### Symbolic Music Generation with MusPy

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

A Python toolkit for symbolic music generation, including dataset management, preprocessing, and model evaluation. It allows loading MIDI files, analyzing music properties, converting to piano roll representation, generating music algorithmically, and saving/evaluating results. Requires MusPy and NumPy.

```python
import muspy
import numpy as np

# Install MusPy: pip install muspy

# Load a MIDI file
# music = muspy.read('input_song.mid')

# Analyze the music
# print(f"Number of tracks: {len(music.tracks)}")
# print(f"Resolution: {music.resolution}")
# print(f"Tempo: {music.tempos[0].qpm if music.tempos else 'N/A'}")

# Convert to piano roll representation
# piano_roll = muspy.to_pianoroll_representation(music, encode_velocity=True)
# print(f"Piano roll shape: {piano_roll.shape}")  # (time_steps, 128)

# Generate random music using a simple algorithm
def generate_simple_melody(length=100, pitch_range=(60, 84)):
    notes = []
    current_time = 0

    for _ in range(length):
        pitch = np.random.randint(*pitch_range)
        duration = np.random.choice([24, 48, 96])  # Quarter, half, whole notes
        velocity = np.random.randint(60, 100)

        notes.append(muspy.Note(
            time=current_time,
            pitch=pitch,
            duration=duration,
            velocity=velocity
        ))
        current_time += duration

    return notes

# Create a new music object
# generated_music = muspy.Music(
#     resolution=24,
#     tempos=[muspy.Tempo(time=0, qpm=120)],
#     tracks=[muspy.Track(
#         name='Generated Melody',
#         program=0,
#         is_drum=False,
#         notes=generate_simple_melody()
#     )]
# )

# Save as MIDI
# muspy.write('generated_melody.mid', generated_music)

# Evaluate the generated music
# metrics = muspy.evaluate(generated_music)
# print(f"Polyphonic rate: {metrics['polyphonic_rate']:.4f}")
# print(f"Pitch entropy: {metrics['pitch_entropy']:.4f}")
# print(f"Note density: {metrics['note_density']:.4f}")

# Visualize the music
# fig, ax = muspy.show_pianoroll(generated_music, figsize=(12, 6))
# fig.savefig('piano_roll_visualization.png')

```

--------------------------------

### Text-to-Music Generation with MusicLM

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

PyTorch implementation of Google's MusicLM for generating high-fidelity music from text descriptions. It utilizes hierarchical attention networks, MuLaN for conditioning, and AudioLM for audio generation. Requires musiclm-pytorch, PyTorch, and torchaudio. Generates audio tokens from text and decodes them into waveforms.

```python
# Install musiclm-pytorch: pip install musiclm-pytorch

import torch
from musiclm_pytorch import MusicLM, MuLaN, AudioSpectrogramTransformer
from musiclm_pytorch import AudioLM
from musiclm_pytorch.audio_decoder import AudioDecoder
import torchaudio

# Configure the model architecture
mulan = MuLaN(
    audio_transformer=AudioSpectrogramTransformer(
        dim=512,
        depth=6,
        heads=8,
        dim_head=64,
        spec_n_fft=128,
        spec_win_length=24,
        spec_aug_stretch_factor=0.8
    ),
    text_transformer=dict(
        dim=512,
        depth=6,
        heads=8,
        dim_head=64
    )
)

# Initialize AudioLM for audio generation
audio_lm = AudioLM(
    codebook_size=1024,
    num_tokens=20000,
    dim=512,
    depth=12,
    heads=8
)

# Create MusicLM model
model = MusicLM(
    audio_lm=audio_lm,
    mulan=mulan,
    condition_on_text=True
)

# Generate music from text
text_description = "ambient electronic music with slow tempo and ethereal synths"
batch_size = 1
sequence_length = 2048

# Encode text to conditioning vector
text_tokens = torch.randint(0, 20000, (batch_size, 256))  # Tokenized text
conditioning = mulan.get_text_latents(text_tokens)

# Generate audio tokens
generated_audio_tokens = model.generate(
    conditioning=conditioning,
    max_length=sequence_length,
    temperature=1.0,
    filter_thres=0.9
)

# Decode tokens to audio waveform (using pre-trained decoder)
decoder = AudioDecoder(
    codebook_size=1024,
    sample_rate=24000
)

audio_waveform = decoder.decode(generated_audio_tokens)

# Save audio
torchaudio.save('musiclm_generated.wav', audio_waveform.cpu(), 24000)

```

--------------------------------

### Generate Music with Audiocraft MusicGen

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Generates music using Meta's MusicGen model, supporting text and melodic conditioning. Requires Audiocraft. Outputs audio files.

```python
# Install Audiocraft
pip install audiocraft

from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write

# Load the pre-trained model
model = MusicGen.get_pretrained('facebook/musicgen-medium')

# Set generation parameters
model.set_generation_params(
    duration=30,  # 30 seconds
    temperature=1.0,
    top_k=250,
    top_p=0.0,
    cfg_coef=3.0
)

# Generate music from text description
descriptions = [
    'upbeat electronic dance music with heavy bass',
    'calm piano melody with ambient background',
    'rock music with electric guitar and drums'
]

# Generate audio
wav = model.generate(descriptions)  # Returns tensor [batch, channels, samples]

# Save generated audio files
for idx, one_wav in enumerate(wav):
    audio_write(
        f'generated_music_{idx}',
        one_wav.cpu(),
        model.sample_rate,
        strategy="loudness",
        loudness_compressor=True
    )

# Generate with melodic conditioning
import torchaudio

melody, sr = torchaudio.load('reference_melody.wav')
melody = melody.unsqueeze(0)  # Add batch dimension

wav_with_melody = model.generate_with_chroma(
    descriptions=['energetic pop song'],
    melody_wavs=melody,
    melody_sample_rate=sr,
    progress=True
)

audio_write('conditioned_music', wav_with_melody[0].cpu(), model.sample_rate)
```

--------------------------------

### Generate Spectrogram with Riffusion

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Adapts Stable Diffusion for music generation by creating spectrogram images from text prompts. Requires PyTorch, Diffusers, and Transformers. Outputs image files.

```python
# Install dependencies
pip install torch diffusers transformers accelerate

from diffusers import StableDiffusionPipeline
import torch
import numpy as np
from PIL import Image
import librosa
import soundfile as sf

# Load Riffusion model
pipe = StableDiffusionPipeline.from_pretrained(
    "riffusion/riffusion-model-v1",
    torch_dtype=torch.float16
).to("cuda")

# Generate spectrogram from text prompt
prompt = "smooth jazz with saxophone, chill vibes, relaxing"
negative_prompt = "drums, percussion, loud"

# Generate image (spectrogram)
spectrogram_image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=50,
    guidance_scale=7.5,
    height=512,
    width=512
).images[0]
```

--------------------------------

### MuseGAN - Multi-track Music Generation

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

A generative adversarial network architecture for generating multi-track symbolic music, focusing on temporal dynamics and inter-track dependencies. Requires PyTorch, torchvision, pretty-midi, and pypianoroll. This snippet shows the initial import of necessary libraries for building such a model.

```python
# Install required dependencies: pip install torch torchvision pretty-midi pypianoroll

import torch
import torch.nn as nn
import numpy as np
import pypianoroll

```

--------------------------------

### VampNet for Waveform Synthesis and Variation (Python)

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Demonstrates the usage of VampNet, a model for masked acoustic token modeling, to generate and modify audio waveforms. The code shows how to initialize the model, load pre-trained weights, encode audio into discrete tokens, perform masked audio inpainting for variation, and generate unconditional audio from scratch. It utilizes PyTorch and torchaudio for audio processing. The model requires specific parameters like n_codebooks, codebook_size, and transformer configuration.

```python
# Install VampNet
# pip install vampnet

import torch
from vampnet import VampNet
from vampnet.modules.transformer import Transformer
import torchaudio

# Initialize VampNet model
model = VampNet(
    n_codebooks=9,
    codebook_size=1024,
    transformer=Transformer(
        n_layers=12,
        n_heads=16,
        d_model=1024,
        mlp_ratio=4.0,
        dropout=0.1
    ),
    sample_rate=44100,
    codec_hz=50  # 50 Hz codec frame rate
)

# Load pre-trained weights (example)
# model.load_state_dict(torch.load('vampnet_pretrained.pth'))
model.eval()

# Example: Music variation - load and modify existing audio
audio, sr = torchaudio.load('input_music.wav')

# Ensure correct sample rate
if sr != 44100:
    resampler = torchaudio.transforms.Resample(sr, 44100)
    audio = resampler(audio)

# Encode audio to discrete tokens
with torch.no_grad():
    tokens = model.encode(audio.unsqueeze(0))

# Create a mask for inpainting (regenerate middle section)
mask = torch.zeros_like(tokens, dtype=torch.bool)
start_frame = 50
end_frame = 100
mask[:, :, start_frame:end_frame] = True  # Mask out middle section

# Generate variations using masked modeling
with torch.no_grad():
    varied_tokens = model.generate(
        tokens,
        mask=mask,
        temperature=1.0,
        top_p=0.95,
        num_steps=12  # Number of parallel decoding steps
    )

# Decode tokens back to audio
with torch.no_grad():
    varied_audio = model.decode(varied_tokens)

# Save the variation
torchaudio.save('vampnet_variation.wav', varied_audio.squeeze(0).cpu(), 44100)

# Example: Unconditional generation from scratch
sequence_length = 200  # 4 seconds at 50 Hz
unconditional_tokens = model.sample(
    batch_size=1,
    sequence_length=sequence_length,
    temperature=1.0,
    top_k=256
)

unconditional_audio = model.decode(unconditional_tokens)
torchaudio.save('vampnet_unconditional.wav', unconditional_audio.squeeze(0).cpu(), 44100)
```

--------------------------------

### Convert Spectrogram to Audio with Riffusion

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Converts a spectrogram image to an audio waveform using the Griffin-Lim algorithm for phase reconstruction. Requires libraries like NumPy, Librosa, and SoundFile. Takes a PIL Image as input and returns a NumPy array representing the audio signal.

```python
import numpy as np
import librosa
import soundfile as sf
from PIL import Image

def spectrogram_to_audio(spec_image, sample_rate=44100, n_fft=2048, hop_length=512):
    # Convert PIL Image to numpy array
    spec_array = np.array(spec_image)

    # Normalize and convert to power spectrogram
    spec_normalized = spec_array / 255.0

    # Apply inverse mel transform
    mel_spec = librosa.db_to_power(spec_normalized[:, :, 0] * 80 - 80)

    # Griffin-Lim algorithm for phase reconstruction
    audio = librosa.feature.inverse.mel_to_audio(
        mel_spec,
        sr=sample_rate,
        n_fft=n_fft,
        hop_length=hop_length
    )

    return audio

# Example usage:
# Assuming spectrogram_image is a PIL Image object
# spectrogram_image = Image.open('spectrogram.png') 
# audio = spectrogram_to_audio(spectrogram_image)
# sf.write('riffusion_output.wav', audio, 44100)

```

--------------------------------

### Generate Music with Magenta RNN

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Uses Magenta's Melody RNN model to generate music sequences based on a primer. Requires Magenta and TensorFlow. Outputs a MIDI file.

```python
# Install Magenta
pip install magenta

# Generate music using a pre-trained Melody RNN model
from magenta.models.melody_rnn import melody_rnn_sequence_generator
from magenta.models.melody_rnn import melody_rnn_model
import magenta.music as mm

# Initialize the generator
bundle = mm.read_bundle_file('path/to/melody_rnn.mag')
generator = melody_rnn_sequence_generator.MelodyRnnSequenceGenerator(
    model=melody_rnn_model.MelodyRnnModel(bundle.generator_config),
    details=bundle.generator_details,
    steps_per_quarter=4,
    checkpoint=None,
    bundle=bundle
)

# Create a primer sequence
primer_sequence = mm.Melody([60, 62, 64, 65, 67])  # C major scale

# Generate options
generator_options = mm.GeneratorOptions()
generator_options.args['temperature'].float_value = 1.0
generator_options.args['beam_size'].int_value = 1
generator_options.args['branch_factor'].int_value = 1
generator_options.args['steps_per_iteration'].int_value = 1
generator_options.generate_sections.add(
    start_time=0,
    end_time=32  # Generate 32 steps
)

# Generate the sequence
generated_sequence = generator.generate(primer_sequence, generator_options)

# Save as MIDI file
mm.sequence_proto_to_midi_file(generated_sequence, 'generated_music.mid')
```

--------------------------------

### Define and Use MuseGAN for Symbolic Music Generation (Python)

Source: https://context7.com/curated-awesome-lists/awesome-ai-music-generation/llms.txt

Defines the MuseGAN neural network architecture for generating symbolic music in pianoroll format. It includes a generator for temporal patterns and bar generators for individual tracks. The model is initialized, used to generate music from random latent vectors, and the output is post-processed into a MIDI file using pypianoroll. Dependencies include PyTorch and pypianoroll.

```python
import torch
import torch.nn as nn
import pypianoroll
import numpy as np

# Define MuseGAN Generator architecture
class MuseGAN(nn.Module):
    def __init__(self, z_dim=128, n_tracks=4, n_bars=2, n_steps_per_bar=16, n_pitches=84):
        super(MuseGAN, self).__init__()
        self.z_dim = z_dim
        self.n_tracks = n_tracks

        # Temporal generator
        self.temporal_generator = nn.Sequential(
            nn.Linear(z_dim, 256),
            nn.ReLU(),
            nn.Linear(256, n_bars * 64)
        )

        # Bar generator for each track
        self.bar_generators = nn.ModuleList([
            nn.Sequential(
                nn.Linear(64 + z_dim, 512),
                nn.BatchNorm1d(512),
                nn.ReLU(),
                nn.Linear(512, n_steps_per_bar * n_pitches),
                nn.Sigmoid()
            ) for _ in range(n_tracks)
        ])

    def forward(self, z_temporal, z_track):
        # Generate temporal patterns
        temporal_features = self.temporal_generator(z_temporal)
        temporal_features = temporal_features.view(-1, self.n_tracks, 64)

        # Generate tracks
        tracks = []
        for i, bar_gen in enumerate(self.bar_generators):
            track_input = torch.cat([temporal_features[:, i], z_track[:, i]], dim=1)
            track_output = bar_gen(track_input)
            tracks.append(track_output)

        return torch.stack(tracks, dim=1)

# Initialize model
model = MuseGAN(z_dim=128, n_tracks=4, n_bars=2, n_steps_per_bar=16, n_pitches=84)

# Generate music
batch_size = 1
z_temporal = torch.randn(batch_size, 128)
z_track = torch.randn(batch_size, 4, 128)

# Generate pianoroll
with torch.no_grad():
    generated = model(z_temporal, z_track)

# Post-process and convert to MIDI
pianoroll = generated[0].cpu().numpy()  # (n_tracks, n_steps * n_pitches)
pianoroll = pianoroll.reshape(4, 32, 84)  # Reshape to (tracks, time, pitch)
pianoroll = (pianoroll > 0.5).astype(np.uint8) * 100  # Binarize and set velocity

# Create pypianoroll Multitrack object
tracks = []
program_numbers = [0, 32, 48, 25]  # Piano, Bass, Strings, Guitar
track_names = ['Piano', 'Bass', 'Strings', 'Guitar']

for i in range(4):
    track = pypianoroll.Track(
        pianoroll=pianoroll[i],
        program=program_numbers[i],
        is_drum=False,
        name=track_names[i]
    )
    tracks.append(track)

multitrack = pypianoroll.Multitrack(
    tracks=tracks,
    tempo=120.0,
    beat_resolution=4
)

# Save as MIDI
pypianoroll.write('musegan_generated.mid', multitrack)
```

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