| --- |
| license: mpl-2.0 |
| 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:** mpl-2.0 (same as original DeepSpeech) |
| - **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 .wav |
| - **Splitting:** Follows DeepSpeech 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) |
| - [DeepSpeech repository](https://github.com/GeorgeFedoseev/DeepSpeech) |
| - [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 **MPL 2.0**, matching original DeepSpeech terms. |
| |
| *** |
