"""
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
from tempfile import NamedTemporaryFile
import argparse
import torch
import gradio as gr
import os
import subprocess
import sys
from pathlib import Path
import time
import typing as tp
import warnings
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
from audiocraft.data.audio_utils import apply_fade, apply_tafade, apply_splice_effect
from audiocraft.utils.extend import generate_music_segments, add_settings_to_image, INTERRUPTING
import numpy as np
import random
#from pathlib import Path
#from typing import List, Union
import librosa
MODEL = None
MODELS = None
IS_SHARED_SPACE = "Surn/UnlimitedMusicGen" in os.environ.get('SPACE_ID', '')
INTERRUPTED = False
UNLOAD_MODEL = False
MOVE_TO_CPU = False
MAX_PROMPT_INDEX = 0
git = os.environ.get('GIT', "git")
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
def interrupt_callback():
return INTERRUPTED
def interrupt():
global INTERRUPTING
INTERRUPTING = True
class FileCleaner:
def __init__(self, file_lifetime: float = 3600):
self.file_lifetime = file_lifetime
self.files = []
def add(self, path: tp.Union[str, Path]):
self._cleanup()
self.files.append((time.time(), Path(path)))
def _cleanup(self):
now = time.time()
for time_added, path in list(self.files):
if now - time_added > self.file_lifetime:
if path.exists():
path.unlink()
self.files.pop(0)
else:
break
#file_cleaner = FileCleaner()
def toggle_audio_src(choice):
if choice == "mic":
return gr.update(source="microphone", value=None, label="Microphone")
else:
return gr.update(source="upload", value=None, label="File")
def make_waveform(*args, **kwargs):
# Further remove some warnings.
be = time.time()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
out = gr.make_waveform(*args, **kwargs)
print("Make a video took", time.time() - be)
return out
def load_model(version):
global MODEL, MODELS, UNLOAD_MODEL
print("Loading model", version)
if MODELS is None:
return MusicGen.get_pretrained(version)
else:
t1 = time.monotonic()
if MODEL is not None:
MODEL.to('cpu') # move to cache
print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
t1 = time.monotonic()
if MODELS.get(version) is None:
print("Loading model %s from disk" % version)
result = MusicGen.get_pretrained(version)
MODELS[version] = result
print("Model loaded in %.2fs" % (time.monotonic() - t1))
return result
result = MODELS[version].to('cuda')
print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
return result
def get_filename(file):
# extract filename from file object
filename = None
if file is not None:
filename = file.name
return filename
def get_filename_from_filepath(filepath):
file_name = os.path.basename(filepath)
file_base, file_extension = os.path.splitext(file_name)
return file_base, file_extension
def get_melody(melody_filepath):
audio_data= list(librosa.load(melody_filepath, sr=None))
audio_data[0], audio_data[1] = audio_data[1], audio_data[0]
melody = tuple(audio_data)
return melody
def commit_hash():
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
try:
from pathlib import Path
changelog_md = Path(__file__).parent.parent / "CHANGELOG.md"
with changelog_md.open(encoding="utf-8") as file:
return next((line.strip() for line in file if line.strip()), "<none>")
except Exception:
return "<none>"
def versions_html():
import torch
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
commit = commit_hash()
#tag = git_tag()
import xformers
xformers_version = xformers.__version__
return f"""
version: <a href="https://github.com/Oncorporation/audiocraft/commit/{"huggingface" if commit == "<none>" else commit}" target="_blank">{"huggingface" if commit == "<none>" else commit}</a>
 • 
python: <span title="{sys.version}">{python_version}</span>
 • 
torch: {getattr(torch, '__long_version__',torch.__version__)}
 • 
xformers: {xformers_version}
 • 
gradio: {gr.__version__}
"""
def load_melody_filepath(melody_filepath, title):
# get melody filename
#$Union[str, os.PathLike]
symbols = ['_', '.', '-']
if (melody_filepath is None) or (melody_filepath == ""):
return title, gr.update(maximum=0, value=0) , gr.update(value="melody-large", interactive=True)
if (title is None) or ("MusicGen" in title) or (title == ""):
melody_name, melody_extension = get_filename_from_filepath(melody_filepath)
# fix melody name for symbols
for symbol in symbols:
melody_name = melody_name.replace(symbol, ' ').title()
else:
melody_name = title
print(f"Melody name: {melody_name}, Melody Filepath: {melody_filepath}\n")
# get melody length in number of segments and modify the UI
melody = get_melody(melody_filepath)
sr, melody_data = melody[0], melody[1]
segment_samples = sr * 30
total_melodys = max(min((len(melody_data) // segment_samples), 25), 0)
print(f"Melody length: {len(melody_data)}, Melody segments: {total_melodys}\n")
MAX_PROMPT_INDEX = total_melodys
return gr.Textbox.update(value=melody_name), gr.update(maximum=MAX_PROMPT_INDEX, value=0), gr.update(value="melody-large", interactive=True)
def predict(model, text, melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap=1, prompt_index = 0, include_title = True, include_settings = True, harmony_only = False):
global MODEL, INTERRUPTED, INTERRUPTING, MOVE_TO_CPU
output_segments = None
melody_name = "Not Used"
melody = None
if melody_filepath:
melody_name, melody_extension = get_filename_from_filepath(melody_filepath)
melody = get_melody(melody_filepath)
INTERRUPTED = False
INTERRUPTING = False
if temperature < 0:
raise gr.Error("Temperature must be >= 0.")
if topk < 0:
raise gr.Error("Topk must be non-negative.")
if topp < 0:
raise gr.Error("Topp must be non-negative.")
if MODEL is None or MODEL.name != model:
MODEL = load_model(model)
else:
if MOVE_TO_CPU:
MODEL.to('cuda')
# prevent hacking
duration = min(duration, 720)
overlap = min(overlap, 15)
#
output = None
segment_duration = duration
initial_duration = duration
output_segments = []
while duration > 0:
if not output_segments: # first pass of long or short song
if segment_duration > MODEL.lm.cfg.dataset.segment_duration:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
else:
segment_duration = duration
else: # next pass of long song
if duration + overlap < MODEL.lm.cfg.dataset.segment_duration:
segment_duration = duration + overlap
else:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
# implement seed
if seed < 0:
seed = random.randint(0, 0xffff_ffff_ffff)
torch.manual_seed(seed)
print(f'Segment duration: {segment_duration}, duration: {duration}, overlap: {overlap}')
MODEL.set_generation_params(
use_sampling=True,
top_k=topk,
top_p=topp,
temperature=temperature,
cfg_coef=cfg_coef,
duration=segment_duration,
two_step_cfg=False,
rep_penalty=0.5
)
if melody:
# todo return excess duration, load next model and continue in loop structure building up output_segments
if duration > MODEL.lm.cfg.dataset.segment_duration:
output_segments, duration = generate_music_segments(text, melody, seed, MODEL, duration, overlap, MODEL.lm.cfg.dataset.segment_duration, prompt_index, harmony_only=False)
else:
# pure original code
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
print(melody.shape)
if melody.dim() == 2:
melody = melody[None]
melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
output = MODEL.generate_with_chroma(
descriptions=[text],
melody_wavs=melody,
melody_sample_rate=sr,
progress=False
)
# All output_segments are populated, so we can break the loop or set duration to 0
break
else:
#output = MODEL.generate(descriptions=[text], progress=False)
if not output_segments:
next_segment = MODEL.generate(descriptions=[text], progress=False)
duration -= segment_duration
else:
last_chunk = output_segments[-1][:, :, -overlap*MODEL.sample_rate:]
next_segment = MODEL.generate_continuation(last_chunk, MODEL.sample_rate, descriptions=[text], progress=False)
duration -= segment_duration - overlap
output_segments.append(next_segment)
if INTERRUPTING:
INTERRUPTED = True
INTERRUPTING = False
print("Function execution interrupted!")
raise gr.Error("Interrupted.")
if output_segments:
try:
# Combine the output segments into one long audio file or stack tracks
#output_segments = [segment.detach().cpu().float()[0] for segment in output_segments]
#output = torch.cat(output_segments, dim=dimension)
output = output_segments[0]
for i in range(1, len(output_segments)):
if overlap > 0:
overlap_samples = overlap * MODEL.sample_rate
#stack tracks and fade out/in
overlapping_output_fadeout = output[:, :, -overlap_samples:]
#overlapping_output_fadeout = apply_fade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True, curve_end=0.0, current_device=MODEL.device)
overlapping_output_fadeout = apply_tafade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True,shape="linear")
overlapping_output_fadein = output_segments[i][:, :, :overlap_samples]
#overlapping_output_fadein = apply_fade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, curve_start=0.0, current_device=MODEL.device)
overlapping_output_fadein = apply_tafade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, shape="linear")
overlapping_output = torch.cat([overlapping_output_fadeout[:, :, :-(overlap_samples // 2)], overlapping_output_fadein],dim=2)
###overlapping_output, overlap_sample_rate = apply_splice_effect(overlapping_output_fadeout, MODEL.sample_rate, overlapping_output_fadein, MODEL.sample_rate, overlap)
print(f" overlap size Fade:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
##overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=1) #stack tracks
##print(f" overlap size stack:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
#overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=2) #stack tracks
#print(f" overlap size cat:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
output = torch.cat([output[:, :, :-overlap_samples], overlapping_output, output_segments[i][:, :, overlap_samples:]], dim=dimension)
else:
output = torch.cat([output, output_segments[i]], dim=dimension)
output = output.detach().cpu().float()[0]
except Exception as e:
print(f"Error combining segments: {e}. Using the first segment only.")
output = output_segments[0].detach().cpu().float()[0]
else:
output = output.detach().cpu().float()[0]
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
video_description = f"{text}\n Duration: {str(initial_duration)} Dimension: {dimension}\n Top-k:{topk} Top-p:{topp}\n Randomness:{temperature}\n cfg:{cfg_coef} overlap: {overlap}\n Seed: {seed}\n Model: {model}\n Melody Condition:{melody_name}\n Sample Segment: {prompt_index}"
if include_settings or include_title:
background = add_settings_to_image(title if include_title else "", video_description if include_settings else "", background_path=background, font=settings_font, font_color=settings_font_color)
audio_write(
file.name, output, MODEL.sample_rate, strategy="loudness",
loudness_headroom_db=18, loudness_compressor=True, add_suffix=False, channels=2)
waveform_video = make_waveform(file.name,bg_image=background, bar_count=45)
if MOVE_TO_CPU:
MODEL.to('cpu')
if UNLOAD_MODEL:
MODEL = None
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return waveform_video, file.name, seed
def ui(**kwargs):
css="""
#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
#btn-generate {background-image:linear-gradient(to right bottom, rgb(157, 255, 157), rgb(229, 255, 235));}
#btn-generate:hover {background-image:linear-gradient(to right bottom, rgb(229, 255, 229), rgb(255, 255, 255));}
#btn-generate:active {background-image:linear-gradient(to right bottom, rgb(229, 255, 235), rgb(157, 255, 157));}
#versions {margin-top: 1em; width:100%; text-align:center;}
.small-btn {max-width:75px;}
"""
with gr.Blocks(title="UnlimitedMusicGen", css=css, theme="Surn/Beeuty") as demo:
gr.Markdown(
"""
# UnlimitedMusicGen
This is your private demo for [UnlimitedMusicGen](https://github.com/Oncorporation/audiocraft), a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
Disclaimer: This won't run on CPU only. Clone this App and run on GPU instance!
Todo: Working on improved Interrupt and new Models.
Theme Available at ["Surn/Beeuty"](https://huggingface.co/spaces/Surn/Beeuty)
"""
)
if IS_SHARED_SPACE and not torch.cuda.is_available():
gr.Markdown("""
⚠ This Space doesn't work in this shared UI ⚠
<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
""")
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Describe your music", interactive=True, value="4/4 100bpm 320kbps 48khz, Industrial/Electronic Soundtrack, Dark, Intense, Sci-Fi")
with gr.Column():
duration = gr.Slider(minimum=1, maximum=720, value=10, label="Duration (s)", interactive=True)
model = gr.Radio(["melody", "medium", "small", "large", "melody-large", "stereo-melody", "stereo-medium", "stereo-small", "stereo-large", "stereo-melody-large"], label="AI Model", value="melody-large", interactive=True)
with gr.Row():
submit = gr.Button("Generate", elem_id="btn-generate")
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
_ = gr.Button("Interrupt", elem_id="btn-interrupt").click(fn=interrupt, queue=False)
with gr.Row():
with gr.Column():
radio = gr.Radio(["file", "mic"], value="file", label="Condition on a melody (optional) File or Mic")
melody_filepath = gr.Audio(source="upload", type="filepath", label="Melody Condition (optional)", interactive=True, elem_id="melody-input")
with gr.Column():
harmony_only = gr.Radio(label="Use Harmony Only",choices=["No", "Yes"], value="No", interactive=True, info="Remove Drums?")
prompt_index = gr.Slider(label="Melody Condition Sample Segment", minimum=-1, maximum=MAX_PROMPT_INDEX, step=1, value=0, interactive=True, info="Which 30 second segment to condition with, - 1 condition each segment independantly")
with gr.Accordion("Video", open=False):
with gr.Row():
background= gr.Image(value="./assets/background.png", source="upload", label="Background", shape=(768,512), type="filepath", interactive=True)
with gr.Column():
include_title = gr.Checkbox(label="Add Title", value=True, interactive=True)
include_settings = gr.Checkbox(label="Add Settings to background", value=True, interactive=True)
with gr.Row():
title = gr.Textbox(label="Title", value="UnlimitedMusicGen", interactive=True)
settings_font = gr.Text(label="Settings Font", value="./assets/arial.ttf", interactive=True)
settings_font_color = gr.ColorPicker(label="Settings Font Color", value="#c87f05", interactive=True)
with gr.Accordion("Expert", open=False):
with gr.Row():
overlap = gr.Slider(minimum=0, maximum=15, value=2, step=1, label="Verse Overlap", interactive=True)
dimension = gr.Slider(minimum=-2, maximum=2, value=2, step=1, label="Dimension", info="determines which direction to add new segements of audio. (1 = stack tracks, 2 = lengthen, -2..0 = ?)", interactive=True)
with gr.Row():
topk = gr.Number(label="Top-k", value=280, precision=0, interactive=True)
topp = gr.Number(label="Top-p", value=1150, precision=0, interactive=True)
temperature = gr.Number(label="Randomness Temperature", value=0.7, precision=None, interactive=True)
cfg_coef = gr.Number(label="Classifier Free Guidance", value=8.5, precision=None, interactive=True)
with gr.Row():
seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
gr.Button('\U0001f3b2\ufe0f', elem_classes="small-btn").click(fn=lambda: -1, outputs=[seed], queue=False)
reuse_seed = gr.Button('\u267b\ufe0f', elem_classes="small-btn")
with gr.Column() as c:
output = gr.Video(label="Generated Music")
wave_file = gr.File(label=".wav file", elem_id="output_wavefile", interactive=True)
seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
radio.change(toggle_audio_src, radio, [melody_filepath], queue=False, show_progress=False)
melody_filepath.change(load_melody_filepath, inputs=[melody_filepath, title], outputs=[title, prompt_index , model], api_name="melody_filepath_change", queue=False)
reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False, api_name="reuse_seed")
submit.click(predict, inputs=[model, text,melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap, prompt_index, include_title, include_settings, harmony_only], outputs=[output, wave_file, seed_used], api_name="submit")
gr.Examples(
fn=predict,
examples=[
[
"4/4 120bpm 320kbps 48khz, An 80s driving pop song with heavy drums and synth pads in the background",
"./assets/bach.mp3",
"melody",
"80s Pop Synth"
],
[
"4/4 120bpm 320kbps 48khz, A cheerful country song with acoustic guitars",
"./assets/bolero_ravel.mp3",
"melody",
"Country Guitar"
],
[
"4/4 120bpm 320kbps 48khz, 90s rock song with electric guitar and heavy drums",
None,
"medium",
"90s Rock Guitar"
],
[
"4/4 120bpm 320kbps 48khz, a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
"./assets/bach.mp3",
"melody",
"EDM my Bach"
],
[
"4/4 320kbps 48khz, lofi slow bpm electro chill with organic samples",
None,
"medium",
"LoFi Chill"
],
],
inputs=[text, melody_filepath, model, title],
outputs=[output]
)
gr.HTML(value=versions_html(), visible=True, elem_id="versions")
# Show the interface
launch_kwargs = {}
share = kwargs.get('share', False)
server_port = kwargs.get('server_port', 0)
server_name = kwargs.get('listen')
launch_kwargs['server_name'] = server_name
if server_port > 0:
launch_kwargs['server_port'] = server_port
if share:
launch_kwargs['share'] = share
launch_kwargs['favicon_path']= "./assets/favicon.ico"
demo.queue(max_size=10, concurrency_count=1, api_open=False).launch(**launch_kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen',
type=str,
default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
help='IP to listen on for connections to Gradio',
)
parser.add_argument(
'--username', type=str, default='', help='Username for authentication'
)
parser.add_argument(
'--password', type=str, default='', help='Password for authentication'
)
parser.add_argument(
'--server_port',
type=int,
default=0,
help='Port to run the server listener on',
)
parser.add_argument(
'--inbrowser', action='store_true', help='Open in browser'
)
parser.add_argument(
'--share', action='store_true', help='Share the gradio UI'
)
parser.add_argument(
'--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
)
parser.add_argument(
'--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
)
parser.add_argument(
'--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
)
args = parser.parse_args()
launch_kwargs = {}
launch_kwargs['listen'] = args.listen
if args.username and args.password:
launch_kwargs['auth'] = (args.username, args.password)
if args.server_port:
launch_kwargs['server_port'] = args.server_port
if args.inbrowser:
launch_kwargs['inbrowser'] = args.inbrowser
if args.share:
launch_kwargs['share'] = args.share
launch_kwargs['favicon_path']= "./assets/favicon.ico"
UNLOAD_MODEL = args.unload_model
MOVE_TO_CPU = args.unload_to_cpu
if args.cache:
MODELS = {}
ui(
unload_to_cpu = MOVE_TO_CPU,
share=args.share,
**launch_kwargs,
)