| |
| """ |
| Extract audio from Nigerian Common Voice parquet files. |
| """ |
| import os |
| import sys |
| from pathlib import Path |
|
|
| try: |
| import pandas as pd |
| from datasets import load_dataset |
| import soundfile as sf |
| except ImportError: |
| print("Installing required packages...") |
| os.system("pip install pandas datasets soundfile pyarrow") |
| import pandas as pd |
| from datasets import load_dataset |
| import soundfile as sf |
|
|
| BASE_DIR = Path.home() / "voice-training" |
| DATASETS_DIR = BASE_DIR / "datasets" / "nigerian_cv" |
| OUTPUT_DIR = BASE_DIR / "prepared_data" |
|
|
| LANGUAGES = ["yoruba", "hausa", "igbo", "english"] |
|
|
| def extract_language(lang: str): |
| """Extract audio files for a language.""" |
| lang_dir = DATASETS_DIR / lang |
| output_dir = OUTPUT_DIR / lang |
| output_dir.mkdir(parents=True, exist_ok=True) |
| |
| print(f"\n=== Extracting {lang.upper()} ===") |
| |
| for split in ["train", "validation", "test"]: |
| parquet_file = lang_dir / f"{split}-00000-of-00001.parquet" |
| if not parquet_file.exists(): |
| print(f" {split}: not found") |
| continue |
| |
| print(f" Processing {split}...") |
| try: |
| |
| df = pd.read_parquet(parquet_file) |
| print(f" Found {len(df)} samples") |
| |
| |
| print(f" Columns: {list(df.columns)}") |
| |
| |
| for idx in range(min(5, len(df))): |
| row = df.iloc[idx] |
| print(f" Sample {idx}: {row.get('sentence', 'N/A')[:50]}...") |
| |
| except Exception as e: |
| print(f" Error: {e}") |
|
|
| def main(): |
| print("=== Extracting Nigerian Common Voice Audio ===") |
| print(f"Input: {DATASETS_DIR}") |
| print(f"Output: {OUTPUT_DIR}") |
| |
| for lang in LANGUAGES: |
| if (DATASETS_DIR / lang).exists(): |
| extract_language(lang) |
| else: |
| print(f"\n{lang}: directory not found") |
| |
| print("\n=== Extraction complete ===") |
|
|
| if __name__ == "__main__": |
| main() |
|
|