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AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.

Skill metadata


Source	Optional — install with `mibyan skills install official/creative/audiocraft-audio-generation`
Path	`optional-skills/creative/audiocraft-audio-generation`
Version	`1.0.0`
Author	Orchestra Research
License	MIT
Dependencies	`audiocraft`, `torch>=2.0.0`, `transformers>=4.30.0`
Platforms	linux, macos
Tags	`Multimodal`, `Audio Generation`, `Text-to-Music`, `Text-to-Audio`, `MusicGen`
Related skills	`heartmula`, `songwriting-and-ai-music`

Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

AudioCraft: Audio Generation

Guide to using Meta’s AudioCraft for text-to-music and text-to-audio generation with MusicGen, AudioGen, and EnCodec.

When to use AudioCraft

Use AudioCraft when:

Need to generate music from text descriptions
Creating sound effects and environmental audio
Building music generation applications
Need melody-conditioned music generation
Want stereo audio output
Require controllable music generation with style transfer

Key features:

MusicGen: Text-to-music generation with melody conditioning
AudioGen: Text-to-sound effects generation
EnCodec: High-fidelity neural audio codec
Multiple model sizes: Small (300M) to Large (3.3B)
Stereo support: Full stereo audio generation
Style conditioning: MusicGen-Style for reference-based generation

Use alternatives instead:

Stable Audio: For longer commercial music generation
Bark: For text-to-speech with music/sound effects
Riffusion: For spectogram-based music generation
OpenAI Jukebox: For raw audio generation with lyrics

Quick start

Installation

# From PyPI
pip install audiocraft

# From GitHub (latest)
pip install git+https://github.com/facebookresearch/audiocraft.git

# Or use HuggingFace Transformers
pip install transformers torch torchaudio

Basic text-to-music (AudioCraft)

import torchaudio
from audiocraft.models import MusicGen

# Load model
model = MusicGen.get_pretrained('facebook/musicgen-small')

# Set generation parameters
model.set_generation_params(
    duration=8,  # seconds
    top_k=250,
    temperature=1.0
)

# Generate from text
descriptions = ["happy upbeat electronic dance music with synths"]
wav = model.generate(descriptions)

# Save audio
torchaudio.save("output.wav", wav[0].cpu(), sample_rate=32000)

Using HuggingFace Transformers

from transformers import AutoProcessor, MusicgenForConditionalGeneration
import scipy

# Load model and processor
processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
model.to("cuda")

# Generate music
inputs = processor(
    text=["80s pop track with bassy drums and synth"],
    padding=True,
    return_tensors="pt"
).to("cuda")

audio_values = model.generate(
    **inputs,
    do_sample=True,
    guidance_scale=3,
    max_new_tokens=256
)

# Save
sampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write("output.wav", rate=sampling_rate, data=audio_values[0, 0].cpu().numpy())

Text-to-sound with AudioGen

from audiocraft.models import AudioGen

# Load AudioGen
model = AudioGen.get_pretrained('facebook/audiogen-medium')

model.set_generation_params(duration=5)

# Generate sound effects
descriptions = ["dog barking in a park with birds chirping"]
wav = model.generate(descriptions)

torchaudio.save("sound.wav", wav[0].cpu(), sample_rate=16000)

Core concepts

Architecture overview

AudioCraft Architecture:
┌──────────────────────────────────────────────────────────────┐
│                    Text Encoder (T5)                          │
│                         │                                     │
│                    Text Embeddings                            │
└────────────────────────┬─────────────────────────────────────┘
                         │
┌────────────────────────▼─────────────────────────────────────┐
│              Transformer Decoder (LM)                         │
│     Auto-regressively generates audio tokens                  │
│     Using efficient token interleaving patterns               │
└────────────────────────┬─────────────────────────────────────┘
                         │
┌────────────────────────▼─────────────────────────────────────┐
│                EnCodec Audio Decoder                          │
│        Converts tokens back to audio waveform                 │
└──────────────────────────────────────────────────────────────┘

Model variants

Model	Size	Description	Use Case
`musicgen-small`	300M	Text-to-music	Quick generation
`musicgen-medium`	1.5B	Text-to-music	Balanced
`musicgen-large`	3.3B	Text-to-music	Best quality
`musicgen-melody`	1.5B	Text + melody	Melody conditioning
`musicgen-melody-large`	3.3B	Text + melody	Best melody
`musicgen-stereo-*`	Varies	Stereo output	Stereo generation
`musicgen-style`	1.5B	Style transfer	Reference-based
`audiogen-medium`	1.5B	Text-to-sound	Sound effects

Generation parameters

Parameter	Default	Description
`duration`	8.0	Length in seconds (1-120)
`top_k`	250	Top-k sampling
`top_p`	0.0	Nucleus sampling (0 = disabled)
`temperature`	1.0	Sampling temperature
`cfg_coef`	3.0	Classifier-free guidance

MusicGen usage

Text-to-music generation

from audiocraft.models import MusicGen
import torchaudio

model = MusicGen.get_pretrained('facebook/musicgen-medium')

# Configure generation
model.set_generation_params(
    duration=30,          # Up to 30 seconds
    top_k=250,            # Sampling diversity
    top_p=0.0,            # 0 = use top_k only
    temperature=1.0,      # Creativity (higher = more varied)
    cfg_coef=3.0          # Text adherence (higher = stricter)
)

# Generate multiple samples
descriptions = [
    "epic orchestral soundtrack with strings and brass",
    "chill lo-fi hip hop beat with jazzy piano",
    "energetic rock song with electric guitar"
]

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

# Save each
for i, audio in enumerate(wav):
    torchaudio.save(f"music_{i}.wav", audio.cpu(), sample_rate=32000)

Melody-conditioned generation

from audiocraft.models import MusicGen
import torchaudio

# Load melody model
model = MusicGen.get_pretrained('facebook/musicgen-melody')
model.set_generation_params(duration=30)

# Load melody audio
melody, sr = torchaudio.load("melody.wav")

# Generate with melody conditioning
descriptions = ["acoustic guitar folk song"]
wav = model.generate_with_chroma(descriptions, melody, sr)

torchaudio.save("melody_conditioned.wav", wav[0].cpu(), sample_rate=32000)

Stereo generation

from audiocraft.models import MusicGen

# Load stereo model
model = MusicGen.get_pretrained('facebook/musicgen-stereo-medium')
model.set_generation_params(duration=15)

descriptions = ["ambient electronic music with wide stereo panning"]
wav = model.generate(descriptions)

# wav shape: [batch, 2, samples] for stereo
print(f"Stereo shape: {wav.shape}")  # [1, 2, 480000]
torchaudio.save("stereo.wav", wav[0].cpu(), sample_rate=32000)

Audio continuation

from transformers import AutoProcessor, MusicgenForConditionalGeneration

processor = AutoProcessor.from_pretrained("facebook/musicgen-medium")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-medium")

# Load audio to continue
import torchaudio
audio, sr = torchaudio.load("intro.wav")

# Process with text and audio
inputs = processor(
    audio=audio.squeeze().numpy(),
    sampling_rate=sr,
    text=["continue with a epic chorus"],
    padding=True,
    return_tensors="pt"
)

# Generate continuation
audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=512)

MusicGen-Style usage

Style-conditioned generation

from audiocraft.models import MusicGen

# Load style model
model = MusicGen.get_pretrained('facebook/musicgen-style')

# Configure generation with style
model.set_generation_params(
    duration=30,
    cfg_coef=3.0,
    cfg_coef_beta=5.0  # Style influence
)

# Configure style conditioner
model.set_style_conditioner_params(
    eval_q=3,          # RVQ quantizers (1-6)
    excerpt_length=3.0  # Style excerpt length
)

# Load style reference
style_audio, sr = torchaudio.load("reference_style.wav")

# Generate with text + style
descriptions = ["upbeat dance track"]
wav = model.generate_with_style(descriptions, style_audio, sr)

Style-only generation (no text)

# Generate matching style without text prompt
model.set_generation_params(
    duration=30,
    cfg_coef=3.0,
    cfg_coef_beta=None  # Disable double CFG for style-only
)

wav = model.generate_with_style([None], style_audio, sr)

AudioGen usage

Sound effect generation

from audiocraft.models import AudioGen
import torchaudio

model = AudioGen.get_pretrained('facebook/audiogen-medium')
model.set_generation_params(duration=10)

# Generate various sounds
descriptions = [
    "thunderstorm with heavy rain and lightning",
    "busy city traffic with car horns",
    "ocean waves crashing on rocks",
    "crackling campfire in forest"
]

wav = model.generate(descriptions)

for i, audio in enumerate(wav):
    torchaudio.save(f"sound_{i}.wav", audio.cpu(), sample_rate=16000)

EnCodec usage

Audio compression

from audiocraft.models import CompressionModel
import torch
import torchaudio

# Load EnCodec
model = CompressionModel.get_pretrained('facebook/encodec_32khz')

# Load audio
wav, sr = torchaudio.load("audio.wav")

# Ensure correct sample rate
if sr != 32000:
    resampler = torchaudio.transforms.Resample(sr, 32000)
    wav = resampler(wav)

# Encode to tokens
with torch.no_grad():
    encoded = model.encode(wav.unsqueeze(0))
    codes = encoded[0]  # Audio codes

# Decode back to audio
with torch.no_grad():
    decoded = model.decode(codes)

torchaudio.save("reconstructed.wav", decoded[0].cpu(), sample_rate=32000)

Common workflows

Workflow 1: Music generation pipeline

import torch
import torchaudio
from audiocraft.models import MusicGen

class MusicGenerator:
    def __init__(self, model_name="facebook/musicgen-medium"):
        self.model = MusicGen.get_pretrained(model_name)
        self.sample_rate = 32000

    def generate(self, prompt, duration=30, temperature=1.0, cfg=3.0):
        self.model.set_generation_params(
            duration=duration,
            top_k=250,
            temperature=temperature,
            cfg_coef=cfg
        )

        with torch.no_grad():
            wav = self.model.generate([prompt])

        return wav[0].cpu()

    def generate_batch(self, prompts, duration=30):
        self.model.set_generation_params(duration=duration)

        with torch.no_grad():
            wav = self.model.generate(prompts)

        return wav.cpu()

    def save(self, audio, path):
        torchaudio.save(path, audio, sample_rate=self.sample_rate)

# Usage
generator = MusicGenerator()
audio = generator.generate(
    "epic cinematic orchestral music",
    duration=30,
    temperature=1.0
)
generator.save(audio, "epic_music.wav")

Workflow 2: Sound design batch processing

import json
from pathlib import Path
from audiocraft.models import AudioGen
import torchaudio

def batch_generate_sounds(sound_specs, output_dir):
    """
    Generate multiple sounds from specifications.

    Args:
        sound_specs: list of {"name": str, "description": str, "duration": float}
        output_dir: output directory path
    """
    model = AudioGen.get_pretrained('facebook/audiogen-medium')
    output_dir = Path(output_dir)
    output_dir.mkdir(exist_ok=True)

    results = []

    for spec in sound_specs:
        model.set_generation_params(duration=spec.get("duration", 5))

        wav = model.generate([spec["description"]])

        output_path = output_dir / f"{spec['name']}.wav"
        torchaudio.save(str(output_path), wav[0].cpu(), sample_rate=16000)

        results.append({
            "name": spec["name"],
            "path": str(output_path),
            "description": spec["description"]
        })

    return results

# Usage
sounds = [
    {"name": "explosion", "description": "massive explosion with debris", "duration": 3},
    {"name": "footsteps", "description": "footsteps on wooden floor", "duration": 5},
    {"name": "door", "description": "wooden door creaking and closing", "duration": 2}
]

results = batch_generate_sounds(sounds, "sound_effects/")

Workflow 3: Gradio demo

import gradio as gr
import torch
import torchaudio
from audiocraft.models import MusicGen

model = MusicGen.get_pretrained('facebook/musicgen-small')

def generate_music(prompt, duration, temperature, cfg_coef):
    model.set_generation_params(
        duration=duration,
        temperature=temperature,
        cfg_coef=cfg_coef
    )

    with torch.no_grad():
        wav = model.generate([prompt])

    # Save to temp file
    path = "temp_output.wav"
    torchaudio.save(path, wav[0].cpu(), sample_rate=32000)
    return path

demo = gr.Interface(
    fn=generate_music,
    inputs=[
        gr.Textbox(label="Music Description", placeholder="upbeat electronic dance music"),
        gr.Slider(1, 30, value=8, label="Duration (seconds)"),
        gr.Slider(0.5, 2.0, value=1.0, label="Temperature"),
        gr.Slider(1.0, 10.0, value=3.0, label="CFG Coefficient")
    ],
    outputs=gr.Audio(label="Generated Music"),
    title="MusicGen Demo"
)

demo.launch()

Performance optimization

Memory optimization

# Use smaller model
model = MusicGen.get_pretrained('facebook/musicgen-small')

# Clear cache between generations
torch.cuda.empty_cache()

# Generate shorter durations
model.set_generation_params(duration=10)  # Instead of 30

# Use half precision
model = model.half()

Batch processing efficiency

# Process multiple prompts at once (more efficient)
descriptions = ["prompt1", "prompt2", "prompt3", "prompt4"]
wav = model.generate(descriptions)  # Single batch

# Instead of
for desc in descriptions:
    wav = model.generate([desc])  # Multiple batches (slower)

GPU memory requirements

Model	FP32 VRAM	FP16 VRAM
musicgen-small	~4GB	~2GB
musicgen-medium	~8GB	~4GB
musicgen-large	~16GB	~8GB

Common issues

Issue	Solution
CUDA OOM	Use smaller model, reduce duration
Poor quality	Increase cfg_coef, better prompts
Generation too short	Check max duration setting
Audio artifacts	Try different temperature
Stereo not working	Use stereo model variant

References

Advanced Usage - Training, fine-tuning, deployment
Troubleshooting - Common issues and solutions

Resources

GitHub: https://github.com/facebookresearch/audiocraft
Paper (MusicGen): https://arxiv.org/abs/2306.05284
Paper (AudioGen): https://arxiv.org/abs/2209.15352
HuggingFace: https://huggingface.co/facebook/musicgen-small
Demo: https://huggingface.co/spaces/facebook/MusicGen

​Skill metadata

​Reference: full SKILL.md

​AudioCraft: Audio Generation

​When to use AudioCraft

​Quick start

​Installation

​Basic text-to-music (AudioCraft)

​Using HuggingFace Transformers

​Text-to-sound with AudioGen

​Core concepts

​Architecture overview

​Model variants

​Generation parameters

​MusicGen usage

​Text-to-music generation

​Melody-conditioned generation

​Stereo generation

​Audio continuation

​MusicGen-Style usage

​Style-conditioned generation

​Style-only generation (no text)

​AudioGen usage

​Sound effect generation

​EnCodec usage

​Audio compression

​Common workflows

​Workflow 1: Music generation pipeline

​Workflow 2: Sound design batch processing

​Workflow 3: Gradio demo

​Performance optimization

​Memory optimization

​Batch processing efficiency

​GPU memory requirements

​Common issues

​References

​Resources

Skill metadata

Reference: full SKILL.md

AudioCraft: Audio Generation

When to use AudioCraft

Quick start

Installation

Basic text-to-music (AudioCraft)

Using HuggingFace Transformers

Text-to-sound with AudioGen

Core concepts

Architecture overview

Model variants

Generation parameters

MusicGen usage

Text-to-music generation

Melody-conditioned generation

Stereo generation

Audio continuation

MusicGen-Style usage

Style-conditioned generation

Style-only generation (no text)

AudioGen usage

Sound effect generation

EnCodec usage

Audio compression

Common workflows

Workflow 1: Music generation pipeline

Workflow 2: Sound design batch processing

Workflow 3: Gradio demo

Performance optimization

Memory optimization

Batch processing efficiency

GPU memory requirements

Common issues

References

Resources