Adaptive Stem Alignment

Adaptive Stem Alignment Workflow

This skill provides a resilient pattern for audio production that emphasizes incremental verification, fail-fast principles, and adaptive duration handling. Each major step produces verified outputs before proceeding, with explicit strategies for handling stems of different durations.

Overview

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

1. Early timing calculation - Derive section transitions from BPM and duration first
2. Verify reference audio - Validate input file properties and establish target duration
3. Generate and verify each stem individually - One stem at a time with immediate verification
4. Generate drum stem separately - Dedicated drum extension with rhythm patterns
5. Align stem durations - Handle duration mismatches with adaptive extension strategies
6. Apply effects with verification - Process each stem and verify output
7. Export master track - Mix all verified stems
8. Archive and final verification - Package deliverables with comprehensive checks

Key Differences from Standard Workflow

• Incremental verification: Verify each stem immediately after generation, not just at the end
• Fail-fast approach: Stop and report errors at each step rather than accumulating failures
• Early timing: Calculate section transitions before any audio generation
• Separated drums: Drum stem generation is a distinct step with rhythm-specific processing
• Memory-efficient: Process stems individually to avoid large array operations that cause sandbox failures
• Adaptive duration handling: Explicit strategies for mismatched stem durations (zero-padding, looping, crossfade extension)
• Pre-mix alignment: Verify all stems match target duration before mixing

Step 1: Calculate Timing Parameters (Early)

Calculate all timing parameters before generating any audio. This ensures consistent timing across all stems:

pythonCopy code
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 2: Verify Reference Audio

Validate the reference file exists and has expected properties:

pythonCopy code
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 reference duration as target

Step 3: Generate and Verify Each Stem Individually

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

pythonCopy code
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_subtype, expected_duration):
    """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 info.subtype != expected_subtype:
        errors.append(f'subtype: expected {expected_subtype}, got {info.subtype}')
    
    if abs(info.duration - expected_duration) > 1.0:  # Allow 1s tolerance
        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
SUBTYPE = 'FLOAT'
STEM_NAMES = ['bass', 'guitars', 'synths', 'bridge']

generated_stems = []
stem_durations = {}  # Track actual durations for alignment step

for stem_name in STEM_NAMES:
    print(f"\n=== Generating {stem_name} stem ===")
    
    # Generate
    filepath, data = generate_stem(stem_name, DURATION, SAMPLE_RATE, subtype=SUBTYPE)
    
    # Verify immediately
    result = verify_stem(filepath, SAMPLE_RATE, SUBTYPE, DURATION)
    
    if result['success']:
        print(f"✓ {stem_name} stem verified: {result['info'].duration:.2f}s @ {result['info'].samplerate}Hz")
        generated_stems.append(filepath)
        stem_durations[stem_name] = result['info'].duration
    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 4: Generate Drum Stem Separately

Drums require different processing (rhythm patterns, percussion sounds):

pythonCopy code
def generate_drum_stem(duration_sec, sample_rate, bpm, section_timing, subtype='FLOAT'):
    """Generate drum stem with rhythm patterns aligned to sections."""
    frames = int(duration_sec * sample_rate)
    audio_data = np.zeros(frames, dtype=np.float32)
    beats_per_second = bpm / 60.0
    
    # Simple kick drum pattern (every beat)
    kick_freq = 60
    kick_duration = 0.1
    kick_frames = int(kick_duration * sample_rate)
    
    for beat_time in np.arange(0, duration_sec, 1.0 / beats_per_second):
        start_frame = int(beat_time * sample_rate)
        end_frame = min(start_frame + kick_frames, frames)
        
        if start_frame < frames:
            t = np.linspace(0, kick_duration, end_frame - start_frame)
            kick = np.exp(-5 * t) * np.sin(2 * np.pi * kick_freq * t)
            audio_data[start_frame:end_frame] += kick * 0.9
    
    # Simple snare pattern (every 2nd and 4th beat)
    snare_freq = 200
    snare_duration = 0.05
    snare_frames = int(snare_duration * sample_rate)
    
    for beat_time in np.arange(0, duration_sec, 2.0 / beats_per_second):
        start_frame = int((beat_time + 0.5 / beats_per_second) * sample_rate)
        end_frame = min(start_frame + snare_frames, frames)
        
        if start_frame < frames:
            t = np.linspace(0, snare_duration, end_frame - start_frame)
            snare = np.exp(-10 * t) * np.random.uniform(-1, 1, len(t)) * 0.5
            audio_data[start_frame:end_frame] += snare * 0.7
    
    audio_data = np.clip(audio_data, -1, 1)
    
    filepath = 'drums_stem.wav'
    sf.write(filepath, audio_data, sample_rate, subtype=subtype, format='WAV')
    
    return filepath, audio_data

print("\n=== Generating drum stem ===")
drums_filepath, drums_data = generate_drum_stem(DURATION, SAMPLE_RATE, BPM, timing, subtype=SUBTYPE)

drums_result = verify_stem(drums_filepath, SAMPLE_RATE, SUBTYPE, DURATION)
if drums_result['success']:
    print(f"✓ Drum stem verified: {drums_result['info'].duration:.2f}s @ {drums_result['info'].samplerate}Hz")
    generated_stems.append(drums_filepath)
    stem_durations['drums'] = drums_result['info'].duration
else:
    print(f"✗ Drum stem FAILED: {drums_result['error']}")
    raise RuntimeError(f"Drum stem generation failed: {drums_result['error']}")

Step 5: Align Stem Durations (NEW)

Handle duration mismatches with adaptive extension strategies. Choose the appropriate method based on stem type:

Duration Mismatch Handling Strategies

pythonCopy code
def align_stem_duration(input_filepath, output_filepath, target_duration, sample_rate, 
                        subtype='FLOAT', strategy='auto', stem_type=None):
    """
    Align stem duration to target using appropriate strategy.
    
    Args:
        input_filepath: Path to input stem
        output_filepath: Path for aligned output
        target_duration: Target duration in seconds
        sample_rate: Sample rate
        subtype: Audio subtype (FLOAT, PCM_24, etc.)
        strategy: 'zero_pad', 'loop', 'crossfade', 'auto'
        stem_type: Type of stem ('rhythmic', 'melodic', 'ambient', 'percussion')
    
    Returns:
        dict with success status and alignment info
    """
    if not os.path.exists(input_filepath):
        return {'success': False, 'error': f'Input file not found: {input_filepath}'}
    
    # Load audio
    data, sr = sf.read(input_filepath)
    current_duration = len(data) / sr
    
    # Check if alignment needed (allow 0.5s tolerance)
    if abs(current_duration - target_duration) < 0.5:
        print(f"  Duration already aligned: {current_duration:.2f}s ≈ {target_duration:.2f}s")
        # Just copy file
        sf.write(output_filepath, data, sample_rate, subtype=subtype, format='WAV')
        return {'success': True, 'strategy': 'none', 'original_duration': current_duration}
    
    # Auto-detect strategy if not specified
    if strategy == 'auto':
        if stem_type in ['rhythmic', 'percussion', 'drums']:
            strategy = 'loop'
        elif stem_type in ['ambient', 'pad', 'drone', 'fx']:
            strategy = 'zero_pad'
        else:  # melodic, vocals, guitars, etc.
            strategy = 'crossfade'
    
    print(f"  Aligning duration: {current_duration:.2f}s → {target_duration:.2f}s using '{strategy}'")
    
    # Calculate frames needed
    target_frames = int(target_duration * sample_rate)
    current_frames = len(data)
    
    if strategy == 'zero_pad':
        # Simple zero-padding
        if current_frames < target_frames:
            aligned_data = np.zeros(target_frames, dtype=data.dtype)
            aligned_data[:current_frames] = data
        else:
            # Truncate with fade-out
            fade_frames = int(0.5 * sample_rate)  # 500ms fade
            aligned_data = data[:target_frames].copy()
            if target_frames < current_frames:
                fade_start = max(0, target_frames - fade_frames)
                fade_curve = np.linspace(1, 0, target_frames - fade_start)
                aligned_data[fade_start:] *= fade_curve
    
    elif strategy == 'loop':
        # Loop to fill duration
        aligned_data = np.zeros(target_frames, dtype=data.dtype)
        loop_count = (target_frames // current_frames) + 1
        
        for i in range(loop_count):
            start = i * current_frames
            end = min(start + current_frames, target_frames)
            copy_len = end - start
            
            if copy_len > 0:
                aligned_data[start:end] = data[:copy_len]
        
        # Apply crossfade at loop points to avoid clicks
        crossfade_frames = int(0.05 * sample_rate)  # 50ms crossfade
        if current_frames > crossfade_frames * 2:
            for i in range(1, loop_count):
                loop_start = i * current_frames
                if loop_start < target_frames:
                    # Crossfade region
                    cf_end = min(loop_start + crossfade_frames, target_frames)
                    cf_start = max(loop_start - crossfade_frames, 0)
                    if cf_end > cf_start:
                        fade_in = np.linspace(0, 1, cf_end - cf_start)
                        fade_out = np.linspace(1, 0, cf_end - cf_start)
                        aligned_data[cf_start:cf_end] = (
                            aligned_data[cf_start:cf_end] * fade_out +
                            np.roll(aligned_data[cf_start:cf_end], -current_frames) * fade_in
                        )
    
    elif strategy == 'crossfade':
        # Crossfade-based extension with smooth transition
        if current_frames < target_frames:
            # Extend with crossfaded loop
            extension_frames = target_frames - current_frames
            fade_frames = min(int(2.0 * sample_rate), extension_frames // 2)  # Max 2s fade
            
            # Create extension from looped content with fade
            extension_data = np.zeros(extension_frames, dtype=data.dtype)
            
            if extension_frames <= current_frames:
                # Just take from beginning with fade-in
                extension_data[:extension_frames] = data[:extension_frames]
                if fade_frames > 0:
                    fade_in = np.linspace(0, 1, min(fade_frames, extension_frames))
                    extension_data[:len(fade_in)] *= fade_in
            else:
                # Loop multiple times with crossfades
                loop_data = np.tile(data, (extension_frames // current_frames) + 2)[:extension_frames]
                
                # Apply fade-in to extension
                if fade_frames > 0:
                    fade_in = np.linspace(0, 1, fade_frames)
                    loop_data[:fade_frames] *= fade_in
                
                extension_data = loop_data
            
            # Concatenate with crossfade
            aligned_data = np.zeros(target_frames, dtype=data.dtype)
            aligned_data[:current_frames] = data
            
            # Crossfade region at junction
            if fade_frames > 0:
                junction_start = current_frames - fade_frames
                junction_end = min(current_frames + fade_frames, target_frames)
                
                if junction_end > junction_start:
                    crossfade_len = junction_end - junction_start
                    fade_out = np.linspace(1, 0, crossfade_len)
                    fade_in = np.linspace(0, 1, crossfade_len)
                    
                    aligned_data[junction_start:junction_end] = (
                        aligned_data[junction_start:junction_end] * fade_out +
                        extension_data[:crossfade_len] * fade_in
                    )
                else:
                    aligned_data[current_frames:current_frames + extension_frames] = extension_data
            else:
                aligned_data[current_frames:] = extension_data
        else:
            # Truncate with fade-out
            fade_frames = int(2.0 * sample_rate)
            aligned_data = data[:target_frames].copy()
            fade_start = max(0, target_frames - fade_frames)
            fade_curve = np.linspace(1, 0, target_frames - fade_start)
            aligned_data[fade_start:] *= fade_curve
    
    else:
        return {'success': False, 'error': f'Unknown strategy: {strategy}'}
    
    # Clip to prevent overload
    aligned_data = np.clip(aligned_data, -1, 1)
    
    # Export
    sf.write(output_filepath, aligned_data, sample_rate, subtype=subtype, format='WAV')
    
    # Verify
    result = verify_stem(output_filepath, sample_rate, subtype, target_duration)
    if result['success']:
        return {
            'success': True,
            'strategy': strategy,
            'original_duration': current_duration,
            'aligned_duration': result['info'].duration
        }
    else:
        return result

# Apply duration alignment to all stems
print("\n=== Aligning stem durations ===")
aligned_stems = []

for stem_name in STEM_NAMES:
    input_file = f'{stem_name}_stem.wav'
    output_file = f'{stem_name}_aligned.wav'
    
    # Determine stem type for strategy selection
    stem_type_map = {
        'bass': 'rhythmic',
        'guitars': 'melodic',
        'synths': 'ambient',
        'bridge': 'melodic'
    }
    stem_type = stem_type_map.get(stem_name, 'melodic')
    
    print(f"Aligning {stem_name} (type: {stem_type})...")
    result = align_stem_duration(
        input_file, output_file, TARGET_DURATION, SAMPLE_RATE,
        subtype=SUBTYPE, strategy='auto', stem_type=stem_type
    )
    
    if result['success']:
        if result['strategy'] != 'none':
            print(f"✓ {stem_name} aligned: {result['original_duration']:.2f}s → {result['aligned_duration']:.2f}s ({result['strategy']})")
        else:
            print(f"✓ {stem_name} already aligned")
        aligned_stems.append(output_file)
    else:
        print(f"✗ {stem_name} alignment FAILED: {result['error']}")
        raise RuntimeError(f"Stem alignment failed for {stem_name}: {result['error']}")

# Align drums separately
drums_aligned = 'drums_aligned.wav'
print(f"Aligning drums (type: percussion)...")
drums_result = align_stem_duration(
    'drums_stem.wav', drums_aligned, TARGET_DURATION, SAMPLE_RATE,
    subtype=SUBTYPE, strategy='auto', stem_type='percussion'
)
if drums_result['success']:
    if drums_result['strategy'] != 'none':
        print(f"✓ Drums aligned: {drums_result['original_duration']:.2f}s → {drums_result['aligned_duration']:.2f}s ({drums_result['strategy']})")
    else:
        print(f"✓ Drums already aligned")
    aligned_stems.append(drums_aligned)
else:
    raise RuntimeError(f"Drums alignment failed: {drums_result['error']}")

# Final duration verification - all stems must match
print("\n=== Verifying duration alignment ===")
final_durations = {}
for stem_file in aligned_stems:
    info = sf.info(stem_file)
    stem_name = os.path.basename(stem_file).replace('_aligned.wav', '')
    final_durations[stem_name] = info.duration
    duration_diff = abs(info.duration - TARGET_DURATION)
    
    if duration_diff > 0.5:
        print(f"✗ WARNING: {stem_name} duration mismatch: {info.duration:.2f}s vs target {TARGET_DURATION:.2f}s")
    else:
        print(f"✓ {stem_name}: {info.duration:.2f}s (Δ{duration_diff:.2f}s)")

max_duration_diff = max(abs(d - TARGET_DURATION) for d in final_durations.values())
if max_duration_diff > 0.5:
    raise RuntimeError(f"Duration alignment incomplete: max deviation {max_duration_diff:.2f}s exceeds tolerance")
print(f"\nAll stems aligned within tolerance (max deviation: {max_duration_diff:.2f}s)")

Step 6: Apply Effects with Verification

Process each stem and verify the output:

pythonCopy code
from scipy import signal

def apply_lowpass_filter(audio_data, sample_rate, cutoff_freq=8000):
    """Apply lowpass filter using scipy.signal."""
    nyquist = sample_rate / 2
    normalized_cutoff = cutoff_freq / nyquist
    b, a = signal.butter(4, normalized_cutoff, btype='low')
    return signal.filtfilt(b, a, audio_data)

def apply_effects_and_verify(input_filepath, output_filepath, sample_rate, subtype):
    """Apply effects to stem and verify output."""
    data, sr = sf.read(input_filepath)
    
    # Apply effects
    processed = apply_lowpass_filter(data, sr, cutoff_freq=8000)
    processed = np.clip(processed, -1, 1)
    
    # Export
    sf.write(output_filepath, processed, sample_rate, subtype=subtype, format='WAV')
    
    # Verify
    result = verify_stem(output_filepath, sample_rate, subtype, TARGET_DURATION)
    return result, processed

print("\n=== Applying effects to all stems ===")
processed_stems = []

for stem_name in STEM_NAMES:
    input_file = f'{stem_name}_aligned.wav'
    output_file = f'{stem_name}_processed.wav'
    
    print(f"Processing {stem_name}...")
    result, _ = apply_effects_and_verify(input_file, output_file, SAMPLE_RATE, SUBTYPE)
    
    if result['success']:
        print(f"✓ {stem_name} processed and verified")
        processed_stems.append(output_file)
    else:
        print(f"✗ {stem_name} processing FAILED: {result['error']}")
        raise RuntimeError(f"Effects processing failed for {stem_name}")

# Process drums separately
drums_output = 'drums_processed.wav'
drums_result, _ = apply_effects_and_verify('drums_aligned.wav', drums_output, SAMPLE_RATE, SUBTYPE)
if drums_result['success']:
    print(f"✓ Drums processed and verified")
    processed_stems.append(drums_output)
else:
    raise RuntimeError(f"Drums processing failed: {drums_result['error']}")

Step 7: Export Master Track

Mix all verified stems into master track:

pythonCopy code
def create_master_track(stem_files, output_filepath, sample_rate, subtype):
    """Create master track from verified stems with gain staging."""
    # Load first stem to get dimensions
    first_data, sr = sf.read(stem_files[0])
    master_audio = np.zeros(len(first_data), dtype=np.float32)
    
    # Verify all stems have matching length
    for stem_file in stem_files:
        data, _ = sf.read(stem_file)
        if len(data) != len(first_data):
            raise ValueError(f"Stem length mismatch: {stem_file} has {len(data)} frames, expected {len(first_data)}")
    
    # Mix all stems with gain staging
    gain_per_stem = 0.4  # Prevent clipping with 5 stems
   
    for i, stem_file in enumerate(stem_files):
        data, sr = sf.read(stem_file)
        master_audio += data * gain_per_stem
        print(f"  Mixed {os.path.basename(stem_file)} (gain: {gain_per_stem})")
    
    # Apply master bus limiting
    master_audio = np.clip(master_audio, -1, 1)
    
    # Soft clip for warmth
    master_audio = np.tanh(master_audio * 1.2) / 1.2
    
    # Export
    sf.write(output_filepath, master_audio, sample_rate, subtype=subtype, format='WAV')
    
    # Verify
    info = sf.info(output_filepath)
    print(f"Master exported: {info.duration:.2f}s @ {info.samplerate}Hz, {info.channels}ch")
    
    return output_filepath, master_audio

print("\n=== Creating master track ===")
master_filepath, master_data = create_master_track(processed_stems, 'master.wav', SAMPLE_RATE, SUBTYPE)

Step 8: Archive and Final Verification

Package deliverables with comprehensive checks:

pythonCopy code
import json
from datetime import datetime

def create_archive_manifest(stem_files, master_file, output_dir='deliverables'):
    """Create archive manifest with comprehensive verification."""
    os.makedirs(output_dir, exist_ok=True)
    
    manifest = {
        'created': datetime.now().isoformat(),
        'target_duration': TARGET_DURATION,
        'sample_rate': SAMPLE_RATE,
        'subtype': SUBTYPE,
        'stems': [],
        'master': None,
        'verification': {
            'all_stems_aligned': True,
            'all_stems_verified': True,
            'master_verified': True
        }
    }
    
    # Verify each stem
    for stem_file in stem_files:
        if not os.path.exists(stem_file):
            manifest['verification']['all_stems_verified'] = False
            continue
        
        info = sf.info(stem_file)
        stem_name = os.path.basename(stem_file)
        duration_diff = abs(info.duration - TARGET_DURATION)
        
        stem_info = {
            'file': stem_name,
            'duration': info.duration,
            'sample_rate': info.samplerate,
            'channels': info.channels,
            'duration_aligned': duration_diff < 0.5
        }
        manifest['stems'].append(stem_info)
        
        if duration_diff >= 0.5:
            manifest['verification']['all_stems_aligned'] = False
            print(f"WARNING: {stem_name} duration misaligned by {duration_diff:.2f}s")
    
    # Verify master
    if os.path.exists(master_file):
        info = sf.info(master_file)
        manifest['master'] = {
            'file': os.path.basename(master_file),
            'duration': info.duration,
            'sample_rate': info.samplerate,
            'channels': info.channels,
            'subtype': info.subtype
        }
        
        # Check master duration matches target
        if abs(info.duration - TARGET_DURATION) > 1.0:
            manifest['verification']['master_verified'] = False
            print(f"WARNING: Master duration {info.duration:.2f}s differs from target {TARGET_DURATION:.2f}s")
    else:
        manifest['verification']['master_verified'] = False
    
    # Save manifest
    manifest_path = os.path.join(output_dir, 'manifest.json')
    with open(manifest_path, 'w') as f:
        json.dump(manifest, f, indent=2)
    
    # Copy files to deliverables
    import shutil
    for stem_file in stem_files:
        shutil.copy(stem_file, output_dir)
    shutil.copy(master_file, output_dir)
    
    return manifest_path, manifest

print("\n=== Creating archive ===")
manifest_path, manifest = create_archive_manifest(processed_stems, master_filepath)
print(f"Archive manifest created: {manifest_path}")

# Final summary
print("\n" + "="*60)
print("PRODUCTION COMPLETE")
print("="*60)
print(f"Target duration: {TARGET_DURATION:.2f}s")
print(f"Sample rate: {SAMPLE_RATE}Hz")
print(f"Stems processed: {len(processed_stems)}")
print(f"All stems aligned: {manifest['verification']['all_stems_aligned']}")
print(f"Master verified: {manifest['verification']['master_verified']}")
print(f"Deliverables: ./deliverables/")
print("="*60)

Troubleshooting Duration Mismatches

Common Causes

1. Different sample rates: Ensure all stems use the same sample rate
2. Incorrect frame calculations: Verify frames = int(duration * sample_rate) calculations
3. Off-by-one errors: Check array indexing and loop boundaries
4. Resampling artifacts: When converting between sample rates, use high-quality resampling

Strategy Selection Guide

Use zero-padding when:

• Stem is ambient/pad/drone content
• Short duration mismatch (< 10% of total)
• Quick turnaround needed

Use looping when:

• Stem is rhythmic (drums, percussion, rhythmic bass)
• Content has clear loop points
• Loop length divides evenly into target duration

Use crossfade extension when:

• Stem is melodic (vocals, guitars, synths)
• Quality is priority over speed
• Significant duration extension needed

Use time-stretch when:

• Content cannot be looped or padded
• Pitch must be preserved
• High-quality processing is available (e.g., librubberband, elasticsearch)

Verification Checklist

• All stems have matching sample rate
• All stems within 0.5s of target duration
• No clipping in any stem (-1 to 1 range)
• Master track duration matches target
• Archive manifest generated with verification status