AudioCraft Music

Generate music and audio with Meta's AudioCraft (MusicGen).

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is AudioCraft?

AudioCraft includes:

• MusicGen - Text-to-music generation

• AudioGen - Sound effects generation

• EnCodec - Audio compression

• MAGNeT - Faster generation

Model Sizes

Model	VRAM	Quality	Speed
small	4GB	Good	Fast
medium	8GB	Great	Medium
large	16GB	Best	Slow
melody	8GB	Great + melody	Medium

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

pip install audiocraft gradio scipy && \
python -c "
import gradio as gr
from audiocraft.models import MusicGen
import scipy.io.wavfile as wav
import tempfile

model = MusicGen.get_pretrained('facebook/musicgen-medium')
model.set_generation_params(duration=10)

def generate(prompt, duration):
    model.set_generation_params(duration=duration)
    output = model.generate([prompt])
    audio = output[0].cpu().numpy().T
    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
        wav.write(f.name, 32000, audio)
        return f.name

demo = gr.Interface(
    fn=generate,
    inputs=[gr.Textbox(label='Prompt'), gr.Slider(5, 30, value=10, label='Duration (s)')],
    outputs=gr.Audio(label='Generated Music'),
    title='MusicGen'
)
demo.launch(server_name='0.0.0.0', server_port=7860)
"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install audiocraft
pip install scipy torchaudio

MusicGen: Text-to-Music

Basic Generation

from audiocraft.models import MusicGen
import torchaudio

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

# Generate
prompt = "upbeat electronic dance music with heavy bass"
output = model.generate([prompt])

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

Multiple Prompts

prompts = [
    "relaxing piano jazz",
    "epic orchestral cinematic",
    "acoustic guitar folk song",
    "aggressive heavy metal"
]

outputs = model.generate(prompts)

for i, output in enumerate(outputs):
    torchaudio.save(f"music_{i}.wav", output.cpu(), sample_rate=32000)

Melody Conditioning

Use a melody as reference:

from audiocraft.models import MusicGen
import torchaudio

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

# Load reference melody
melody, sr = torchaudio.load("reference.wav")
melody = melody.unsqueeze(0).cuda()

# Generate with melody
output = model.generate_with_chroma(
    ["jazz piano version"],
    melody,
    sr
)

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

Continuation

Continue from existing audio:

# Load audio to continue
audio, sr = torchaudio.load("start.wav")
audio = audio.unsqueeze(0).cuda()

# Continue
output = model.generate_continuation(
    audio,
    prompt_sample_rate=sr,
    descriptions=["more energetic with drums"],
    progress=True
)

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

AudioGen: Sound Effects

from audiocraft.models import AudioGen

# Load model
model = AudioGen.get_pretrained('facebook/audiogen-medium')
model.set_generation_params(duration=5)

# Generate sounds
prompts = [
    "dog barking in the distance",
    "rain on a window",
    "car engine starting",
    "crowd cheering at a concert"
]

outputs = model.generate(prompts)

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

Generation Parameters

model.set_generation_params(
    duration=30,           # Length in seconds
    top_k=250,             # Top-k sampling
    top_p=0.0,             # Nucleus sampling (0 = disabled)
    temperature=1.0,       # Randomness
    cfg_coef=3.0,          # Classifier-free guidance
    two_step_cfg=False,    # Two-step CFG
)

Parameter Effects

Parameter	Low Value	High Value
temperature	Conservative	Creative
top_k	More focused	More variety
cfg_coef	Loose interpretation	Strict to prompt

Batch Processing

from audiocraft.models import MusicGen
import torchaudio
import os

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

prompts = [
    {"name": "intro", "prompt": "mysterious ambient intro, slow build"},
    {"name": "verse", "prompt": "chill lo-fi hip hop beat"},
    {"name": "chorus", "prompt": "energetic electronic pop chorus"},
    {"name": "outro", "prompt": "calm piano fade out"},
]

output_dir = "./music_parts"
os.makedirs(output_dir, exist_ok=True)

for item in prompts:
    output = model.generate([item["prompt"]])
    torchaudio.save(
        os.path.join(output_dir, f"{item['name']}.wav"),
        output[0].cpu(),
        sample_rate=32000
    )
    print(f"Generated: {item['name']}")

Streaming Generation

from audiocraft.models import MusicGen
import torch

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

# Enable streaming
streamer = model.get_streaming_generator(
    "upbeat pop music",
    max_gen_len=256  # tokens
)

all_tokens = []
for tokens in streamer:
    all_tokens.append(tokens)
    # Process chunk...

# Decode all
audio = model.decode(torch.cat(all_tokens, dim=-1))

Stereo Generation

from audiocraft.models import MusicGen

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

output = model.generate(["cinematic orchestral score"])

# Output shape: [batch, 2, samples] for stereo

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

API Server

from fastapi import FastAPI
from fastapi.responses import FileResponse
from audiocraft.models import MusicGen
import torchaudio
import tempfile

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

@app.post("/generate")
async def generate_music(prompt: str, duration: int = 10):
    model.set_generation_params(duration=duration)
    output = model.generate([prompt])

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        torchaudio.save(f.name, output[0].cpu(), sample_rate=32000)
        return FileResponse(f.name, media_type="audio/wav")

@app.post("/generate_with_melody")
async def generate_with_melody(prompt: str, melody_path: str, duration: int = 15):
    melody, sr = torchaudio.load(melody_path)

    model_melody = MusicGen.get_pretrained('facebook/musicgen-melody')
    model_melody.set_generation_params(duration=duration)

    output = model_melody.generate_with_chroma([prompt], melody.unsqueeze(0).cuda(), sr)

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        torchaudio.save(f.name, output[0].cpu(), sample_rate=32000)
        return FileResponse(f.name, media_type="audio/wav")

# Run: uvicorn server:app --host 0.0.0.0 --port 8000

Prompt Engineering

Effective Prompts

# Genre + instruments + mood
"upbeat jazz with saxophone and piano, happy and energetic"

# Style reference
"lo-fi hip hop beat, chill study music, vinyl crackle"

# Cinematic
"epic orchestral trailer music, building tension, dramatic"

# Specific elements
"acoustic guitar strumming pattern, folk song, campfire vibes"

Bad Prompts

# Too vague
"nice music"  # Not specific enough

# Song lyrics
"Happy birthday to you..."  # Won't work

# Artist names
"like Beatles"  # Doesn't understand artists

Post-Processing

Combine Clips

from pydub import AudioSegment

intro = AudioSegment.from_wav("intro.wav")
verse = AudioSegment.from_wav("verse.wav")
chorus = AudioSegment.from_wav("chorus.wav")

# Crossfade
song = intro.append(verse, crossfade=1000)
song = song.append(chorus, crossfade=1000)

song.export("full_song.mp3", format="mp3")

Add Effects

from pydub import AudioSegment
from pydub.effects import normalize, compress_dynamic_range

audio = AudioSegment.from_wav("generated.wav")

# Normalize volume
audio = normalize(audio)

# Add compression
audio = compress_dynamic_range(audio)

# Fade in/out
audio = audio.fade_in(2000).fade_out(3000)

audio.export("processed.wav", format="wav")

Memory Optimization

import torch
from audiocraft.models import MusicGen

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

# Enable CPU offload
model.to('cpu')

# Generate on GPU, offload immediately
with torch.cuda.amp.autocast():
    output = model.generate(["prompt"])
    output = output.cpu()
    torch.cuda.empty_cache()

Performance

Model	GPU	30s Generation
small	RTX 3090	~10s
medium	RTX 3090	~25s
large	RTX 4090	~45s
melody	RTX 3090	~30s

Comparison

Feature	MusicGen	Stable Audio	Riffusion
Quality	Great	Great	Good
Length	30s	90s	Loop
Melody Input	Yes	No	No
Open Source	Yes	No	Yes

Troubleshooting

Out of Memory

• Use smaller model (small instead of large)

• Reduce duration

• Clear cache: torch.cuda.empty_cache()

Poor Quality

• Use more specific prompts

• Try medium or large model

• Adjust temperature (0.8-1.2)

Repetitive Output

• Increase top_k

• Lower cfg_coef

• Try different prompts

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Bark TTS - Voice generation

• RVC Voice Clone - Voice conversion

• Demucs Separation - Audio separation