setup_annotation.py

#!/usr/bin/env python3
"""
Simple annotation setup for Voice Notes dataset
Creates task list from audio files and AI transcripts
"""

import json
import os
from pathlib import Path

def create_task_list():
    """Create annotation task list"""
    
    # Create annotations directory
    os.makedirs("annotations", exist_ok=True)
    
    # Find all audio files
    audio_files = list(Path("audio").glob("*.mp3"))
    audio_files.extend(list(Path("audio").glob("*.wav")))
    
    tasks = []
    dataset_metadata = []
    
    for audio_file in sorted(audio_files):
        file_id = audio_file.stem
        transcript_file = Path("aitranscripts") / f"{file_id}.txt"
        
        # Read AI transcript
        ai_transcript = ""
        if transcript_file.exists():
            ai_transcript = transcript_file.read_text().strip()
        
        task = {
            "id": file_id,
            "audio_path": str(audio_file),
            "ai_transcript": ai_transcript,
            "corrected_transcript": "",
            "parameters": {
                "speaker_info": "",
                "audio_quality": "",
                "environment": "",
                "corrections_needed": []
            },
            "status": "pending"
        }
        tasks.append(task)
        
        # Also create dataset metadata with all fields
        metadata_entry = {
            "id": file_id,
            "audio": str(audio_file),
            "ai_transcript": ai_transcript,
            "corrected_transcript": "",
            "audio_challenges": [],
            "non_speaker_content": "",
            "conversation_languages": [],
            "recording_place": "",
            "microphone_type": "",
            "recording_environment": "",
            "audio_quality": 0,
            "content_type": []
        }
        dataset_metadata.append(metadata_entry)
    
    # Save task list
    with open("annotations/task_list.json", "w") as f:
        json.dump(tasks, f, indent=2)
    
    # Save dataset metadata
    with open("dataset_metadata.json", "w") as f:
        json.dump(dataset_metadata, f, indent=2)
    
    print(f"Created {len(tasks)} annotation tasks")
    for task in tasks:
        print(f"- {task['id']}: {task['audio_path']}")
    
    return len(tasks)

def prepare_for_hf():
    """Prepare completed annotations for HF dataset"""
    try:
        from datasets import Dataset, Audio
        
        with open("annotations/task_list.json") as f:
            tasks = json.load(f)
        
        # Get completed tasks
        completed = [t for t in tasks if t["status"] == "completed"]
        
        if not completed:
            print("No completed annotations found")
            return None
        
        # Format for HF
        hf_data = []
        for task in completed:
            hf_data.append({
                "audio": task["audio_path"],
                "ai_transcript": task["ai_transcript"],
                "corrected_transcript": task["corrected_transcript"],
                "audio_challenges": task.get("audio_challenges", []),
                "non_speaker_content": task.get("non_speaker_content", ""),
                "conversation_languages": task.get("conversation_languages", []),
                "recording_place": task.get("recording_place", ""),
                "microphone_type": task.get("microphone_type", ""),
                "recording_environment": task.get("recording_environment", ""),
                "audio_quality": task.get("audio_quality", 0),
                "content_type": task.get("content_type", [])
            })
        
        dataset = Dataset.from_list(hf_data)
        dataset = dataset.cast_column("audio", Audio())
        
        # Save dataset
        dataset.save_to_disk("annotations/hf_dataset")
        print(f"HF dataset saved with {len(completed)} completed annotations")
        
        return dataset
        
    except ImportError:
        print("Install datasets: pip install datasets")
        return None

if __name__ == "__main__":
    create_task_list()
    print("\nNext steps:")
    print("1. Edit annotations/task_list.json")
    print("2. Add corrected transcripts and parameters")
    print("3. Set status to 'completed' when done")
    print("4. Run prepare_for_hf() to create HF dataset")