Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,186 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
+language:
+- ru
+pretty_name: OpenSTT annotate by Balalaika
+---
+
+# OpenSTT Annotated by Balalaika
+
+**A curated Russian speech dataset for advanced speech generative tasks.**
+
+## Overview
+
+**OpenSTT 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:** OpenSTT ([GitHub link](https://github.com/snakers4/open_stt?tab=readme-ov-file))
+- **License:** CC BY-NC 4.0 (same as original OpenSTT)
+- **Total Duration After Filtering:** 278.6 hours (from over 1000 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
+4. **Filtered out speech with music background** (custom music detector)
+5. **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))
+6. **Punctuation added** using [RuPunct](https://huggingface.co/RUPunct/RUPunct_big)
+7. **Stress marks added** via [RuAccent](https://github.com/Den4ikAI/ruaccent)
+8. **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**
+
+
+
+***
+
+## 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)
+- [GigaAMv2-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.
+
+***