Update README.md

README.md

@@ -28,25 +28,76 @@ pretty_name: OpenSTT annotate by Balalaika
 ## Usage
 
 **Primary Use Cases:**
-- Speech generative modeling (TTS)
-- Speech recognition
-- Accent, stress, and prosody analysis
-- Russian speech research
+- Text-to-Speech (TTS) generation
+- Automatic Speech Recognition (ASR)
+- Analysis of accent, stress, and prosody
+- Russian speech technology research
 
-1. Download dataset
-2. Unwrap files:
+### 1. Download the dataset
 
-```bash
-for a in *.tar.gz; do
-    dir="${a%.tar.gz}"
+### 2. Extract the files
+
+```basg
+for archive in *.tar.gz; do
+    dir="${archive%.tar.gz}"
     mkdir -p "$dir"
-    tar -xzvf "$a" -C "$dir"
-    rm "$a"
+    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"],
+            "phonemes": row["phonemes"]
+        }
+
+# 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