The **number of parallel processes** to use for audio separation.
* `nisqa_config`: Specifies the **path to the configuration file for NISQA** 
* `one_speaker`: A **boolean flag** (`True`/`False`) that, when enabled (`True`), instructs the system to download and process only those audio recordings that should contain a single speaker.

---

### `transcription` Section

This section is responsible for converting audio into text.

* `podcasts_path`: (As explained above) The directory containing the processed audio files ready for transcription.
* `model_name`: Specifies the **type of automatic speech recognition (ASR) model** to use. Options typically include `"ctc" or "rnnt"`.
* `num_workers`: The **number of parallel processes per GPU** to use for transcription.
* `with_timestamps`: A **boolean flag** (`True`/`False`) that, when enabled, allows the transcription process to generate timestamps for each word or segment. **it only works with ctc**
* `lm_path`: Specifies the **path to a language model file (`.bin`)**. A language model can improve transcription accuracy by providing contextual information. 

---

### `punctuation` Section

This section focuses on adding proper punctuation to the transcribed text.

* `podcasts_path`: (As explained above) The directory where the transcribed text files are located.
* `model_name`: Specifies the **name of the RUPunct model** to be used for punctuation restoration. 
* `num_workers`: The **number of parallel processes per GPU** to use for punctuation.
---

### `accent` Section

In the transcribed text this part is restored with accents.

* `podcasts_path`: (As explained above) The directory containing the relevant podcast files.
* `num_workers`: The **number of parallel processes per GPU** to use for accent processing.
* `model_name`: Specifies the **name of the ruAccent model** to be used.

---

### `phonemizer` Section

This section is responsible for converting text into phonetic representations (phonemes).

* `podcasts_path`: (As explained above) The directory where the text files (from transcription and punctuation stages) are located.
* `num_workers`: The **number of parallel processes per GPU** to use for phonemization.
---

### `classification` Section

This section relates to global speaker clustering.

* `podcasts_path`: (As explained above) The directory containing the podcast files relevant for classification.
* `num_workers`: The **number of parallel processes per GPU** to use for classification.
* `threshold`: This is the **speaker classification confidence threshold**. Values typically range from `0.6` to `0.9`. A higher threshold means the model needs to be more confident in its classification to assign a label. 
* `model_path`: Specifies the **path to the pretrained speaker classification model** in `.pt` format.
---

### Execution Scripts

Each processing script (`*_yaml.sh` and `*_args.sh`) offers flexibility in how parameters are provided:

* `*_yaml.sh`: These scripts read all necessary parameters directly from the main `config.yaml` file, ensuring consistency across different stages.
* `*_args.sh`: These scripts allow for hardcoded arguments directly within the shell script itself, which can be useful for quick tests or specific overrides without modifying the main configuration file.

## Environment Variables

Create a `.env` file in the project root with the following:

```ini
HF_TOKEN=<your_huggingface_token>
YANDEX_KEY=<your_yandex_music_token>
```

* `HF_TOKEN`: Required for speaker count estimation.
* `YANDEX_KEY`: Required for dataset downloads.

---

## Important Notes

- All scripts must be executed from the **project root directory**.
- Paths in the config file must be **absolute**.
- The processing scripts (punctuation, accents) should be run **sequentially**.
- You’ll need:
  - Yandex Music API key ([How to get one](https://yandex-music.readthedocs.io/en/main/token.html)) 
  - Hugging Face token

## Models

Place all required models under the `models/` directory with the following structure:

```
models/
├── voxblink_resnet/        # Speaker classification model
│   └── ...
└── nisqa_s.tar             # Audio quality assessment model
```

Supported models:

- [NISQA](https://github.com/deepvk/NISQA-s)  – Audio quality assessment.
- [GigaAM](https://github.com/salute-developers/GigaAM)  – ASR.
- [ruAccent](https://github.com/Den4ikAI/ruaccent)  – Accent restoration.
- [RUPunct](https://huggingface.co/RUPunct/RUPunct_big)  – Punctuation restoration.
- [VoxBlink ResNet](https://github.com/wenet-e2e/wespeaker)  – Speaker classification.
- [TryIPaG2P](https://github.com/NikiPshg/TryIPaG2P)  – Phonemization.
- [Speaker Diarization](https://github.com/pyannote/pyannote-audio)  – Speaker diarization.
- [Whisper](https://github.com/SYSTRAN/faster-whisper)  – ASR + segmentation

---

## Citation

If you use this pipeline in your research or production, please cite:
```
@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}, 
}
```

---

<!-- ## References and Acknowledgements

Thanks to all the developers and contributors who made this project possible.

<a href="https://github.com/mtuciru/balalaika/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=yeongpin/balalaikap&preview=true&max=&columns=" />
</a> -->


## License


### Dataset: Balalaika  
- **CC BY-NC-ND 4.0** – non-commercial, no derivatives, research use only.  
- Cite the corpus and do **not** redistribute files without written permission.

### Code  
- **CC BY-NC-SA 4.0** – You may use, modify, and share the material for academic, non-commercial purposes only.
-You must retain the copyright and license notices; contact the authors for commercial use.


### Third-Party Models & Libraries  
Comply with each component’s original license in addition to the above:

| Component | License |
|-----------|---------|
| NISQA-s | Apache 2.0 |
| GigaAM | MIT |
| ruAccent | CC BY-NC-ND 4.0 |
| RUPunct | CC BY-NC-ND 4.0 |
| VoxBlink ResNet | Apache 2.0 |
| TryIPaG2P | MIT |
| pyannote-audio | MIT |
| Faster-Whisper | MIT |