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music_gen: add segmented generation, Bark vocals, and song mixing

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- Add segmented generation with crossfading for long audio (>30s)
- Add Bark integration for speech/vocal generation (--speech flag)
- Add full song generation with vocals over instrumental (--song flag)
- Auto-select MusicGen model size based on available VRAM
- Enforce CUDA for NVIDIA GPUs (no CPU fallback)
- Update README with new features and examples
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Krzysztof kuhy Rudnicki 2025-12-04 21:26:52 +01:00
parent 9e6d07b4e7
commit 740726a3ae
2 changed files with 713 additions and 67 deletions
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75
python_pkg/music_gen/README.md
View File

@ -1,6 +1,14 @@
# MusicGen - Local AI Music Generator
# MusicGen - Local AI Music & Speech Generator
Generate music from text prompts using Meta's open-source MusicGen model.
Generate music and speech/vocals from text prompts using Meta's MusicGen and Suno's Bark.
## Features
- **Music Generation**: Create instrumental music from text descriptions (MusicGen)
- **Long Audio Support**: Generate music of any length via automatic segmentation with crossfading
- **Speech/Vocals**: Generate speech and singing with Bark (optional)
- **CUDA Optimized**: Auto-detects GPU and selects best model for your VRAM
- **No API Keys**: Runs 100% locally on your hardware
## Quick Start
@ -18,22 +26,53 @@ python music_generator.py "upbeat electronic dance music with synths"
## Usage
### Single Generation
### Music Generation (MusicGen)
```bash
# Basic usage
python music_generator.py "jazz piano with soft drums"
# Set duration (in seconds, max ~30 recommended)
python music_generator.py --duration 20 "epic orchestral soundtrack"
# Set duration (any length supported via segmentation)
python music_generator.py --duration 60 "epic orchestral soundtrack"
# Generate a full 3-minute track
python music_generator.py --duration 180 "ambient electronic music"
# Use smaller/faster model
python music_generator.py --model small "rock guitar riff"
# Use larger/better quality model (needs 16GB+ VRAM)
# Use larger/better quality model (needs 12GB+ VRAM)
python music_generator.py --model large "ambient electronic"
```
### Speech/Vocals Generation (Bark)
```bash
# First install Bark (not included in base setup)
pip install git+https://github.com/suno-ai/bark.git
# Generate speech
python music_generator.py --speech "Hello, how are you today?"
# Use different voice
python music_generator.py --speech --voice v2/en_speaker_3 "Welcome!"
# Generate singing
python music_generator.py --speech "♪ La la la, I love to sing ♪"
# With laughter and expression
python music_generator.py --speech "That's so funny! [laughter] I can't believe it."
```
**Bark special tokens:**
- `[laughter]`, `[laughs]`, `[sighs]`, `[gasps]` - expressions
- `[music]`, `[clears throat]` - sounds
- `♪` - singing
- `...` or `—` - hesitations
**Available voices:** `v2/en_speaker_0` through `v2/en_speaker_9`
### Interactive Mode
```bash
@ -47,20 +86,20 @@ In interactive mode:
- `:h` - Show example prompts
- `:q` - Quit
## Model Sizes
## Model Sizes (Auto-Selected by VRAM)
| Model | Size | VRAM | Quality | Speed |
| ------ | ------ | ----- | ------- | ------ |
| small | ~500MB | ~4GB | Good | Fast |
| medium | ~3.3GB | ~8GB | Better | Medium |
| large | ~6.5GB | ~16GB | Best | Slow |
| small | ~500MB | 3GB+ | Good | Fast |
| medium | ~3.3GB | 8GB+ | Better | Medium |
| large | ~6.5GB | 12GB+ | Best | Slow |
## Requirements
- Python 3.10+
- 8GB+ RAM (16GB recommended)
- GPU recommended (CUDA or Apple Silicon MPS)
- Works on CPU but much slower
- NVIDIA GPU with CUDA (required for NVIDIA systems)
- Apple Silicon supported via MPS
- 8GB+ VRAM recommended for best results
## Output
@ -83,7 +122,7 @@ Generated audio files are saved to `./output/` as WAV files with timestamps.
### Out of Memory
- Try `--model small` for lower VRAM usage
- Reduce duration with `--duration 5`
- Reduce duration with `--duration 10`
- Close other GPU applications
### Slow Generation
@ -96,3 +135,11 @@ Generated audio files are saved to `./output/` as WAV files with timestamps.
- Check if scipy is installed: `pip install scipy`
- Try a different audio player (VLC recommended)
### CUDA Not Available
If you see "NVIDIA GPU detected but CUDA is not available":
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu121
```

705
python_pkg/music_gen/music_generator.py
View File

@ -27,9 +27,18 @@ warnings.filterwarnings("ignore", category=UserWarning)
VRAM_THRESHOLD_LARGE = 12 # Use large model with 12GB+ VRAM
VRAM_THRESHOLD_MEDIUM = 8 # Use medium model with 8GB+ VRAM
# Generation settings for segmented long audio
SEGMENT_DURATION = 25 # Seconds per segment (under 30s MusicGen limit)
CROSSFADE_DURATION = 2 # Seconds of crossfade between segments
BARK_MAX_CHARS = 200 # Max characters per Bark segment (~13s of speech)
def check_dependencies() -> bool:
"""Check if required packages are installed."""
def check_dependencies(*, include_bark: bool = False) -> bool:
"""Check if required packages are installed.
Args:
include_bark: Whether to check for Bark dependencies as well.
"""
missing = []
try:
@ -47,12 +56,21 @@ def check_dependencies() -> bool:
except ImportError:
missing.append("scipy")
if include_bark:
try:
from bark import generate_audio as _bark_gen # noqa: F401
except ImportError:
missing.append("git+https://github.com/suno-ai/bark.git")
if missing:
print("Missing dependencies. Install with:")
print(f" pip install {' '.join(missing)}")
print("\nFor CUDA support:")
print(" pip install torch --index-url https://download.pytorch.org/whl/cu121")
print(" pip install transformers scipy")
if include_bark:
print("\nFor Bark vocals:")
print(" pip install git+https://github.com/suno-ai/bark.git")
return False
return True
@ -182,6 +200,547 @@ def load_model(
return model, processor
# Available Bark voice presets
BARK_VOICES = [
"v2/en_speaker_0",
"v2/en_speaker_1",
"v2/en_speaker_2",
"v2/en_speaker_3",
"v2/en_speaker_4",
"v2/en_speaker_5",
"v2/en_speaker_6",
"v2/en_speaker_7",
"v2/en_speaker_8",
"v2/en_speaker_9",
]
def generate_speech(
text: str,
voice: str = "v2/en_speaker_6",
output_dir: Path | None = None,
) -> Path:
"""Generate speech audio from text using Bark.
Bark supports various speech patterns:
- [laughter], [laughs], [sighs], [music]
- [gasps], [clears throat], — or ... for hesitations
- ♪ for singing
Args:
text: Text to convert to speech (max ~13s per segment)
voice: Voice preset to use (see BARK_VOICES)
output_dir: Directory to save output (defaults to ./output)
Returns:
Path to the generated audio file
"""
import functools
import numpy as np
import scipy.io.wavfile
import torch
# Bark uses older checkpoint format with pickle
# Monkey-patch torch.load to allow unsafe loading for Bark models
original_torch_load = torch.load
@functools.wraps(original_torch_load)
def patched_load(*args: object, **kwargs: object) -> object:
kwargs.setdefault("weights_only", False)
return original_torch_load(*args, **kwargs)
torch.load = patched_load
try:
from bark import SAMPLE_RATE, generate_audio, preload_models
if output_dir is None:
output_dir = Path(__file__).parent / "output"
output_dir.mkdir(exist_ok=True)
print("\nLoading Bark model...")
print("(First run will download models, ~5GB total)")
preload_models()
print(f"\nGenerating speech with voice: {voice}")
print(f"Text: {text!r}")
# Bark can only generate ~13s at a time
# For longer text, we need to split into sentences
audio_segments = []
# Split on sentence boundaries for longer texts
sentences = _split_into_sentences(text)
for i, sentence in enumerate(sentences):
if len(sentences) > 1:
print(f" Generating segment {i + 1}/{len(sentences)}...")
audio = generate_audio(
sentence.strip(),
history_prompt=voice,
)
audio_segments.append(audio)
# Combine segments
if len(audio_segments) > 1:
audio_data = np.concatenate(audio_segments)
else:
audio_data = audio_segments[0]
# Create filename
timestamp = datetime.now(tz=timezone.utc).strftime("%Y%m%d_%H%M%S")
safe_text = "".join(c if c.isalnum() or c in " -_" else "" for c in text[:30])
safe_text = safe_text.strip().replace(" ", "_")
filename = f"{timestamp}_speech_{safe_text}.wav"
output_path = output_dir / filename
scipy.io.wavfile.write(output_path, SAMPLE_RATE, audio_data)
print(f"\nSaved to: {output_path}")
print(f"Duration: {len(audio_data) / SAMPLE_RATE:.1f}s")
return output_path
finally:
# Restore original torch.load
torch.load = original_torch_load
def _split_into_sentences(text: str) -> list[str]:
"""Split text into sentences for Bark processing.
Args:
text: Text to split
Returns:
List of sentences
"""
import re
# Split on sentence-ending punctuation followed by space
sentences = re.split(r"(?<=[.!?])\s+", text.strip())
# Group very short sentences together
result = []
current = ""
for sentence in sentences:
if len(current) + len(sentence) < BARK_MAX_CHARS:
current = f"{current} {sentence}".strip()
else:
if current:
result.append(current)
current = sentence
if current:
result.append(current)
return result if result else [text]
def _resample_audio(
audio: object,
orig_sr: int,
target_sr: int,
) -> object:
"""Resample audio to a different sample rate.
Args:
audio: Audio data as numpy array
orig_sr: Original sample rate
target_sr: Target sample rate
Returns:
Resampled audio data
"""
import numpy as np
from scipy import signal
if orig_sr == target_sr:
return audio
# Calculate the resampling ratio
duration = len(audio) / orig_sr
target_length = int(duration * target_sr)
return signal.resample(audio, target_length).astype(np.float32)
def _mix_audio(
instrumental: object,
vocals: object,
vocal_volume: float = 0.8,
instrumental_volume: float = 0.6,
) -> object:
"""Mix vocals over instrumental track.
Args:
instrumental: Instrumental audio (numpy array)
vocals: Vocal audio (numpy array)
vocal_volume: Volume multiplier for vocals (0.0-1.0)
instrumental_volume: Volume multiplier for instrumental (0.0-1.0)
Returns:
Mixed audio data
"""
import numpy as np
# Ensure same length - pad or trim vocals to match instrumental
if len(vocals) < len(instrumental):
# Pad vocals with silence at the end
vocals = np.pad(vocals, (0, len(instrumental) - len(vocals)))
elif len(vocals) > len(instrumental):
# Trim vocals to match instrumental
vocals = vocals[: len(instrumental)]
# Mix the tracks
mixed = (instrumental * instrumental_volume) + (vocals * vocal_volume)
# Normalize to prevent clipping
max_val = np.max(np.abs(mixed))
if max_val > 1.0:
mixed = mixed / max_val
return mixed.astype(np.float32)
def _generate_vocals_for_song(lyrics: str, voice: str) -> tuple[object, int]:
"""Generate vocals using Bark for song mixing.
Args:
lyrics: Text/lyrics to sing
voice: Bark voice preset
Returns:
Tuple of (vocal audio array, sample rate)
"""
import functools
import numpy as np
import torch
# Patch torch.load for Bark compatibility
original_torch_load = torch.load
@functools.wraps(original_torch_load)
def patched_load(*args: object, **kwargs: object) -> object:
kwargs.setdefault("weights_only", False)
return original_torch_load(*args, **kwargs)
torch.load = patched_load
try:
from bark import SAMPLE_RATE as BARK_SR
from bark import generate_audio, preload_models
print("Loading Bark model...")
preload_models()
print(f"Generating vocals with voice: {voice}")
print(f"Lyrics: {lyrics!r}")
sentences = _split_into_sentences(lyrics)
vocal_segments = []
for i, sentence in enumerate(sentences):
if len(sentences) > 1:
print(f" Vocal segment {i + 1}/{len(sentences)}...")
audio = generate_audio(sentence.strip(), history_prompt=voice)
vocal_segments.append(audio)
if len(vocal_segments) > 1:
vocals = np.concatenate(vocal_segments)
else:
vocals = vocal_segments[0]
return vocals, BARK_SR
finally:
torch.load = original_torch_load
def _generate_instrumental_for_song(
music_prompt: str,
duration: int,
) -> tuple[object, int]:
"""Generate instrumental music using MusicGen for song mixing.
Args:
music_prompt: Description of the music
duration: Duration in seconds
Returns:
Tuple of (instrumental audio array, sample rate)
"""
model_size = select_model_size(None)
model, processor = load_model(model_size)
print(f"Music prompt: {music_prompt!r}")
print(f"Duration: {duration}s")
device = str(next(model.parameters()).device)
sample_rate = model.config.audio_encoder.sampling_rate
if duration <= SEGMENT_DURATION:
instrumental = generate_segment(
music_prompt,
model,
processor,
duration,
device,
)
else:
instrumental = _generate_long_audio(
music_prompt,
model,
processor,
duration,
)
return instrumental, sample_rate
def generate_song(
lyrics: str,
music_prompt: str,
voice: str = "v2/en_speaker_6",
output_dir: Path | None = None,
) -> Path:
"""Generate a complete song with vocals over instrumental music.
This combines Bark for vocals and MusicGen for instrumental backing.
Args:
lyrics: The lyrics/text to sing (use ♪ for singing style)
music_prompt: Description of the instrumental music
voice: Bark voice preset (default: v2/en_speaker_6)
output_dir: Directory to save output
Returns:
Path to the generated song file
"""
import scipy.io.wavfile
if output_dir is None:
output_dir = Path(__file__).parent / "output"
output_dir.mkdir(exist_ok=True)
print("=" * 60)
print("GENERATING SONG WITH VOCALS")
print("=" * 60)
# Step 1: Generate vocals
print("\n[1/3] Generating vocals...")
vocals, bark_sr = _generate_vocals_for_song(lyrics, voice)
vocal_duration = len(vocals) / bark_sr
print(f"Vocals generated: {vocal_duration:.1f}s")
# Step 2: Generate instrumental (match vocal duration + buffer)
print("\n[2/3] Generating instrumental music...")
music_duration = int(vocal_duration) + 2
instrumental, musicgen_sr = _generate_instrumental_for_song(
music_prompt,
music_duration,
)
print(f"Instrumental generated: {len(instrumental) / musicgen_sr:.1f}s")
# Step 3: Mix vocals and instrumental
print("\n[3/3] Mixing vocals and instrumental...")
vocals_resampled = _resample_audio(vocals, bark_sr, musicgen_sr)
mixed = _mix_audio(instrumental, vocals_resampled)
# Save the song
timestamp = datetime.now(tz=timezone.utc).strftime("%Y%m%d_%H%M%S")
safe_lyrics = "".join(c if c.isalnum() or c in " -_" else "" for c in lyrics[:20])
safe_lyrics = safe_lyrics.strip().replace(" ", "_")
filename = f"{timestamp}_song_{safe_lyrics}.wav"
output_path = output_dir / filename
scipy.io.wavfile.write(output_path, musicgen_sr, mixed)
print("\n" + "=" * 60)
print(f"Song saved to: {output_path}")
print(f"Duration: {len(mixed) / musicgen_sr:.1f}s")
print("=" * 60)
return output_path
def crossfade_audio(
audio1: object,
audio2: object,
crossfade_samples: int,
) -> object:
"""Crossfade two audio segments together.
Args:
audio1: First audio segment (numpy array)
audio2: Second audio segment (numpy array)
crossfade_samples: Number of samples to use for crossfade
Returns:
Combined audio with crossfade applied (numpy array)
"""
import numpy as np
if crossfade_samples <= 0 or len(audio1) < crossfade_samples:
return np.concatenate([audio1, audio2])
# Create fade curves
fade_out = np.linspace(1.0, 0.0, crossfade_samples)
fade_in = np.linspace(0.0, 1.0, crossfade_samples)
# Apply fades
audio1_end = audio1[-crossfade_samples:] * fade_out
audio2_start = audio2[:crossfade_samples] * fade_in
# Combine
crossfaded = audio1_end + audio2_start
# Build final audio
return np.concatenate(
[
audio1[:-crossfade_samples],
crossfaded,
audio2[crossfade_samples:],
]
)
def generate_segment(
prompt: str,
model: object,
processor: object,
duration_seconds: int,
device: str,
) -> object:
"""Generate a single audio segment.
Args:
prompt: Text description of the music
model: The MusicGen model
processor: The MusicGen processor
duration_seconds: Length of segment to generate
device: Device to generate on
Returns:
Audio data as numpy array
"""
import torch
inputs = processor(
text=[prompt],
padding=True,
return_tensors="pt",
)
inputs = {k: v.to(device) for k, v in inputs.items()}
max_new_tokens = int(duration_seconds * 50)
with torch.no_grad():
audio_values = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
)
return audio_values[0, 0].cpu().numpy()
def _calculate_segment_duration(
segment_index: int,
num_segments: int,
generated_samples: int,
sample_rate: int,
total_duration: int,
) -> int:
"""Calculate duration for a specific segment.
Args:
segment_index: Current segment index
num_segments: Total number of segments
generated_samples: Number of samples generated so far
sample_rate: Audio sample rate
total_duration: Target total duration
Returns:
Duration in seconds for this segment
"""
if segment_index == num_segments - 1:
# Last segment: calculate remaining time
generated_so_far = generated_samples / sample_rate
remaining = total_duration - generated_so_far
min_duration = max(5, int(remaining) + CROSSFADE_DURATION)
return min(SEGMENT_DURATION, min_duration)
return SEGMENT_DURATION
def _generate_long_audio(
prompt: str,
model: object,
processor: object,
duration_seconds: int,
) -> object:
"""Generate long audio by segmenting with crossfades.
Args:
prompt: Text description of the music
model: The MusicGen model
processor: The MusicGen processor
duration_seconds: Total duration to generate
Returns:
Audio data as numpy array
"""
import numpy as np
device = str(next(model.parameters()).device)
sample_rate = model.config.audio_encoder.sampling_rate
crossfade_samples = CROSSFADE_DURATION * sample_rate
effective_segment = SEGMENT_DURATION - CROSSFADE_DURATION
total = duration_seconds + effective_segment - 1
num_segments = max(1, total // effective_segment)
print(f"Generating {num_segments} segments of ~{SEGMENT_DURATION}s each...")
audio_data = np.array([], dtype=np.float32)
for i in range(num_segments):
segment_duration = _calculate_segment_duration(
i,
num_segments,
len(audio_data),
sample_rate,
duration_seconds,
)
seg_num = i + 1
msg = f" Segment {seg_num}/{num_segments} ({segment_duration}s)..."
print(msg, end=" ", flush=True)
segment = generate_segment(
prompt,
model,
processor,
segment_duration,
device,
)
if len(audio_data) == 0:
audio_data = segment
else:
audio_data = crossfade_audio(audio_data, segment, crossfade_samples)
print(f"done (total: {len(audio_data) / sample_rate:.1f}s)")
# Trim to exact duration if needed
target_samples = int(duration_seconds * sample_rate)
if len(audio_data) > target_samples:
audio_data = audio_data[:target_samples]
return audio_data
def generate_music(
prompt: str,
model: object,
@ -191,53 +750,43 @@ def generate_music(
) -> Path:
"""Generate music from a text prompt.
For durations over 30 seconds, generates in segments with crossfading.
Args:
prompt: Text description of the music to generate
model: The MusicGen model
processor: The MusicGen processor
duration_seconds: Length of audio to generate (max ~30s recommended)
duration_seconds: Length of audio to generate (any duration supported)
output_dir: Directory to save output (defaults to ./output)
Returns:
Path to the generated audio file
"""
import scipy.io.wavfile
import torch
if output_dir is None:
output_dir = Path(__file__).parent / "output"
output_dir.mkdir(exist_ok=True)
print(f"\nGenerating {duration_seconds}s of music...")
print(f"Prompt: {prompt!r}")
device = next(model.parameters()).device
# Prepare inputs
inputs = processor(
text=[prompt],
padding=True,
return_tensors="pt",
)
inputs = {k: v.to(device) for k, v in inputs.items()}
# Calculate tokens needed for duration
# MusicGen generates ~50 tokens per second of audio
max_new_tokens = int(duration_seconds * 50)
# Generate
with torch.no_grad():
audio_values = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
)
# Get sample rate from model config
sample_rate = model.config.audio_encoder.sampling_rate
# Convert to numpy and save
audio_data = audio_values[0, 0].cpu().numpy()
# For short durations, generate directly
if duration_seconds <= SEGMENT_DURATION:
print(f"\nGenerating {duration_seconds}s of music...")
print(f"Prompt: {prompt!r}")
device = str(next(model.parameters()).device)
audio_data = generate_segment(
prompt,
model,
processor,
duration_seconds,
device,
)
else:
# Long duration: generate in segments with crossfading
print(f"\nGenerating {duration_seconds}s of music in segments...")
print(f"Prompt: {prompt!r}")
audio_data = _generate_long_audio(prompt, model, processor, duration_seconds)
# Create filename with timestamp and sanitized prompt
timestamp = datetime.now(tz=timezone.utc).strftime("%Y%m%d_%H%M%S")
@ -328,46 +877,58 @@ def interactive_mode(model: object, processor: object) -> None:
def main() -> None:
"""Main entry point."""
parser = argparse.ArgumentParser(
description="Generate music from text prompts using MusicGen",
description="Generate music or speech from text prompts",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Music generation (MusicGen):
%(prog)s "upbeat electronic dance music"
%(prog)s --duration 20 "calm piano melody"
%(prog)s --duration 60 "calm piano melody"
%(prog)s --model small "jazz guitar solo"
%(prog)s --interactive
Model sizes (auto-selected based on VRAM if not specified):
# Speech/vocals generation (Bark):
%(prog)s --speech "Hello, how are you today?"
%(prog)s --speech --voice v2/en_speaker_3 "Welcome!"
%(prog)s --speech "♪ La la la, I love to sing ♪"
# Full song with vocals over music:
%(prog)s --song "♪ Hello world, this is my song ♪" --music "upbeat pop"
Model sizes for MusicGen (auto-selected based on VRAM if not specified):
small - ~500MB, fastest, lower quality (3GB+ VRAM)
medium - ~3.3GB, good balance (8GB+ VRAM)
large - ~6.5GB, best quality (12GB+ VRAM)
Bark voices: v2/en_speaker_0 to v2/en_speaker_9
Bark tokens: [laughter] [laughs] [sighs] [music] [gasps] ♪ (singing)
""",
)
parser.add_argument(
"prompt",
nargs="?",
help="Text description of music to generate",
help="Text description of music/speech to generate",
)
parser.add_argument(
"-d",
"--duration",
type=int,
default=10,
help="Duration in seconds (default: 10, max recommended: 30)",
help="Duration in seconds (default: 10, any length supported)",
)
parser.add_argument(
"-m",
"--model",
choices=["small", "medium", "large"],
default=None,
help="Model size (default: auto-select based on VRAM, largest possible)",
help="MusicGen model size (auto-select based on VRAM by default)",
)
parser.add_argument(
"-i",
"--interactive",
action="store_true",
help="Run in interactive mode",
help="Run in interactive mode (MusicGen only)",
)
parser.add_argument(
"-o",
@ -375,6 +936,29 @@ Model sizes (auto-selected based on VRAM if not specified):
type=Path,
help="Output directory (default: ./output)",
)
parser.add_argument(
"-s",
"--speech",
action="store_true",
help="Generate speech/vocals using Bark instead of music",
)
parser.add_argument(
"-v",
"--voice",
default="v2/en_speaker_6",
help="Bark voice preset (default: v2/en_speaker_6)",
)
parser.add_argument(
"--song",
action="store_true",
help="Generate a full song with vocals over instrumental",
)
parser.add_argument(
"--music",
type=str,
default="upbeat pop instrumental backing track",
help="Music style for --song mode (default: upbeat pop)",
)
args = parser.parse_args()
@ -384,25 +968,40 @@ Model sizes (auto-selected based on VRAM if not specified):
sys.exit(1)
# Check dependencies
if not check_dependencies():
use_bark = args.speech or args.song
if not check_dependencies(include_bark=use_bark):
sys.exit(1)
# Select model size based on VRAM if not specified
model_size = select_model_size(args.model)
# Load model
model, processor = load_model(model_size)
if args.interactive:
interactive_mode(model, processor)
else:
generate_music(
if args.song:
# Full song generation mode (vocals + instrumental)
generate_song(
args.prompt,
model,
processor,
duration_seconds=args.duration,
args.music,
voice=args.voice,
output_dir=args.output,
)
elif args.speech:
# Bark speech generation mode
generate_speech(
args.prompt,
voice=args.voice,
output_dir=args.output,
)
else:
# MusicGen music generation mode
model_size = select_model_size(args.model)
model, processor = load_model(model_size)
if args.interactive:
interactive_mode(model, processor)
else:
generate_music(
args.prompt,
model,
processor,
duration_seconds=args.duration,
output_dir=args.output,
)
if __name__ == "__main__":