🎛️ Pedalboard Cheat Sheet #

# Combine libraries for comprehensive audio processing
from pedalboard import Pedalboard, Compressor, Reverb
from pydub import AudioSegment
import librosa
import audioflux as af

def comprehensive_audio_pipeline(input_file):
    """Example combining multiple libraries"""
    
    # 1. Load and basic manipulation with Pydub-NG
    audio = AudioSegment.from_file(input_file)
    audio = audio.normalize()  # Basic preprocessing
    
    # 2. Convert to numpy for analysis
    samples = audio.get_array_of_samples()
    audio_np = np.array(samples, dtype=np.float32)
    
    # 3. Feature extraction with librosa
    tempo, beats = librosa.beat.beat_track(y=audio_np, sr=audio.frame_rate)
    
    # 4. Advanced analysis with AudioFlux
    spectral_features = af.spectral_centroid(audio_np)
    
    # 5. Real-time effects with Pedalboard
    board = Pedalboard([Compressor(), Reverb()])
    processed = board(audio_np.reshape(1, -1), audio.frame_rate)
    
    return processed, tempo, spectral_features

pip install pedalboard

uv add pedalboard

# For contributing or custom builds
git clone https://github.com/spotify/pedalboard.git
cd pedalboard
pip install -e .

from pedalboard import Chorus, Distortion, Phaser, Clipping

# Guitar effects
chorus = Chorus()
distortion = Distortion()
phaser = Phaser()
clipping = Clipping()

from pedalboard import Compressor, Gain, Limiter

# Dynamics control
compressor = Compressor(threshold_db=-20, ratio=4)
gain = Gain(gain_db=6)
limiter = Limiter()

from pedalboard import HighpassFilter, LadderFilter, LowpassFilter

# Filtering
highpass = HighpassFilter(cutoff_hz=100)
lowpass = LowpassFilter(cutoff_hz=8000)
ladder = LadderFilter(mode=LadderFilter.Mode.HPF12, cutoff_hz=900)

from pedalboard import Convolution, Delay, Reverb

# Spatial processing
delay = Delay(delay_seconds=0.25, mix=0.5)
reverb = Reverb(room_size=0.25)
convolution = Convolution("./impulse_response.wav", 1.0)

from pedalboard import PitchShift

# Pitch manipulation
pitch_shift = PitchShift(semitones=7)

from pedalboard import GSMFullRateCompressor, MP3Compressor, Resample, Bitcrush

# Lossy compression
gsm_comp = GSMFullRateCompressor()
mp3_comp = MP3Compressor()

# Quality reduction
resample = Resample(target_sample_rate=22050)
bitcrush = Bitcrush(bit_depth=8)

from pedalboard import Pedalboard, Chorus, Reverb
from pedalboard.io import AudioFile

# Create pedalboard with effects
board = Pedalboard([Chorus(), Reverb(room_size=0.25)])

# Process audio file
with AudioFile('input.wav') as f:
    with AudioFile('output.wav', 'w', f.samplerate, f.num_channels) as o:
        while f.tell() < f.frames:
            chunk = f.read(f.samplerate)  # Read 1 second
            effected = board(chunk, f.samplerate, reset=False)
            o.write(effected)

from pedalboard import *
from pedalboard.io import AudioFile

# Read entire file with resampling
samplerate = 44100.0
with AudioFile('input.wav').resampled_to(samplerate) as f:
    audio = f.read(f.frames)

# Create complex pedalboard
board = Pedalboard([
    Compressor(threshold_db=-50, ratio=25),
    Gain(gain_db=30),
    Chorus(),
    LadderFilter(mode=LadderFilter.Mode.HPF12, cutoff_hz=900),
    Phaser(),
    Convolution("./guitar_amp.wav", 1.0),
    Reverb(room_size=0.25),
])

# Process and save
effected = board(audio, samplerate)
with AudioFile('output.wav', 'w', samplerate, effected.shape[0]) as f:
    f.write(effected)

from pedalboard import Pedalboard, Chorus, Compressor, Delay, Gain, Reverb, Phaser
from pedalboard.io import AudioStream

# Create real-time effects chain
board = Pedalboard([
    Compressor(threshold_db=-16, ratio=2.5),
    Gain(gain_db=3),
    Chorus(rate_hz=1.0, depth=0.25, mix=0.3),
    Phaser(),
    Delay(delay_seconds=0.125, mix=0.25),
    Reverb(room_size=0.25, mix=0.2),
])

# Stream live audio
with AudioStream(
    input_device_name="Built-in Microphone",
    output_device_name="Built-in Output",
) as stream:
    stream.plugins = board
    input("Press Enter to stop streaming...")

from pedalboard import load_plugin, Pedalboard, Reverb
from mido import Message

# Load VST3 or Audio Unit
instrument = load_plugin("./VSTs/Magical8BitPlug2.vst3")
effect = load_plugin("./VSTs/RoughRider3.vst3")

# Inspect parameters
print(effect.parameters.keys())
# Set parameters
effect.ratio = 15

# Use with MIDI for instruments
sample_rate = 44100
audio = instrument(
    [Message("note_on", note=60), Message("note_off", note=60, time=5)],
    duration=5,
    sample_rate=sample_rate,
)

# Chain with other effects
board = Pedalboard([effect, Reverb()])
effected = board(audio, sample_rate)

from pedalboard import Pedalboard, Compressor, Delay, Gain, PitchShift, Reverb, Mix

# Create parallel processing chains
passthrough = Gain(gain_db=0)

delay_and_pitch_shift = Pedalboard([
    Delay(delay_seconds=0.25, mix=1.0),
    PitchShift(semitones=7),
    Gain(gain_db=-3),
])

delay_longer_and_more_pitch_shift = Pedalboard([
    Delay(delay_seconds=0.5, mix=1.0),
    PitchShift(semitones=12),
    Gain(gain_db=-6),
])

# Combine with Mix plugin
board = Pedalboard([
    Compressor(),
    Mix([
        passthrough,
        delay_and_pitch_shift,
        delay_longer_and_more_pitch_shift,
    ]),
    Reverb()
])

from pedalboard import Pedalboard, Compressor, Gain, Limiter

# Create pedalboard
board = Pedalboard([
    Compressor(threshold_db=-50, ratio=25),
    Gain(gain_db=30),
])

# Pedalboards behave like lists
board.append(Limiter())
board.insert(1, Gain(gain_db=10))

# Modify parameters dynamically
board[0].threshold_db = -40
board[1].gain_db = 15

# Remove effects
del board[2]

import tensorflow as tf
from pedalboard import Pedalboard, Compressor, Reverb

def apply_effects(audio_batch):
    board = Pedalboard([Compressor(), Reverb()])
    return board(audio_batch, sample_rate=44100)

# Use in tf.data pipeline
dataset = tf.data.Dataset.from_tensor_slices(audio_data)
dataset = dataset.map(apply_effects)

from pedalboard.io import AudioFile

# Basic file reading
with AudioFile('input.wav') as f:
    print(f"Sample rate: {f.samplerate}")
    print(f"Channels: {f.num_channels}")
    print(f"Frames: {f.frames}")
    print(f"Duration: {f.duration}")
    
    # Read all audio
    audio = f.read(f.frames)
    
    # Read chunk by chunk
    chunk_size = f.samplerate  # 1 second
    while f.tell() < f.frames:
        chunk = f.read(chunk_size)
        # Process chunk...

from pedalboard.io import AudioFile
import numpy as np

# Generate test audio
sample_rate = 44100
duration = 5.0
audio = np.sin(2 * np.pi * 440 * np.linspace(0, duration, int(sample_rate * duration)))

# Write to file
with AudioFile('output.wav', 'w', sample_rate, 1) as f:
    f.write(audio)

# Write with different formats
with AudioFile('output.flac', 'w', sample_rate, 1) as f:
    f.write(audio)

from pedalboard.io import AudioFile

# Automatic resampling during read
target_rate = 22050
with AudioFile('input.wav').resampled_to(target_rate) as f:
    audio = f.read(f.frames)
    print(f"Resampled to: {f.samplerate} Hz")

from pedalboard import Compressor, Reverb

# Create effects with parameters
comp = Compressor(threshold_db=-20, ratio=4, attack_ms=10, release_ms=100)
reverb = Reverb(room_size=0.5, damping=0.3, wet_level=0.2, dry_level=0.8)

# Access and modify parameters
print(comp.threshold_db)  # -20.0
comp.ratio = 6
reverb.room_size = 0.8

# Get all parameters
print(comp.parameters)

import numpy as np
from pedalboard import Pedalboard, LowpassFilter

board = Pedalboard([LowpassFilter(cutoff_hz=1000)])
sample_rate = 44100
audio = np.random.randn(sample_rate * 2)  # 2 seconds of noise

# Process with parameter automation
chunk_size = 1024
output = []

for i in range(0, len(audio), chunk_size):
    chunk = audio[i:i+chunk_size]
    
    # Automate cutoff frequency
    time = i / sample_rate
    cutoff = 500 + 1500 * (0.5 + 0.5 * np.sin(2 * np.pi * 0.5 * time))
    board[0].cutoff_hz = cutoff
    
    processed = board(chunk, sample_rate, reset=False)
    output.append(processed)

final_audio = np.concatenate(output)

# ✅ Good: Process in chunks for large files
with AudioFile('large_file.wav') as f:
    chunk_size = f.samplerate  # 1 second chunks
    while f.tell() < f.frames:
        chunk = f.read(chunk_size)
        processed = board(chunk, f.samplerate, reset=False)

# ❌ Avoid: Loading entire large files into memory
with AudioFile('large_file.wav') as f:
    audio = f.read(f.frames)  # May cause memory issues

# For continuous processing (streaming/chunks)
board = Pedalboard([Delay(), Reverb()])

# Don't reset between chunks
for chunk in audio_chunks:
    processed = board(chunk, sample_rate, reset=False)

# Reset when starting new audio stream
processed = board(new_audio, sample_rate, reset=True)

# ✅ Good: Use context managers
with AudioFile('input.wav') as f:
    audio = f.read(f.frames)

# ✅ Good: Process in place when possible
audio = board(audio, sample_rate)  # Modifies audio in place

# ✅ Good: Delete large arrays when done
del audio

from pedalboard import load_plugin
from pedalboard.io import AudioFile

try:
    plugin = load_plugin("./path/to/plugin.vst3")
except Exception as e:
    print(f"Failed to load plugin: {e}")

try:
    with AudioFile('input.wav') as f:
        audio = f.read(f.frames)
except FileNotFoundError:
    print("Audio file not found")
except Exception as e:
    print(f"Error reading audio: {e}")

import random
from pedalboard import Pedalboard, Gain, PitchShift, Reverb, Compressor

def augment_audio(audio, sample_rate):
    """Apply random audio augmentation for ML training"""
    effects = []
    
    # Random gain adjustment
    if random.random() > 0.5:
        gain_db = random.uniform(-6, 6)
        effects.append(Gain(gain_db=gain_db))
    
    # Random pitch shift
    if random.random() > 0.7:
        semitones = random.uniform(-2, 2)
        effects.append(PitchShift(semitones=semitones))
    
    # Random reverb
    if random.random() > 0.6:
        room_size = random.uniform(0.1, 0.8)
        effects.append(Reverb(room_size=room_size, mix=0.2))
    
    # Random compression
    if random.random() > 0.5:
        threshold = random.uniform(-30, -10)
        ratio = random.uniform(2, 8)
        effects.append(Compressor(threshold_db=threshold, ratio=ratio))
    
    if effects:
        board = Pedalboard(effects)
        return board(audio, sample_rate)
    return audio

from pedalboard import Pedalboard, Compressor, Distortion, Chorus, Delay, Reverb
from pedalboard.io import AudioStream

# Create guitar amp simulation
guitar_amp = Pedalboard([
    Compressor(threshold_db=-18, ratio=3, attack_ms=5, release_ms=50),
    Distortion(drive_db=15),
    Chorus(rate_hz=0.5, depth=0.3, mix=0.4),
    Delay(delay_seconds=0.2, feedback=0.3, mix=0.25),
    Reverb(room_size=0.4, mix=0.15),
])

# Stream guitar input
with AudioStream(
    input_device_name="Audio Interface",
    output_device_name="Speakers",
    sample_rate=44100,
    buffer_size=128,  # Low latency
) as stream:
    stream.plugins = guitar_amp
    print("Guitar amp ready! Play your guitar...")
    input("Press Enter to stop...")

import os
from pathlib import Path
from pedalboard import Pedalboard, Compressor, Limiter
from pedalboard.io import AudioFile

def process_audio_batch(input_dir, output_dir, effects_chain):
    """Process all audio files in a directory"""
    input_path = Path(input_dir)
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)
    
    audio_extensions = {'.wav', '.mp3', '.flac', '.aiff', '.ogg'}
    
    for file_path in input_path.iterdir():
        if file_path.suffix.lower() in audio_extensions:
            print(f"Processing: {file_path.name}")
            
            try:
                with AudioFile(str(file_path)) as f:
                    audio = f.read(f.frames)
                    
                processed = effects_chain(audio, f.samplerate)
                
                output_file = output_path / f"{file_path.stem}_processed{file_path.suffix}"
                with AudioFile(str(output_file), 'w', f.samplerate, f.num_channels) as out:
                    out.write(processed)
                    
            except Exception as e:
                print(f"Error processing {file_path.name}: {e}")

# Usage
mastering_chain = Pedalboard([
    Compressor(threshold_db=-12, ratio=3),
    Limiter(threshold_db=-1),
])

process_audio_batch('./input_tracks', './mastered_tracks', mastering_chain)

import unittest
import numpy as np
from pedalboard import Pedalboard, Compressor, Gain, Reverb
from pedalboard.io import AudioFile
import tempfile
import os

class TestPedalboardProcessing(unittest.TestCase):
    def setUp(self):
        """Create test audio and temporary files"""
        self.sample_rate = 44100
        self.duration = 1.0  # 1 second
        self.num_samples = int(self.sample_rate * self.duration)
        
        # Generate test sine wave
        t = np.linspace(0, self.duration, self.num_samples)
        self.test_audio = np.sin(2 * np.pi * 440 * t).astype(np.float32)
        self.test_audio = self.test_audio.reshape(1, -1)  # Mono
        
        self.temp_dir = tempfile.mkdtemp()
    
    def tearDown(self):
        """Clean up temporary files"""
        import shutil
        shutil.rmtree(self.temp_dir)
    
    def test_gain_effect(self):
        """Test gain adjustment"""
        gain_db = 6.0
        board = Pedalboard([Gain(gain_db=gain_db)])
        
        processed = board(self.test_audio, self.sample_rate)
        
        # Check that gain was applied (approximately)
        expected_gain_linear = 10 ** (gain_db / 20)
        actual_gain = np.max(np.abs(processed)) / np.max(np.abs(self.test_audio))
        
        self.assertAlmostEqual(actual_gain, expected_gain_linear, places=2)
    
    def test_compressor_reduces_peaks(self):
        """Test that compressor reduces dynamic range"""
        # Create audio with varying amplitude
        loud_audio = self.test_audio * 0.9
        quiet_audio = self.test_audio * 0.1
        test_signal = np.concatenate([loud_audio, quiet_audio], axis=1)
        
        board = Pedalboard([Compressor(threshold_db=-20, ratio=4)])
        processed = board(test_signal, self.sample_rate)
        
        # Check that dynamic range was reduced
        original_range = np.max(test_signal) - np.min(test_signal)
        processed_range = np.max(processed) - np.min(processed)
        
        self.assertLess(processed_range, original_range)
    
    def test_audio_file_roundtrip(self):
        """Test file I/O operations"""
        temp_file = os.path.join(self.temp_dir, "test.wav")
        
        # Write test audio
        with AudioFile(temp_file, 'w', self.sample_rate, 1) as f:
            f.write(self.test_audio)
        
        # Read it back
        with AudioFile(temp_file) as f:
            loaded_audio = f.read(f.frames)
        
        # Check that audio is preserved (within floating point precision)
        np.testing.assert_array_almost_equal(
            self.test_audio, loaded_audio, decimal=5
        )
    
    def test_pedalboard_chain_order(self):
        """Test that effect order matters"""
        # Chain 1: Gain then Compressor
        chain1 = Pedalboard([Gain(gain_db=12), Compressor(threshold_db=-10)])
        result1 = chain1(self.test_audio, self.sample_rate)
        
        # Chain 2: Compressor then Gain
        chain2 = Pedalboard([Compressor(threshold_db=-10), Gain(gain_db=12)])
        result2 = chain2(self.test_audio, self.sample_rate)
        
        # Results should be different
        self.assertFalse(np.allclose(result1, result2, rtol=1e-3))
    
    def test_reset_parameter(self):
        """Test reset parameter for stateful effects"""
        board = Pedalboard([Reverb(room_size=0.5)])
        
        # Process with reset=True (default)
        result1 = board(self.test_audio, self.sample_rate, reset=True)
        result2 = board(self.test_audio, self.sample_rate, reset=True)
        
        # Results should be identical
        np.testing.assert_array_almost_equal(result1, result2, decimal=5)
        
        # Process with reset=False (continuous)
        board(self.test_audio, self.sample_rate, reset=True)  # Initialize
        result3 = board(self.test_audio, self.sample_rate, reset=False)
        result4 = board(self.test_audio, self.sample_rate, reset=False)
        
        # Results should be different (reverb tail continues)
        self.assertFalse(np.allclose(result3, result4, rtol=1e-3))

if __name__ == '__main__':
    unittest.main()

from hypothesis import given, strategies as st
import numpy as np
from pedalboard import Pedalboard, Gain

class TestAudioProperties:
    @given(st.floats(min_value=-20, max_value=20))
    def test_gain_linearity(self, gain_db):
        """Test that gain changes are linear"""
        sample_rate = 44100
        test_audio = np.random.randn(1, 1000).astype(np.float32) * 0.1
        
        board = Pedalboard([Gain(gain_db=gain_db)])
        processed = board(test_audio, sample_rate)
        
        expected_gain = 10 ** (gain_db / 20)
        actual_gain = np.sqrt(np.mean(processed**2)) / np.sqrt(np.mean(test_audio**2))
        
        # Allow for some numerical precision
        assert abs(actual_gain - expected_gain) < 0.01
    
    @given(st.integers(min_value=1000, max_value=100000))
    def test_audio_length_preservation(self, num_samples):
        """Test that audio length is preserved"""
        sample_rate = 44100
        test_audio = np.random.randn(1, num_samples).astype(np.float32) * 0.1
        
        board = Pedalboard([Gain(gain_db=0)])  # No-op gain
        processed = board(test_audio, sample_rate)
        
        assert processed.shape == test_audio.shape

import time
import numpy as np
from pedalboard import Pedalboard, Compressor, Reverb, Delay

def benchmark_pedalboard_performance():
    """Benchmark Pedalboard processing speed"""
    sample_rate = 44100
    duration = 10.0  # 10 seconds
    num_samples = int(sample_rate * duration)
    
    # Generate test audio
    test_audio = np.random.randn(2, num_samples).astype(np.float32) * 0.1
    
    # Create complex pedalboard
    board = Pedalboard([
        Compressor(threshold_db=-20, ratio=4),
        Delay(delay_seconds=0.1, mix=0.3),
        Reverb(room_size=0.5, mix=0.2)
    ])
    
    # Benchmark processing
    start_time = time.time()
    processed = board(test_audio, sample_rate)
    end_time = time.time()
    
    processing_time = end_time - start_time
    real_time_factor = duration / processing_time
    
    print(f"Processed {duration}s of audio in {processing_time:.3f}s")
    print(f"Real-time factor: {real_time_factor:.1f}x")
    
    # For real-time applications, factor should be > 1
    assert real_time_factor > 1, "Processing too slow for real-time"
    
    return real_time_factor

if __name__ == "__main__":
    benchmark_pedalboard_performance()

import unittest.mock as mock
from pedalboard import load_plugin
import pytest

def test_vst_plugin_loading_error_handling():
    """Test handling of VST plugin loading errors"""
    
    # Test with non-existent plugin
    with pytest.raises(Exception) as exc_info:
        load_plugin("/non/existent/plugin.vst3")
    
    assert "not found" in str(exc_info.value).lower() or "no such file" in str(exc_info.value).lower()
    
    # Test with invalid plugin format
    with mock.patch('os.path.exists') as mock_exists:
        mock_exists.return_value = True
        
        with pytest.raises(Exception) as exc_info:
            load_plugin("/fake/invalid.txt")
        
        assert "invalid" in str(exc_info.value).lower() or "format" in str(exc_info.value).lower()

from fastapi import FastAPI, UploadFile, File, WebSocket
from fastapi.responses import StreamingResponse
from pedalboard import Pedalboard, Compressor, Reverb, Gain
from pedalboard.io import AudioFile
import tempfile
import asyncio
import numpy as np
import io

app = FastAPI(title="Pedalboard Audio API")

# Global pedalboard for consistent processing
global_board = Pedalboard([
    Compressor(threshold_db=-20, ratio=4),
    Gain(gain_db=3),
    Reverb(room_size=0.3, mix=0.2)
])

@app.post("/process-audio/")
async def process_audio_file(file: UploadFile = File(...)):
    """Process uploaded audio file with effects"""
    
    with tempfile.NamedTemporaryFile(suffix=".wav") as temp_input:
        # Save uploaded file
        content = await file.read()
        temp_input.write(content)
        temp_input.flush()
        
        # Process with Pedalboard
        with AudioFile(temp_input.name) as f:
            audio = f.read(f.frames)
            processed = global_board(audio, f.samplerate)
        
        # Return processed audio
        with tempfile.NamedTemporaryFile(suffix=".wav") as temp_output:
            with AudioFile(temp_output.name, 'w', f.samplerate, f.num_channels) as out:
                out.write(processed)
            
            temp_output.seek(0)
            return StreamingResponse(
                io.BytesIO(temp_output.read()),
                media_type="audio/wav",
                headers={"Content-Disposition": "attachment; filename=processed.wav"}
            )

@app.websocket("/ws/realtime-effects")
async def websocket_audio_processing(websocket: WebSocket):
    """Real-time audio processing via WebSocket"""
    await websocket.accept()
    
    board = Pedalboard([Compressor(), Reverb()])
    
    try:
        while True:
            # Receive audio data (base64 encoded)
            data = await websocket.receive_json()
            audio_data = np.frombuffer(
                base64.b64decode(data['audio']), 
                dtype=np.float32
            ).reshape(1, -1)
            
            # Process audio
            processed = board(audio_data, data['sample_rate'], reset=False)
            
            # Send back processed audio
            await websocket.send_json({
                'processed_audio': base64.b64encode(processed.tobytes()).decode(),
                'sample_rate': data['sample_rate']
            })
    except Exception as e:
        await websocket.close(code=1000)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

from flask import Flask, request, send_file, jsonify
from pedalboard import Pedalboard, Distortion, Chorus, Delay
from pedalboard.io import AudioFile
import tempfile
import os

app = Flask(__name__)

# Effect presets
PRESETS = {
    'guitar': Pedalboard([Distortion(drive_db=10), Chorus(), Delay()]),
    'vocal': Pedalboard([Compressor(), Reverb(room_size=0.4)]),
    'master': Pedalboard([Compressor(), Limiter()])
}

@app.route('/effects/<preset>', methods=['POST'])
def apply_preset(preset):
    """Apply effect preset to uploaded audio"""
    if preset not in PRESETS:
        return jsonify({'error': 'Unknown preset'}), 400
    
    if 'audio' not in request.files:
        return jsonify({'error': 'No audio file provided'}), 400
    
    audio_file = request.files['audio']
    
    with tempfile.NamedTemporaryFile(suffix='.wav') as temp_input, \
         tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_output:
        
        # Save uploaded file
        audio_file.save(temp_input.name)
        
        # Process with preset
        with AudioFile(temp_input.name) as f:
            audio = f.read(f.frames)
            processed = PRESETS[preset](audio, f.samplerate)
        
        # Save processed audio
        with AudioFile(temp_output.name, 'w', f.samplerate, f.num_channels) as out:
            out.write(processed)
        
        return send_file(temp_output.name, as_attachment=True, 
                        download_name=f'{preset}_processed.wav')

if __name__ == '__main__':
    app.run(debug=True)

import tensorflow as tf
import numpy as np
from pedalboard import Pedalboard, Gain, PitchShift, Reverb
import random

class AudioAugmentation:
    """TensorFlow-compatible audio augmentation using Pedalboard"""
    
    def __init__(self, sample_rate=44100):
        self.sample_rate = sample_rate
        self.augmentation_boards = [
            Pedalboard([Gain(gain_db=random.uniform(-3, 3))]),
            Pedalboard([PitchShift(semitones=random.uniform(-2, 2))]),
            Pedalboard([Reverb(room_size=random.uniform(0.1, 0.5))]),
        ]
    
    @tf.function
    def augment_audio(self, audio_tensor):
        """Apply random augmentation to audio tensor"""
        # Convert to numpy for Pedalboard processing
        audio_np = audio_tensor.numpy()
        
        # Randomly select augmentation
        board_idx = random.randint(0, len(self.augmentation_boards) - 1)
        board = self.augmentation_boards[board_idx]
        
        # Apply augmentation
        augmented = board(audio_np.reshape(1, -1), self.sample_rate)
        
        return tf.convert_to_tensor(augmented.flatten(), dtype=tf.float32)

# Usage in tf.data pipeline
def create_augmented_dataset(audio_files, labels):
    """Create augmented audio dataset"""
    augmenter = AudioAugmentation()
    
    dataset = tf.data.Dataset.from_tensor_slices((audio_files, labels))
    
    def load_and_augment(file_path, label):
        # Load audio file
        audio = tf.py_function(
            lambda path: load_audio_file(path.numpy().decode()),
            [file_path],
            tf.float32
        )
        
        # Apply augmentation
        augmented = tf.py_function(
            augmenter.augment_audio,
            [audio],
            tf.float32
        )
        
        return augmented, label
    
    return dataset.map(load_and_augment)

def load_audio_file(file_path):
    """Load audio file using Pedalboard"""
    from pedalboard.io import AudioFile
    
    with AudioFile(file_path) as f:
        audio = f.read(f.frames)
        return audio.flatten().astype(np.float32)

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from pedalboard import Pedalboard, Compressor, EQ
import numpy as np

class PedalboardAudioDataset(Dataset):
    """PyTorch Dataset with Pedalboard augmentation"""
    
    def __init__(self, audio_files, labels, sample_rate=44100, augment=True):
        self.audio_files = audio_files
        self.labels = labels
        self.sample_rate = sample_rate
        self.augment = augment
        
        if augment:
            self.augmentation_board = Pedalboard([
                Compressor(threshold_db=-20, ratio=2),
                # Add more effects as needed
            ])
    
    def __len__(self):
        return len(self.audio_files)
    
    def __getitem__(self, idx):
        # Load audio
        from pedalboard.io import AudioFile
        
        with AudioFile(self.audio_files[idx]) as f:
            audio = f.read(f.frames)
        
        # Apply augmentation if enabled
        if self.augment and np.random.random() > 0.5:
            audio = self.augmentation_board(audio, self.sample_rate)
        
        # Convert to PyTorch tensor
        audio_tensor = torch.from_numpy(audio.flatten()).float()
        label_tensor = torch.tensor(self.labels[idx], dtype=torch.long)
        
        return audio_tensor, label_tensor

# Usage
train_dataset = PedalboardAudioDataset(train_files, train_labels, augment=True)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# Dockerfile for Pedalboard application
FROM python:3.11-slim

# Install system dependencies for audio processing
RUN apt-get update && apt-get install -y \
    ffmpeg \
    libsndfile1 \
    && rm -rf /var/lib/apt/lists/*

# Set working directory
WORKDIR /app

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose port
EXPOSE 8000

# Run application
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: pedalboard-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: pedalboard-api
  template:
    metadata:
      labels:
        app: pedalboard-api
    spec:
      containers:
      - name: pedalboard-api
        image: your-registry/pedalboard-api:latest
        ports:
        - containerPort: 8000
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        env:
        - name: WORKERS
          value: "4"
---
apiVersion: v1
kind: Service
metadata:
  name: pedalboard-service
spec:
  selector:
    app: pedalboard-api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer

import time
import psutil
import logging
from functools import wraps
from pedalboard import Pedalboard

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def monitor_performance(func):
    """Decorator to monitor audio processing performance"""
    @wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss / 1024 / 1024  # MB
        
        try:
            result = func(*args, **kwargs)
            
            end_time = time.time()
            end_memory = psutil.Process().memory_info().rss / 1024 / 1024  # MB
            
            processing_time = end_time - start_time
            memory_delta = end_memory - start_memory
            
            logger.info(f"{func.__name__} - Time: {processing_time:.3f}s, Memory: {memory_delta:.1f}MB")
            
            return result
            
        except Exception as e:
            logger.error(f"{func.__name__} failed: {str(e)}")
            raise
    
    return wrapper

@monitor_performance
def process_audio_with_monitoring(audio, sample_rate, effects_chain):
    """Process audio with performance monitoring"""
    board = Pedalboard(effects_chain)
    return board(audio, sample_rate)

import time
import numpy as np
from pedalboard import Pedalboard, Compressor, Reverb, Delay
import psutil

def comprehensive_benchmark():
    """Comprehensive performance benchmark"""
    sample_rates = [22050, 44100, 48000, 96000]
    durations = [1, 5, 10, 30]  # seconds
    
    # Complex effect chain
    board = Pedalboard([
        Compressor(threshold_db=-20, ratio=4, attack_ms=5, release_ms=100),
        Delay(delay_seconds=0.125, feedback=0.3, mix=0.25),
        Reverb(room_size=0.5, damping=0.3, wet_level=0.3)
    ])
    
    results = []
    
    for sr in sample_rates:
        for duration in durations:
            num_samples = int(sr * duration)
            test_audio = np.random.randn(2, num_samples).astype(np.float32) * 0.1
            
            # Memory before
            process = psutil.Process()
            mem_before = process.memory_info().rss / 1024 / 1024  # MB
            
            # Benchmark processing
            start_time = time.time()
            processed = board(test_audio, sr)
            end_time = time.time()
            
            # Memory after
            mem_after = process.memory_info().rss / 1024 / 1024  # MB
            
            processing_time = end_time - start_time
            real_time_factor = duration / processing_time
            memory_delta = mem_after - mem_before
            
            results.append({
                'sample_rate': sr,
                'duration': duration,
                'processing_time': processing_time,
                'real_time_factor': real_time_factor,
                'memory_delta': memory_delta
            })
            
            print(f"SR: {sr}Hz, Duration: {duration}s, "
                  f"Processing: {processing_time:.3f}s, "
                  f"RT Factor: {real_time_factor:.1f}x, "
                  f"Memory: +{memory_delta:.1f}MB")
    
    return results

# Run benchmark
if __name__ == "__main__":
    benchmark_results = comprehensive_benchmark()

from pedalboard import Pedalboard, Compressor, NoiseGate, EQ, Limiter
from pedalboard.io import AudioFile
import os

class PodcastProcessor:
    """Professional podcast processing chain"""
    
    def __init__(self):
        # Voice processing chain
        self.voice_chain = Pedalboard([
            NoiseGate(threshold_db=-40, ratio=10, release_ms=250),
            Compressor(threshold_db=-18, ratio=3, attack_ms=3, release_ms=100),
            # EQ for voice clarity
            HighpassFilter(cutoff_hz=80),  # Remove rumble
            # Presence boost around 2-5kHz would go here
            Limiter(threshold_db=-1, release_ms=50)
        ])
        
        # Music/intro processing
        self.music_chain = Pedalboard([
            Compressor(threshold_db=-12, ratio=2),
            Limiter(threshold_db=-0.1)
        ])
    
    def process_episode(self, voice_file, music_file, output_file):
        """Process complete podcast episode"""
        
        # Process voice
        with AudioFile(voice_file) as f:
            voice_audio = f.read(f.frames)
            processed_voice = self.voice_chain(voice_audio, f.samplerate)
        
        # Process music/intro
        with AudioFile(music_file) as f:
            music_audio = f.read(f.frames)
            processed_music = self.music_chain(music_audio, f.samplerate)
        
        # Mix and save (simplified - real mixing would be more complex)
        # This is a basic example - professional mixing requires more steps
        mixed_audio = processed_voice + (processed_music * 0.3)  # Music at 30%
        
        with AudioFile(output_file, 'w', f.samplerate, f.num_channels) as out:
            out.write(mixed_audio)
        
        print(f"Podcast episode processed: {output_file}")

# Usage
processor = PodcastProcessor()
processor.process_episode('voice.wav', 'intro_music.wav', 'final_episode.wav')

from pedalboard import Pedalboard, Compressor, Distortion, Chorus, Delay, Reverb
from pedalboard.io import AudioStream
import threading
import time

class LivePerformanceRig:
    """Live audio processing rig with preset switching"""
    
    def __init__(self):
        self.presets = {
            'clean': Pedalboard([
                Compressor(threshold_db=-20, ratio=2),
                Chorus(rate_hz=0.5, depth=0.2, mix=0.3),
                Reverb(room_size=0.3, mix=0.15)
            ]),
            'crunch': Pedalboard([
                Compressor(threshold_db=-15, ratio=3),
                Distortion(drive_db=8),
                Delay(delay_seconds=0.125, mix=0.2),
                Reverb(room_size=0.4, mix=0.1)
            ]),
            'lead': Pedalboard([
                Compressor(threshold_db=-12, ratio=4),
                Distortion(drive_db=15),
                Delay(delay_seconds=0.25, feedback=0.4, mix=0.3),
                Reverb(room_size=0.5, mix=0.25)
            ])
        }
        
        self.current_preset = 'clean'
        self.stream = None
    
    def start_performance(self, input_device="Audio Interface", output_device="Speakers"):
        """Start live audio processing"""
        self.stream = AudioStream(
            input_device_name=input_device,
            output_device_name=output_device,
            sample_rate=44100,
            buffer_size=128  # Low latency
        )
        
        self.stream.plugins = self.presets[self.current_preset]
        self.stream.__enter__()
        
        print(f"Live rig started with '{self.current_preset}' preset")
        print("Use switch_preset() to change sounds")
    
    def switch_preset(self, preset_name):
        """Switch to different preset during performance"""
        if preset_name in self.presets and self.stream:
            self.current_preset = preset_name
            self.stream.plugins = self.presets[preset_name]
            print(f"Switched to '{preset_name}' preset")
    
    def stop_performance(self):
        """Stop live processing"""
        if self.stream:
            self.stream.__exit__(None, None, None)
            print("Live rig stopped")

# Usage example
rig = LivePerformanceRig()
rig.start_performance()

# Simulate preset changes during performance
time.sleep(5)
rig.switch_preset('crunch')
time.sleep(5)
rig.switch_preset('lead')
time.sleep(5)
rig.stop_performance()

from pedalboard import Pedalboard, Compressor, EQ, Limiter, Reverb
from pedalboard.io import AudioFile
import numpy as np

class MasteringProcessor:
    """Professional mastering chain"""
    
    def __init__(self):
        # Multi-stage mastering chain
        self.mastering_chain = Pedalboard([
            # Stage 1: Gentle compression
            Compressor(
                threshold_db=-24, 
                ratio=1.5, 
                attack_ms=30, 
                release_ms=100
            ),
            
            # Stage 2: EQ adjustments (simplified)
            HighpassFilter(cutoff_hz=30),  # Remove sub-bass
            
            # Stage 3: Main compressor
            Compressor(
                threshold_db=-12, 
                ratio=2.5, 
                attack_ms=10, 
                release_ms=50
            ),
            
            # Stage 4: Final limiting
            Limiter(
                threshold_db=-0.3, 
                release_ms=30
            )
        ])
    
    def master_track(self, input_file, output_file, target_lufs=-14):
        """Master audio track to broadcast standards"""
        
        with AudioFile(input_file) as f:
            audio = f.read(f.frames)
            
            # Apply mastering chain
            mastered = self.mastering_chain(audio, f.samplerate)
            
            # Analyze loudness (simplified)
            rms_level = np.sqrt(np.mean(mastered**2))
            peak_level = np.max(np.abs(mastered))
            
            print(f"Mastering complete:")
            print(f"  RMS Level: {20 * np.log10(rms_level):.1f} dB")
            print(f"  Peak Level: {20 * np.log10(peak_level):.1f} dB")
            print(f"  Peak to RMS: {20 * np.log10(peak_level/rms_level):.1f} dB")
            
            # Save mastered audio
            with AudioFile(output_file, 'w', f.samplerate, f.num_channels) as out:
                out.write(mastered)
    
    def batch_master(self, input_dir, output_dir):
        """Master entire album/EP"""
        import os
        from pathlib import Path
        
        input_path = Path(input_dir)
        output_path = Path(output_dir)
        output_path.mkdir(exist_ok=True)
        
        audio_files = list(input_path.glob('*.wav')) + list(input_path.glob('*.flac'))
        
        for audio_file in audio_files:
            output_file = output_path / f"{audio_file.stem}_mastered{audio_file.suffix}"
            print(f"Mastering: {audio_file.name}")
            self.master_track(str(audio_file), str(output_file))

# Usage
mastering = MasteringProcessor()
mastering.master_track('raw_mix.wav', 'mastered_track.wav')
mastering.batch_master('./raw_mixes', './mastered_tracks')

from pedalboard import Pedalboard, Compressor, EQ
from pedalboard.io import AudioFile
import librosa
import numpy as np

class AudioAnalysisProcessor:
    """Combine Pedalboard processing with audio analysis"""
    
    def __init__(self):
        self.processing_chain = Pedalboard([
            Compressor(threshold_db=-20, ratio=3),
            HighpassFilter(cutoff_hz=80)
        ])
    
    def analyze_and_process(self, input_file):
        """Analyze audio characteristics and apply appropriate processing"""
        
        # Load and analyze original audio
        with AudioFile(input_file) as f:
            audio = f.read(f.frames)
            sr = f.samplerate
        
        # Convert to mono for analysis
        mono_audio = np.mean(audio, axis=0) if audio.shape[0] > 1 else audio[0]
        
        # Extract features using librosa
        tempo, beats = librosa.beat.beat_track(y=mono_audio, sr=sr)
        spectral_centroid = librosa.feature.spectral_centroid(y=mono_audio, sr=sr)[0]
        zero_crossing_rate = librosa.feature.zero_crossing_rate(mono_audio)[0]
        
        # Analyze characteristics
        avg_centroid = np.mean(spectral_centroid)
        avg_zcr = np.mean(zero_crossing_rate)
        
        print(f"Audio Analysis:")
        print(f"  Tempo: {tempo:.1f} BPM")
        print(f"  Spectral Centroid: {avg_centroid:.0f} Hz")
        print(f"  Zero Crossing Rate: {avg_zcr:.4f}")
        
        # Adapt processing based on analysis
        if avg_centroid > 3000:  # Bright/harsh audio
            print("  -> Applying gentle high-frequency reduction")
            self.processing_chain.append(LowpassFilter(cutoff_hz=8000))
        
        if avg_zcr > 0.1:  # Very dynamic/noisy audio
            print("  -> Applying stronger compression")
            self.processing_chain[0].ratio = 4  # Increase compression ratio
        
        # Apply processing
        processed = self.processing_chain(audio, sr)
        
        return processed, {
            'tempo': tempo,
            'spectral_centroid': avg_centroid,
            'zero_crossing_rate': avg_zcr
        }

# Usage
analyzer = AudioAnalysisProcessor()
processed_audio, features = analyzer.analyze_and_process('input.wav')

# Check plugin path and format
try:
    plugin = load_plugin("/full/path/to/plugin.vst3")
except Exception as e:
    print(f"Plugin error: {e}")
    # Try different path or plugin format

# Check supported formats
supported_formats = ['.wav', '.flac', '.mp3', '.ogg', '.aiff']
if file_path.suffix.lower() not in supported_formats:
    print(f"Unsupported format: {file_path.suffix}")

# Process in chunks instead of loading entire file
chunk_size = sample_rate * 10  # 10 seconds
with AudioFile('large_file.wav') as f:
    with AudioFile('output.wav', 'w', f.samplerate, f.num_channels) as out:
        while f.tell() < f.frames:
            chunk = f.read(min(chunk_size, f.frames - f.tell()))
            processed = board(chunk, f.samplerate, reset=False)
            out.write(processed)