Aligned Stem Audio Production Workflow

This skill provides a resilient pattern for audio production that emphasizes incremental verification, fail-fast principles, and automatic duration alignment. Each major step produces verified outputs before proceeding, with explicit handling for stem duration mismatches using appropriate extension strategies.

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 extract target duration
3. Generate and verify each stem individually - One stem at a time with immediate verification
4. Detect and resolve duration mismatches - Apply appropriate extension strategy (zero-pad, loop, or crossfade)
5. Generate drum stem separately - Dedicated drum extension with rhythm patterns
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
• Duration alignment: Explicit detection and resolution of stem duration mismatches
• Adaptive extension: Choose appropriate strategy (zero-pad/loop/crossfade) based on stem type
• 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

Step 1: Calculate Timing Parameters (Early)

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

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:

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 3: 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_subtype, 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 info.subtype != expected_subtype:
        errors.append(f'subtype: expected {expected_subtype}, got {info.subtype}')
    
    if abs(info.duration - expected_duration) > tolerance_sec:
        errors.append(f'duration: expected ~{expected_duration}s, got {info.duration}s')
    
    # Calculate duration discrepancy
    duration_diff = info.duration - expected_duration
    
    if errors:
        return {'success': False, 'error': '; '.join(errors), 'duration_diff': duration_diff}
    
    return {'success': True, 'info': info, 'duration_diff': duration_diff}

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

generated_stems = []
stem_info = {}  # Track duration discrepancies

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, TARGET_DURATION)
    
    if result['success']:
        print(f"✓ {stem_name} stem verified: {result['info'].duration:.2f}s @ {result['info'].samplerate}Hz")
        if abs(result['duration_diff']) > 0.1:
            print(f"  ⚠ Duration discrepancy: {result['duration_diff']:+.2f}s")
        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 4: Detect and Resolve Duration Mismatches

When stems have different durations, apply the appropriate extension strategy:

Strategy Selection Guidelines

Strategy	Best For	Duration Gap	Sound Characteristic
Zero-padding	Short gaps (<0.5s), silence sections, endings	Small	Clean, abrupt
Looping	Repetitive patterns (drums, bass, rhythmic elements)	Medium to large	Seamless, rhythmic
Crossfade extension	Melodic content, sustained instruments, vocals	Any	Natural, smooth

def align_stem_duration(input_filepath, output_filepath, target_duration, strategy='auto', 
                        sample_rate=None, subtype='FLOAT', loop_seamless=True):
    """
    Align stem duration to target using appropriate strategy.
    
    Args:
        input_filepath: Path to source stem
        output_filepath: Path for aligned output
        target_duration: Target duration in seconds
        strategy: 'zero_pad', 'loop', 'crossfade', or 'auto'
        sample_rate: Sample rate (auto-detected if None)
        subtype: Audio subtype
        loop_seamless: Apply crossfade at loop boundaries if True
    
    Returns:
        dict with success status and alignment details
    """
    if not os.path.exists(input_filepath):
        return {'success': False, 'error': f'Input file not found: {input_filepath}'}
    
    # Load source
    data, sr = sf.read(input_filepath)
    if sample_rate is None:
        sample_rate = sr
    
    source_duration = len(data) / sample_rate
    duration_diff = target_duration - source_duration
    
    # If already aligned (within tolerance), just copy
    if abs(duration_diff) < 0.01:
        sf.write(output_filepath, data, sample_rate, subtype=subtype, format='WAV')
        return {'success': True, 'strategy': 'none', 'duration_diff': 0}
    
    if duration_diff > 0:
        # Need to EXTEND
        extend_frames = int(duration_diff * sample_rate)
        
        if strategy == 'auto':
            # Auto-select based on duration gap and stem type
            if duration_diff < 0.5:
                strategy = 'zero_pad'
            elif 'drum' in input_filepath or 'bass' in input_filepath:
                strategy = 'loop'
            else:
                strategy = 'crossfade'
        
        if strategy == 'zero_pad':
            # Append zeros
            padding = np.zeros(extend_frames, dtype=data.dtype)
            aligned_data = np.concatenate([data, padding])
            
        elif strategy == 'loop':
            # Loop the content
            loop_frames = len(data)
            loops_needed = int(np.ceil(extend_frames / loop_frames))
            
            if loop_seamless and loops_needed > 1:
                # Apply crossfade at loop boundaries for seamless looping
                crossfade_frames = min(int(0.05 * sample_rate), loop_frames // 4)
                loop_extension = np.zeros(extend_frames, dtype=data.dtype)
                
                for i in range(loops_needed):
                    start = i * loop_frames
                    end = min(start + loop_frames, extend_frames)
                    actual_len = end - start
                    
                    # Extract loop segment
                    loop_segment = data[:actual_len].copy()
                    
                    # Apply crossfade at boundaries
                    if i > 0 and actual_len >= crossfade_frames * 2:
                        # Fade in from previous loop
                        fade_in = np.linspace(0, 1, crossfade_frames)
                        loop_segment[:crossfade_frames] *= fade_in
                    
                    if i < loops_needed - 1 and actual_len >= crossfade_frames * 2:
                        # Fade out for next loop
                        fade_out = np.linspace(1, 0, crossfade_frames)
                        loop_segment[-crossfade_frames:] *= fade_out
                    
                    loop_extension[start:end] = loop_segment
                
                extend_frames_actual = len(loop_extension)
            else:
                # Simple tiling
                loop_extension = np.tile(data, loops_needed)[:extend_frames]
                extend_frames_actual = extend_frames
            
            aligned_data = np.concatenate([data, loop_extension[:extend_frames_actual]])
            
        elif strategy == 'crossfade':
            # Extend using crossfade from the end of the source
            # Take last portion and crossfade it onto itself
            fade_duration = min(duration_diff * 0.3, 2.0)  # 30% of gap, max 2s
            fade_frames = int(fade_duration * sample_rate)
            
            if fade_frames >= len(data) // 2:
                # Source too short for crossfade, fall back to loop
                fade_frames = len(data) // 4
            
            # Extract tail segment for extension
            tail_segment = data[-fade_frames:].copy()
            
            # Create extended portion with crossfade
            extended_portion = np.zeros(extend_frames, dtype=data.dtype)
            
            if extend_frames <= fade_frames:
                # Short extension: just crossfade tail onto itself
                fade_in = np.linspace(0, 1, extend_frames)
                extended_portion = tail_segment[:extend_frames] * fade_in
            else:
                # Longer extension: loop tail with crossfades
                loops = int(np.ceil(extend_frames / fade_frames))
                for i in range(loops):
                    start = i * fade_frames
                    end = min(start + fade_frames, extend_frames)
                    seg_len = end - start
                    
                    segment = tail_segment[:seg_len].copy()
                    
                    # Crossfade boundaries
                    if seg_len >= 100:
                        cf_len = min(50, seg_len // 4)
                        if i > 0:
                            fade_in = np.linspace(0, 1, cf_len)
                            segment[:cf_len] *= fade_in
                    
                    extended_portion[start:end] = segment
            
            aligned_data = np.concatenate([data, extended_portion])
        
        else:
            return {'success': False, 'error': f'Unknown extension strategy: {strategy}'}
        
    else:
        # Need to TRUNCATE
        truncate_frames = int(abs(duration_diff) * sample_rate)
        aligned_data = data[:len(data) - truncate_frames]
        strategy = 'truncate'
    
    # Ensure proper data type and clip
    if subtype == 'FLOAT':
        aligned_data = aligned_data.astype(np.float32)
    elif subtype == 'PCM_24':
        aligned_data = np.clip(aligned_data, -1, 1) * (2**23 - 1)
        aligned_data = aligned_data.astype(np.int32)
    else:
        aligned_data = np.clip(aligned_data, -1, 1)
    
    # Export aligned stem
    sf.write(output_filepath, aligned_data, sample_rate, subtype=subtype, format='WAV')
    
    return {
        'success': True, 
        'strategy': strategy,
        'source_duration': source_duration,
        'target_duration': target_duration,
        'duration_diff': duration_diff,
        'aligned_frames': len(aligned_data)
    }

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

TARGET_DURATION = ref_info['duration']  # Use reference as target

for stem_name in STEM_NAMES:
    input_file = f'{stem_name}_stem.wav'
    output_file = f'{stem_name}_aligned.wav'
    
    # Determine strategy based on stem type
    if stem_name in ['bass', 'drums']:
        strategy = 'loop'  # Rhythmic elements loop well
    elif stem_name in ['bridge', 'outro']:
        strategy = 'crossfade'  # Sustained content benefits from crossfade
    else:
        strategy = 'auto'  # Let the function decide
    
    print(f"Aligning {stem_name} (strategy: {strategy})...")
    result = align_stem_duration(input_file, output_file, TARGET_DURATION, 
                                  strategy=strategy, sample_rate=SAMPLE_RATE, subtype=SUBTYPE)
    
    if result['success']:
        if result['strategy'] != 'none':
            print(f"✓ {stem_name} aligned: {result['source_duration']:.2f}s -> {result['target_duration']:.2f}s via {result['strategy']}")
        else:
            print(f"✓ {stem_name} already aligned at {result['target_duration']:.2f}s")
        aligned_stems.append(output_file)
    else:
        print(f"✗ {stem_name} alignment FAILED: {result['error']}")
        raise RuntimeError(f"Duration alignment failed for {stem_name}: {result['error']}")

print(f"\nAll {len(aligned_stems)} stems duration-aligned successfully")

Step 5: Generate Drum Stem Separately

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

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(TARGET_DURATION, SAMPLE_RATE, BPM, timing, subtype=SUBTYPE)

drums_result = verify_stem(drums_filepath, SAMPLE_RATE, SUBTYPE, TARGET_DURATION)
if drums_result['success']:
    print(f"✓ Drum stem verified: {drums_result['info'].duration:.2f}s @ {drums_result['info'].samplerate}Hz")
    # Align drums if needed (usually uses loop strategy)
    drums_aligned = 'drums_aligned.wav'
    drums_align_result = align_stem_duration(drums_filepath, drums_aligned, TARGET_DURATION,
                                              strategy='loop', sample_rate=SAMPLE_RATE, subtype=SUBTYPE)
    if drums_align_result['success']:
        aligned_stems.append(drums_aligned)
        print(f"✓ Drums aligned via {drums_align_result['strategy']}")
    else:
        print(f"✗ Drum alignment FAILED: {drums_align_result['error']}")
        raise RuntimeError(f"Drum alignment failed: {drums_align_result['error']}")
else:
    print(f"✗ Drum stem FAILED: {drums_result['error']}")
    raise RuntimeError(f"Drum stem generation failed: {drums_result['error']}")

Step 6: Apply Effects with Verification

Process each aligned stem and verify the output:

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, tolerance_sec=2.0)
    return result, processed

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

for aligned_file in aligned_stems:
    stem_name = os.path.basename(aligned_file).replace('_aligned.wav', '')
    output_file = f'{stem_name}_processed.wav'
    
    print(f"Processing {stem_name}...")
    result, _ = apply_effects_and_verify(aligned_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}")

print(f"\nAll {len(processed_stems)} stems processed successfully")

Step 7: Export Master Track

Mix all verified stems into master track:

def create_master_track(stem_files, output_filepath, sample_rate, subtype):
    """Create master track from verified stems."""
    # Load first stem to get dimensions
    first_data, sr = sf.read(stem_files[0])
    master_audio = np.zeros(len(first_data), dtype=np.float32)
    
    # Mix all stems with gain staging
    gain_per_stem = 0.4  # Prevent clipping with 5 stems
    
    for i, stem_file in enumerate(stem_files):
        print(f"  Mixing stem {i+1}/{len(stem_files)}: {stem_file}")
        data, sr = sf.read(stem_file)
        
        # Ensure same length as master
        if len(data) > len(master_audio):
            data = data[:len(master_audio)]
        elif len(data) < len(master_audio):
            # Pad with zeros if shorter (shouldn't happen after alignment)
            padding = np.zeros(len(master_audio) - len(data), dtype=np.float32)
            data = np.concatenate([data, padding])
        
        master_audio += data * gain_per_stem
    
    # Apply master bus processing
    master_audio = np.clip(master_audio, -1, 1)
    
    # Export master
    sf.write(output_filepath, master_audio, sample_rate, subtype=subtype, format='WAV')
    
    return output_filepath, master_audio

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

# Verify master
master_info = sf.info(master_filepath)
print(f"Master exported: {master_filepath}")
print(f"  Duration: {master_info.duration:.2f}s")
print(f"  Sample rate: {master_info.samplerate}Hz")
print(f"  Channels: {master_info.channels}")
print(f"  Subtype: {master_info.subtype}")

Step 8: Archive and Final Verification

Package deliverables with comprehensive checks:

def create_archive(stem_files, master_file, output_archive='audio交付.zip'):
    """Create archive of all deliverables."""
    import zipfile
    
    all_files = stem_files + [master_file]
    
    with zipfile.ZipFile(output_archive, 'w', zipfile.ZIP_DEFLATED) as zipf:
        for filepath in all_files:
            if os.path.exists(filepath):
                zipf.write(filepath)
                print(f"  Added: {filepath}")
    
    return output_archive

def final_verification(master_filepath, expected_duration=None, expected_sample_rate=None):
    """Comprehensive final verification."""
    issues = []
    
    # Verify master file
    if not os.path.exists(master_filepath):
        return {'success': False, 'error': 'Master file not found'}
    
    info = sf.info(master_filepath)
    
    if expected_duration and abs(info.duration - expected_duration) > 2.0:
        issues.append(f"Duration mismatch: expected ~{expected_duration}s, got {info.duration}s")
    
    if expected_sample_rate and info.samplerate != expected_sample_rate:
        issues.append(f"Sample rate mismatch: expected {expected_sample_rate}, got {info.samplerate}")
    
    # Check for clipping
    data, _ = sf.read(master_filepath)
    clip_ratio = np.sum(np.abs(data) >= 0.99) / len(data)
    if clip_ratio > 0.001:  # More than 0.1% clipped
        issues.append(f"Excessive clipping detected: {clip_ratio*100:.2f}% of samples at max level")
    
    # Check for silence
    rms = np.sqrt(np.mean(data**2))
    if rms < 0.01:
        issues.append(f"Audio too quiet: RMS level {rms:.4f}")
    
    success = len(issues) == 0
    
    return {
        'success': success,
        'issues': issues,
        'info': {
            'duration': info.duration,
            'sample_rate': info.samplerate,
            'channels': info.channels,
            'subtype': info.subtype,
            'clipping_ratio': clip_ratio,
            'rms_level': rms
        }
    }

print("\n=== Final verification ===")
final_result = final_verification(master_filepath, expected_duration=TARGET_DURATION, 
                                   expected_sample_rate=SAMPLE_RATE)

if final_result['success']:
    print("✓ All verification checks passed")
    print(f"  Master: {final_result['info']['duration']:.2f}s @ {final_result['info']['sample_rate']}Hz")
    print(f"  RMS level: {final_result['info']['rms_level']:.4f}")
    print(f"  Clipping: {final_result['info']['clipping_ratio']*100:.2f}%")
    
    # Create archive
    print("\n=== Creating archive ===")
    archive_path = create_archive(processed_stems, master_filepath, 'audio_deliverables.zip')
    print(f"✓ Archive created: {archive_path}")
else:
    print("✗ Verification failed:")
    for issue in final_result['issues']:
        print(f"  - {issue}")

Troubleshooting

Duration Mismatch Issues

Problem: Stems have significantly different durations causing alignment artifacts

Solutions:

1. Check source generation: Ensure all stems use the same duration parameter
2. Adjust strategy: Switch from auto to explicit strategy based on content type
3. Tune crossfade parameters: Increase fade_duration for smoother transitions
4. Enable seamless looping: Set loop_seamless=True for rhythmic content

Extension Quality Issues

Problem: Loop points audible or crossfade sounds unnatural

Solutions:

1. For looping: Find better loop points (at zero-crossings or beat boundaries)
2. For crossfade: Increase overlap duration or use different source segments
3. For padding: Only use for very short gaps (<0.3s) at song endings

Memory Issues

Problem: Large files cause sandbox failures

Solutions:

1. Process in chunks: Use streaming I/O for very long files
2. Reduce sample rate: Temporarily work at 44.1kHz, upsample for final export
3. Process stems sequentially: Clear memory between stem operations