diagnostic-stem-delivery

name	diagnostic-stem-delivery
description	Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow

Diagnostic Stem Audio Production Workflow

This skill provides a resilient pattern for audio production that emphasizes diagnostic analysis before editing, explicit timecode extraction from documents, incremental verification, fail-fast principles, and mandatory deliverable verification. Each major step produces verified outputs before proceeding, with comprehensive audio diagnostics at specified timecodes.

Overview

Follow these steps in strict order. Each step must complete successfully and pass verification before proceeding to the next:

1. Parse timecodes from source documents - Extract edit spots/timecodes from DOCX/text sources
2. Perform diagnostic audio analysis - Analyze reference audio at each timecode (pitch, clicks, frequency)
3. Calculate timing parameters - Derive section transitions from BPM and duration
4. Verify reference audio - Validate input file properties and extract target duration
5. Generate and verify each stem individually - One stem at a time with immediate verification
6. Detect and resolve duration mismatches - Apply appropriate extension strategy
7. Apply edits based on diagnostics - Make informed edits using analysis results
8. Mix with verification - Combine stems and verify mix integrity
9. Export and verify deliverable - Generate final output with comprehensive checks

Key Principles

• Diagnostics first: Analyze audio at edit points BEFORE making any changes
• Document-driven: Parse timecodes directly from source documents (DOCX, TXT)
• Incremental verification: Verify each stem immediately after generation
• Fail-fast approach: Stop and report errors at each step
• Mandatory export: Final step MUST produce verified deliverable file
• Tool reliability: Use run_shell with inline Python for audio processing (avoid execute_code_sandbox for audio)

Step 0: Parse Timecodes from Source Documents

Extract edit spots and timecodes from document sources. Use python-docx via run_shell for reliable DOCX parsing:

# Parse DOCX file for timecodes and edit spots
python3 -c "
from docx import Document
import re
import sys

doc_path = sys.argv[1] if len(sys.argv) > 1 else 'Bass Edit Spots.docx'
doc = Document(doc_path)

edit_spots = []
timecode_pattern = r'(\d{1,2}:?\d{2}:?\d{2}[.:\d]*)|(\d+[.:\d]+)s'

for para in doc.paragraphs:
    text = para.text.strip()
    if not text:
        continue
    
    # Look for timecodes in various formats
    matches = re.findall(timecode_pattern, text, re.IGNORECASE)
    if matches:
        for match in matches:
            timecode = match[0] if match[0] else match[1]
            if timecode:
                edit_spots.append({'timecode': timecode, 'context': text[:100]})
    
    # Also check tables
    for table in doc.tables:
        for row in table.rows:
            for cell in row.cells:
                cell_text = cell.text.strip()
                matches = re.findall(timecode_pattern, cell_text, re.IGNORECASE)
                for match in matches:
                    timecode = match[0] if match[0] else match[1]
                    if timecode:
                        edit_spots.append({'timecode': timecode, 'context': cell_text[:100]})

print(f'Found {len(edit_spots)} edit spots:')
for i, spot in enumerate(edit_spots, 1):
    print(f'{i}. {spot[\"timecode\"]} - {spot[\"context\"][:50]}...')
"

Step 1: Perform Diagnostic Audio Analysis at Timecodes

Before any editing, analyze the reference audio at each identified timecode:

import numpy as np
import soundfile as sf
import librosa

def analyze_audio_at_timecode(filepath, timecode_str, sample_rate=48000):
    """
    Perform comprehensive diagnostic analysis at a specific timecode.
    
    Returns dict with:
    - pitch_estimate: Dominant frequency/pitch
    - click_pop_score: Likelihood of clicks/pops (0-1, higher = more likely)
    - frequency_spectrum: Dominant frequency bands
    - amplitude: RMS amplitude at timecode
    - issues: List of detected issues
    """
    # Parse timecode to seconds
    timecode_str = timecode_str.replace(':', '.').strip()
    if 's' in timecode_str:
        timecode_str = timecode_str.replace('s', '')
    
    try:
        parts = timecode_str.split('.')
        if len(parts) == 3:
            seconds = int(parts[0]) * 3600 + int(parts[1]) * 60 + float(parts[2])
        elif len(parts) == 2:
            seconds = int(parts[0]) * 60 + float(parts[1])
        else:
            seconds = float(parts[0])
    except:
        return {'error': f'Invalid timecode format: {timecode_str}'}
    
    # Load audio
    data, sr = sf.read(filepath)
    if sr != sample_rate:
        data = librosa.resample(data, orig_sr=sr, target_sr=sample_rate)
        sr = sample_rate
    
    # Extract window around timecode (±50ms for analysis)
    window_samples = int(0.1 * sample_rate)  # 100ms window
    start_sample = max(0, int(seconds * sample_rate) - window_samples // 2)
    end_sample = min(len(data), start_sample + window_samples)
    window = data[start_sample:end_sample]
    
    if len(window) < 100:
        return {'error': 'Window too short for analysis'}
    
    # Pitch detection (using autocorrelation for monophonic content)
    def estimate_pitch(signal, sr):
        # Simple autocorrelation-based pitch detection
        signal = signal - np.mean(signal)  # DC removal
        autocorr = np.correlate(signal, signal, mode='full')
        autocorr = autocorr[len(autocorr)//2:]
        
        # Find first significant peak after zero lag
        for i in range(1, min(len(autocorr) // 2, int(sr / 50))):
            if autocorr[i] > 0.3 * autocorr[0]:
                for j in range(i + 1, min(len(autocorr), int(sr / 20))):
                    if autocorr[j] > autocorr[i]:
                        period = j
                        freq = sr / period
                        return freq
        return None
    
    pitch = estimate_pitch(window, sr)
    
    # Click/pop detection (sudden amplitude changes)
    def detect_clicks(signal):
        diff = np.diff(np.abs(signal))
        threshold = 5 * np.std(diff)
        click_positions = np.where(np.abs(diff) > threshold)[0]
        click_score = min(1.0, len(click_positions) / len(signal) * 1000)
        return click_score, click_positions
    
    click_score, click_positions = detect_clicks(window)
    
    # Frequency analysis
    spectrum = np.abs(np.fft.rfft(window))
    freqs = np.fft.rfftfreq(len(window), 1/sr)
    dominant_freqs = []
    for band in [(20, 200, 'sub'), (200, 2000, 'mid'), (2000, 20000, 'high')]:
        mask = (freqs >= band[0]) & (freqs < band[1])
        if np.any(mask):
            band_power = np.sum(spectrum[mask])
            dominant_freqs.append({'range': f'{band[0]}-{band[1]}Hz', 'power': float(band_power), 'label': band[2]})
    dominant_freqs.sort(key=lambda x: x['power'], reverse=True)
    
    # Amplitude
    rms = np.sqrt(np.mean(window ** 2))
    
    # Detect issues
    issues = []
    if click_score > 0.3:
        issues.append(f'High click/pop probability ({click_score:.2f})')
    if rms < 0.001:
        issues.append('Near-silence detected')
    if rms > 0.9:
        issues.append('Potential clipping')
    if pitch and pitch < 40:
        issues.append(f'Very low frequency content ({pitch:.1f}Hz)')
    
    return {
        'timecode': timecode_str,
        'seconds': seconds,
        'pitch_hz': pitch,
        'click_pop_score': click_score,
        'frequency_spectrum': dominant_freqs[:3],
        'amplitude_rms': float(rms),
        'issues': issues,
        'window_length': len(window)
    }

# Analyze all edit spots
# edit_spots from Step 0
for i, spot in enumerate(edit_spots):
    print(f'\\n=== Analyzing edit spot {i+1}: {spot["timecode"]} ===')
    analysis = analyze_audio_at_timecode('reference.wav', spot['timecode'])
    if 'error' in analysis:
        print(f'ERROR: {analysis["error"]}')
    else:
        print(f'Pitch: {analysis["pitch_hz"]} Hz' if analysis["pitch_hz"] else 'Pitch: N/A (complex/noisy)')
        print(f'Click/Pop Score: {analysis["click_pop_score"]:.3f} (0=none, 1=certain)')
        print(f'Amplitude (RMS): {analysis["amplitude_rms"]:.6f}')
        if analysis['issues']:
            print(f'Issues: {", ".join(analysis["issues"])}')
        for freq in analysis['frequency_spectrum']:
            print(f'  {freq["label"]} band ({freq["range"]}): power={freq["power"]:.2f}')

Step 2: Calculate Timing Parameters (Early)

Calculate all timing parameters before generating any audio:

def calculate_section_transitions(bpm, total_duration_sec, sections):
    """Calculate beat-aligned transition points for song sections."""
    beats_per_second = bpm / 60.0
    
    section_durations = {}
    cumulative_time = 0
    
    for section_name, beat_count in sections.items():
        duration = beat_count / beats_per_second
        section_durations[section_name] = {
            'start': cumulative_time,
            'end': cumulative_time + duration,
            'beats': beat_count,
            'start_beat': cumulative_time * beats_per_second
        }
        cumulative_time += duration
    
    return section_durations

# Configuration
BPM = 120
DURATION = 137
SECTIONS = {'intro': 16, 'verse': 32, 'chorus': 32, 'bridge': 16, 'outro': 16}

timing = calculate_section_transitions(BPM, DURATION, SECTIONS)
print('Timing calculated:')
for section, data in timing.items():
    print(f'  {section}: {data["start"]:.2f}s - {data["end"]:.2f}s ({data["beats"]} beats)')

Step 3: Verify Reference Audio

Validate the reference file exists and has expected properties:

import soundfile as sf
import os

def verify_reference_file(filepath, expected_sample_rate=None, min_duration=None):
    """Verify reference audio file and return info dict."""
    if not os.path.exists(filepath):
        raise FileNotFoundError(f'Reference file not found: {filepath}')
    
    info = sf.info(filepath)
    errors = []
    
    if expected_sample_rate and info.samplerate != expected_sample_rate:
        errors.append(f'Sample rate mismatch: expected {expected_sample_rate}, got {info.samplerate}')
    
    if min_duration and info.duration < min_duration:
        errors.append(f'Duration too short: expected >= {min_duration}s, got {info.duration}s')
    
    if errors:
        raise ValueError(f'Reference file validation failed: {"; ".join(errors)}')
    
    print(f'Reference verified: {info.duration:.2f}s @ {info.samplerate}Hz, {info.channels}ch, {info.subtype}')
    return {
        'sample_rate': info.samplerate,
        'duration': info.duration,
        'channels': info.channels,
        'subtype': info.subtype
    }

# Verify reference
ref_info = verify_reference_file('reference.wav', expected_sample_rate=48000, min_duration=130)
TARGET_DURATION = ref_info['duration']  # Use actual reference duration as target

Step 4: Generate and Verify Each Stem Individually

Generate one stem at a time, verify it immediately before proceeding to the next:

import numpy as np

def generate_stem(name, duration_sec, sample_rate, subtype='FLOAT', section_timing=None):
    """Generate a single stem with explicit sample type."""
    frames = int(duration_sec * sample_rate)
    t = np.linspace(0, duration_sec, frames)
    
    # Generate stem-specific content (customize per stem type)
    if name == 'bass':
        freq = 110  # A2
        audio_data = np.sin(2 * np.pi * freq * t) * 0.8
    elif name == 'guitars':
        freq = 440  # A4
        audio_data = np.sin(2 * np.pi * freq * t) * 0.6
    elif name == 'synths':
        freq = 880  # A5
        audio_data = np.sin(2 * np.pi * freq * t) * 0.5
    elif name == 'bridge':
        freq = 220  # A3
        audio_data = np.sin(2 * np.pi * freq * t) * 0.7
    else:
        audio_data = np.sin(2 * np.pi * 440 * t) * 0.5
    
    # Ensure proper data type
    if subtype == 'FLOAT':
        audio_data = audio_data.astype(np.float32)
    elif subtype == 'PCM_24':
        audio_data = np.clip(audio_data, -1, 1) * (2**23 - 1)
        audio_data = audio_data.astype(np.int32)
    
    filepath = f'{name}_stem.wav'
    sf.write(filepath, audio_data, sample_rate, subtype=subtype, format='WAV')
    
    return filepath, audio_data

def verify_stem(filepath, expected_sample_rate, expected_duration, tolerance_sec=1.0):
    """Verify a single stem meets specifications."""
    if not os.path.exists(filepath):
        return {'success': False, 'error': f'File not found: {filepath}'}
    
    info = sf.info(filepath)
    errors = []
    
    if info.samplerate != expected_sample_rate:
        errors.append(f'sample_rate: expected {expected_sample_rate}, got {info.samplerate}')
    
    if abs(info.duration - expected_duration) > tolerance_sec:
        errors.append(f'duration: expected ~{expected_duration}s, got {info.duration}s')
    
    if errors:
        return {'success': False, 'error': '; '.join(errors)}
    
    return {'success': True, 'info': info}

# Generate stems one at a time with verification
SAMPLE_RATE = 48000
STEM_NAMES = ['bass', 'guitars', 'synths', 'bridge']

generated_stems = []
stem_info = {}

for stem_name in STEM_NAMES:
    print(f'\\n=== Generating {stem_name} stem ===')
    
    # Generate
    filepath, data = generate_stem(stem_name, DURATION, SAMPLE_RATE, subtype='FLOAT')
    
    # Verify immediately
    result = verify_stem(filepath, SAMPLE_RATE, TARGET_DURATION)
    
    if result['success']:
        print(f'✓ {stem_name} stem verified: {result["info"].duration:.2f}s @ {result["info"].samplerate}Hz')
        generated_stems.append(filepath)
        stem_info[stem_name] = result
    else:
        print(f'✗ {stem_name} stem FAILED: {result["error"]}')
        raise RuntimeError(f'Stem generation failed for {stem_name}: {result["error"]}')

print(f'\\nAll {len(generated_stems)} stems generated and verified successfully')

Step 5: Apply Edits Based on Diagnostics

Use the diagnostic analysis from Step 1 to make informed editing decisions:

def apply_edit_based_on_diagnostics(stem_filepath, edit_spot, analysis, output_filepath):
    """
    Apply targeted edit at a specific timecode based on diagnostic analysis.
    
    Decisions based on diagnostics:
    - High click/pop score (>0.3): Apply fade or click removal
    - Very low pitch (<40Hz): May need high-pass filter
    - Near-silence: Consider removal or gain adjustment
    - Potential clipping: Apply gain reduction
    """
    data, sr = sf.read(stem_filepath)
    
    timecode_sec = analysis['seconds']
    edit_start = max(0, int((timecode_sec - 0.05) * sr))
    edit_end = min(len(data), int((timecode_sec + 0.05) * sr))
    
    # Apply edits based on diagnostics
    if analysis['click_pop_score'] > 0.3:
        # Apply short crossfade to smooth clicks
        fade_len = min(100, (edit_end - edit_start) // 4)
        if fade_len > 0:
            fade_in = np.linspace(0, 1, fade_len)
            fade_out = np.linspace(1, 0, fade_len)
            data[edit_start:edit_start + fade_len] *= fade_in
            data[edit_end - fade_len:edit_end] *= fade_out
        print(f'  Applied click smoothing at {timecode_sec:.2f}s')
    
    if analysis['amplitude_rms'] > 0.9:
        # Apply gentle gain reduction to prevent clipping
        gain = 0.8
        data[edit_start:edit_end] *= gain
        print(f'  Applied gain reduction ({gain:.1f}x) at {timecode_sec:.2f}s')
    
    # Save edited stem
    sf.write(output_filepath, data, sr, subtype='FLOAT', format='WAV')
    return output_filepath

# Apply edits to stems based on diagnostic analysis
for stem_name in ['bass']:  # Customize as needed
    stem_file = f'{stem_name}_stem.wav'
    edited_file = f'{stem_name}_stem_edited.wav'
    
    print(f'\\n=== Applying edits to {stem_name} based on diagnostics ===')
    
    for i, spot in enumerate(edit_spots):
        # Re-analyze or use cached analysis
        analysis = analyze_audio_at_timecode('reference.wav', spot['timecode'])
        if 'error' not in analysis:
            apply_edit_based_on_diagnostics(stem_file, spot, analysis, edited_file)
    
    print(f'Edited {stem_name} stem saved to {edited_file}')

Step 6: Mix with Verification

Combine all stems and verify mix integrity:

def mix_stems(stem_files, output_filepath, sample_rate=48000):
    """Mix multiple stems into a single output file."""
    mixed_data = None
    
    for stem_file in stem_files:
        if not os.path.exists(stem_file):
            raise FileNotFoundError(f'Stem not found: {stem_file}')
        
        data, sr = sf.read(stem_file)
        
        # Resample if needed
        if sr != sample_rate:
            data = librosa.resample(data, orig_sr=sr, target_sr=sample_rate)
        
        # Ensure same length
        if mixed_data is None:
            mixed_data = np.zeros(len(data), dtype=np.float32)
        elif len(data) != len(mixed_data):
            min_len = min(len(data), len(mixed_data))
            mixed_data = mixed_data[:min_len]
            data = data[:min_len]
        
        mixed_data += data
    
    # Normalize to prevent clipping
    if np.max(np.abs(mixed_data)) > 0.95:
        mixed_data *= 0.95 / np.max(np.abs(mixed_data))
    
    sf.write(output_filepath, mixed_data, sample_rate, subtype='FLOAT', format='WAV')
    
    return {
        'filepath': output_filepath,
        'duration': len(mixed_data) / sample_rate,
        'peak': float(np.max(np.abs(mixed_data))),
        'rms': float(np.sqrt(np.mean(mixed_data ** 2)))
    }

# Mix all stems
print('\\n=== Mixing all stems ===')
all_stems = [f'{name}_stem_edited.wav' if os.path.exists(f'{name}_stem_edited.wav') 
             else f'{name}_stem.wav' for name in STEM_NAMES]

mix_info = mix_stems(all_stems, 'State_of_Affairs_FULL_EDIT_MIX.wav')
print(f'Mix complete: {mix_info["duration"]:.2f}s, peak={mix_info["peak"]:.3f}, RMS={mix_info["rms"]:.6f}')

Step 7: Export and Verify Deliverable (MANDATORY)

This step MUST complete successfully - no task is complete without verified deliverable:

def verify_deliverable(filepath, required_sample_rate=48000, required_channels=None, min_duration=None):
    """
    Comprehensive verification of final deliverable.
    
    Returns dict with verification status and details.
    Task CANNOT complete if verification fails.
    """
    if not os.path.exists(filepath):
        return {
            'success': False,
            'error': f'DELIVERABLE MISSING: {filepath}',
            'blocking': True
        }
    
    try:
        info = sf.info(filepath)
    except Exception as e:
        return {
            'success': False,
            'error': f'DELIVERABLE CORRUPT: {str(e)}',
            'blocking': True
        }
    
    errors = []
    warnings = []
    
    # Critical checks (blocking)
    if info.samplerate != required_sample_rate:
        errors.append(f'CRITICAL: Sample rate {info.samplerate}Hz != required {required_sample_rate}Hz')
    
    if required_channels and info.channels != required_channels:
        errors.append(f'CRITICAL: Channels {info.channels} != required {required_channels}')
    
    if min_duration and info.duration < min_duration:
        errors.append(f'CRITICAL: Duration {info.duration:.2f}s < minimum {min_duration}s')
    
    # Verify file is not empty
    if info.duration < 0.1:
        errors.append('CRITICAL: File appears to be empty or silent')
    
    # Non-critical checks (warnings)
    if info.duration < 60:
        warnings.append(f'Short duration: {info.duration:.2f}s')
    
    if os.path.getsize(filepath) < 1000:
        warnings.append('File size unusually small')
    
    # Load and analyze audio content
    data, sr = sf.read(filepath)
    peak = np.max(np.abs(data))
    rms = np.sqrt(np.mean(data ** 2))
    
    if peak > 0.99:
        warnings.append(f'Potential clipping: peak={peak:.4f}')
    
    if rms < 0.001:
        errors.append('CRITICAL: Audio appears to be silent (RMS too low)')
    
    # Final verdict
    success = len(errors) == 0
    
    result = {
        'success': success,
        'blocking': not success,
        'filepath': filepath,
        'info': {
            'duration': info.duration,
            'sample_rate': info.samplerate,
            'channels': info.channels,
            'subtype': info.subtype,
            'format': info.format,
            'peak': float(peak),
            'rms': float(rms),
            'file_size': os.path.getsize(filepath)
        },
        'errors': errors,
        'warnings': warnings
    }
    
    return result

# MANDATORY deliverable verification
print('\\n=== DELIVERABLE VERIFICATION (MANDATORY) ===')
deliverable_path = 'State_of_Affairs_FULL_EDIT_MIX.wav'

verification = verify_deliverable(
    deliverable_path,
    required_sample_rate=48000,
    required_channels=2,  # stereo
    min_duration=60  # minimum 60 seconds
)

if verification['success']:
    print('✓ DELIVERABLE VERIFIED SUCCESSFULLY')
    print(f'  File: {verification["filepath"]}')
    print(f'  Duration: {verification["info"]["duration"]:.2f}s')
    print(f'  Sample Rate: {verification["info"]["sample_rate"]}Hz')
    print(f'  Channels: {verification["info"]["channels"]}')
    print(f'  Peak: {verification["info"]["peak"]:.4f}')
    print(f'  RMS: {verification["info"]["rms"]:.6f}')
    
    if verification['warnings']:
        print('  Warnings:')
        for warn in verification['warnings']:
            print(f'    ⚠ {warn}')
    
    print('\\n✓ TASK COMPLETE - All deliverables verified')
    
else:
    print('✗ DELIVERABLE VERIFICATION FAILED')
    print('  ERRORS (blocking):')
    for err in verification['errors']:
        print(f'    ✗ {err}')
    
    if verification['warnings']:
        print('  Warnings:')
        for warn in verification['warnings']:
            print(f'    ⚠ {warn}')
    
    raise RuntimeError(f'Task cannot complete: {verification["errors"]}')

Tool Usage Notes

Critical for reliability:

1. DOCX Parsing: Use run_shell with python3 -c inline syntax and python-docx, NOT read_file (returns 'unknown error' for .docx)

2. Audio Processing: Use run_shell with inline Python scripts for audio operations, NOT execute_code_sandbox (frequently returns 'unknown error')

3. Heredoc Workaround: Avoid complex heredoc syntax in shell; use simpler -c inline Python for reliability

Example reliable pattern:

python3 -c "import soundfile as sf; import numpy as np; ...your code..."

Checklist Before Completion

• Step 0: Timecodes parsed from document source
• Step 1: Diagnostic analysis performed at all edit spots
• Step 2: Timing parameters calculated
• Step 3: Reference audio verified
• Step 4: All stems generated and verified individually
• Step 5: Edits applied based on diagnostic results
• Step 6: Stems mixed together
• Step 7: Deliverable exported AND verified (MANDATORY)
• Final file exists at expected path with correct format (48k/24b WAV)