# 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.
# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
# also released under the MIT license.
import random
import argparse
from concurrent.futures import ProcessPoolExecutor
import os
import subprocess as sp
from tempfile import NamedTemporaryFile
import time
import warnings
import glob
import re
from pathlib import Path
from PIL import Image
from pydub import AudioSegment
from pydub.effects import normalize
from datetime import datetime
import json
import shutil
import taglib
import torch
import torchaudio
import gradio as gr
import numpy as np
import typing as tp
from audiocraft.data.audio_utils import convert_audio
from audiocraft.data.audio import audio_write
from audiocraft.models import MusicGen
from audiocraft.utils import ui
import subprocess, random, string
theme = gr.themes.Base(
primary_hue="lime",
secondary_hue="lime",
neutral_hue="neutral",
).set(
button_primary_background_fill_hover='*primary_500',
button_primary_background_fill_hover_dark='*primary_500',
button_secondary_background_fill_hover='*primary_500',
button_secondary_background_fill_hover_dark='*primary_500'
)
MODEL = None # Last used model
MODELS = None
IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '')
INTERRUPTED = False
UNLOAD_MODEL = False
MOVE_TO_CPU = False
IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
MAX_BATCH_SIZE = 12
BATCHED_DURATION = 15
INTERRUPTING = False
# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
_old_call = sp.call
def generate_random_string(length):
characters = string.ascii_letters + string.digits
return ''.join(random.choice(characters) for _ in range(length))
def resize_video(input_path, output_path, target_width, target_height):
ffmpeg_cmd = [
'ffmpeg',
'-y',
'-i', input_path,
'-vf', f'scale={target_width}:{target_height}',
'-c:a', 'copy',
output_path
]
subprocess.run(ffmpeg_cmd)
def _call_nostderr(*args, **kwargs):
# Avoid ffmpeg vomitting on the logs.
kwargs['stderr'] = sp.DEVNULL
kwargs['stdout'] = sp.DEVNULL
_old_call(*args, **kwargs)
sp.call = _call_nostderr
# Preallocating the pool of processes.
pool = ProcessPoolExecutor(4)
pool.__enter__()
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 make_waveform(*args, **kwargs):
# Further remove some warnings.
be = time.time()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
height = kwargs.pop('height')
width = kwargs.pop('width')
if height < 256:
height = 256
if width < 256:
width = 256
waveform_video = gr.make_waveform(*args, **kwargs)
out = f"{generate_random_string(12)}.mp4"
image = kwargs.get('bg_image', None)
if image is None:
resize_video(waveform_video, out, 900, 300)
else:
resize_video(waveform_video, out, width, height)
print("Make a video took", time.time() - be)
return out
def load_model(version='melody', custom_model=None, base_model='medium'):
global MODEL, MODELS
print("Loading model", version)
if MODELS is None:
if version == 'custom':
MODEL = MusicGen.get_pretrained(base_model)
MODEL.lm.load_state_dict(torch.load(custom_model))
else:
MODEL = MusicGen.get_pretrained(version)
return
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 version != 'custom' and 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))
MODEL = result
return
result = MODELS[version].to('cuda')
print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
MODEL = result
def get_audio_info(audio_path):
if audio_path is not None:
with taglib.File(audio_path.name, save_on_exit=False) as song:
json_string = song.tags['COMMENT'][0]
data = json.loads(json_string)
prompts = str("Prompts: " + data['texts'])
duration = str("Duration: " + data['duration'])
overlap = str("Overlap: " + data['overlap'])
seed = str("Seed: " + data['seed'])
audio_mode = str("Audio Mode: " + data['audio_mode'])
input_length = str("Input Length: " + data['input_length'])
channel = str("Channel: " + data['channel'])
sr_select = str("Sample Rate: " + data['sr_select'])
model = str("Model: " + data['model'])
topk = str("Topk: " + data['topk'])
topp = str("Topp: " + data['topp'])
temperature = str("Temperature: " + data['temperature'])
cfg_coef = str("Classifier Free Guidance: " + data['cfg_coef'])
info = str(prompts + "\n" + duration + "\n" + overlap + "\n" + seed + "\n" + audio_mode + "\n" + input_length + "\n" + channel + "\n" + sr_select + "\n" + model + "\n" + topk + "\n" + topp + "\n" + temperature + "\n" + cfg_coef)
return info
else:
return None
def info_to_params(audio_path):
if audio_path is not None:
with taglib.File(audio_path.name, save_on_exit=False) as song:
json_string = song.tags['COMMENT'][0]
data = json.loads(json_string)
s = data['texts']
s = re.findall(r"'(.*?)'", s)
text = []
repeat = []
i = 0
for elem in s:
if elem.strip():
if i == 0 or elem != s[i-1]:
text.append(elem)
repeat.append(1)
else:
repeat[-1] += 1
i += 1
text.extend([""] * (10 - len(text)))
repeat.extend([1] * (10 - len(repeat)))
unique_prompts = len([t for t in text if t])
return data['model'], unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], data['audio_mode'], int(data['duration']), float(data['topk']), float(data['topp']), float(data['temperature']), float(data['cfg_coef']), int(data['seed']), int(data['overlap']), data['channel'], data['sr_select']
else:
return "large", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
def make_pseudo_stereo (filename, sr_select, pan, delay):
if pan:
temp = AudioSegment.from_wav(filename)
if sr_select != "32000":
temp = temp.set_frame_rate(int(sr_select))
left = temp.pan(-0.5) - 5
right = temp.pan(0.6) - 5
temp = left.overlay(right, position=5)
temp.export(filename, format="wav")
if delay:
waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
delay_seconds = 0.01 # set delay 10ms
delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
torchaudio.save(filename, stereo_waveform, sample_rate)
return
def normalize_audio(audio_data):
audio_data = audio_data.astype(np.float32)
max_value = np.max(np.abs(audio_data))
audio_data /= max_value
return audio_data
def _do_predictions(texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
maximum_size = 29.5
cut_size = 0
input_length = 0
sampleP = None
if sample is not None:
globalSR, sampleM = sample[0], sample[1]
sampleM = normalize_audio(sampleM)
sampleM = torch.from_numpy(sampleM).t()
if sampleM.dim() == 1:
sampleM = sampleM.unsqueeze(0)
sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
if trim_start >= sample_length:
trim_start = sample_length - 0.5
if trim_end >= sample_length:
trim_end = sample_length - 0.5
if trim_start + trim_end >= sample_length:
tmp = sample_length - 0.5
trim_start = tmp / 2
trim_end = tmp / 2
sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
sample_length = sample_length - (trim_start + trim_end)
if sample_length > maximum_size:
cut_size = sample_length - maximum_size
sampleP = sampleM[..., :int(globalSR * cut_size)]
sampleM = sampleM[..., int(globalSR * cut_size):]
if sample_length >= duration:
duration = sample_length + 0.5
input_length = sample_length
global MODEL
MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
be = time.time()
processed_melodies = []
target_sr = 32000
target_ac = 1
for melody in melodies:
if melody is None:
processed_melodies.append(None)
else:
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
if melody.dim() == 1:
melody = melody[None]
melody = melody[..., :int(sr * duration)]
melody = convert_audio(melody, sr, target_sr, target_ac)
processed_melodies.append(melody)
if sample is not None:
if sampleP is None:
outputs = MODEL.generate_continuation(
prompt=sampleM,
prompt_sample_rate=globalSR,
descriptions=texts,
progress=progress,
)
else:
if sampleP.dim() > 1:
sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
outputs = MODEL.generate_continuation(
prompt=sampleM,
prompt_sample_rate=globalSR,
descriptions=texts,
progress=progress,
)
outputs = torch.cat([sampleP, outputs], 2)
elif any(m is not None for m in processed_melodies):
outputs = MODEL.generate_with_chroma(
descriptions=texts,
melody_wavs=processed_melodies,
melody_sample_rate=target_sr,
progress=progress,
)
else:
outputs = MODEL.generate(texts, progress=progress)
outputs = outputs.detach().cpu().float()
backups = outputs
if channel == "stereo":
outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
elif channel == "mono" and sr_select != "32000":
outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
out_files = []
out_audios = []
out_backup = []
for output in outputs:
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
audio_write(
file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
if channel == "stereo effect":
make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
out_audios.append(file.name)
out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
file_cleaner.add(file.name)
for backup in backups:
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
audio_write(
file.name, backup, MODEL.sample_rate, strategy="loudness",
loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
out_backup.append(file.name)
file_cleaner.add(file.name)
res = [out_file.result() for out_file in out_files]
res_audio = out_audios
res_backup = out_backup
for file in res:
file_cleaner.add(file)
print("batch finished", len(texts), time.time() - be)
print("Tempfiles currently stored: ", len(file_cleaner.files))
if MOVE_TO_CPU:
MODEL.to('cpu')
if UNLOAD_MODEL:
MODEL = None
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return res, res_audio, res_backup, input_length
def predict_batched(texts, melodies):
max_text_length = 512
texts = [text[:max_text_length] for text in texts]
load_model('melody')
res = _do_predictions(texts, melodies, BATCHED_DURATION)
return [res]
def add_tags(filename, tags):
json_string = None
data = {
"texts": tags[0],
"duration": tags[1],
"overlap": tags[2],
"seed": tags[3],
"audio_mode": tags[4],
"input_length": tags[5],
"channel": tags[6],
"sr_select": tags[7],
"model": tags[8],
"topk": tags[9],
"topp": tags[10],
"temperature": tags[11],
"cfg_coef": tags[12]
}
json_string = json.dumps(data)
if os.path.exists(filename):
with taglib.File(filename, save_on_exit=True) as song:
song.tags = {'COMMENT': json_string }
return;
def save_outputs(mp4, wav_tmp, tags):
# mp4: .mp4 file name in root running folder of app.py
# wav_tmp: temporary wav file located in %TEMP% folder
# seed - used seed
# exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
# procedure read generated .mp4 and wav files, rename it by using seed as name,
# and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
# if file with same seed number already exist its make postfix in name like seed(n)
# where is n - consiqunce number 1-2-3-4 and so on
# then we store generated mp4 and wav into destination folders.
current_date = datetime.now().strftime("%Y%m%d")
wav_directory = os.path.join(os.getcwd(), 'output', current_date,'wav')
mp4_directory = os.path.join(os.getcwd(), 'output', current_date,'mp4')
os.makedirs(wav_directory, exist_ok=True)
os.makedirs(mp4_directory, exist_ok=True)
filename = str(tags[3]) + '.wav'
target = os.path.join(wav_directory, filename)
counter = 1
while os.path.exists(target):
filename = str(tags[3]) + f'({counter})' + '.wav'
target = os.path.join(wav_directory, filename)
counter += 1
shutil.copyfile(wav_tmp, target); # make copy of original file
add_tags(target, tags);
wav_target=target;
target=target.replace('wav', 'mp4');
mp4_target=target;
mp4=r'./' +mp4;
shutil.copyfile(mp4, target); # make copy of original file
add_tags(target, tags);
return wav_target, mp4_target;
def clear_cash():
# delete all temporary files genegated my system
current_date = datetime.now().date()
current_directory = os.getcwd()
files = glob.glob(os.path.join(current_directory, '*.mp4'))
for file in files:
creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
if creation_date == current_date:
os.remove(file)
temp_directory = os.environ.get('TEMP')
files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
for file in files:
creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
if creation_date == current_date:
os.remove(file)
files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
for file in files:
creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
if creation_date == current_date:
os.remove(file)
files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
for file in files:
creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
if creation_date == current_date:
os.remove(file)
return
def predict_full(model, custom_model, base_model, prompt_amount, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
global INTERRUPTING
INTERRUPTING = False
#clear_cash();
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 trim_start < 0:
trim_start = 0
if trim_end < 0:
trim_end = 0
topk = int(topk)
if MODEL is None or MODEL.name != model:
load_model(model, custom_model, base_model)
else:
if MOVE_TO_CPU:
MODEL.to('cuda')
if seed < 0:
seed = random.randint(0, 0xffff_ffff_ffff)
torch.manual_seed(seed)
predict_full.last_upd = time.monotonic()
def _progress(generated, to_generate):
if time.monotonic() - predict_full.last_upd > 1:
progress((generated, to_generate))
predict_full.last_upd = time.monotonic()
if INTERRUPTING:
raise gr.Error("Interrupted.")
MODEL.set_custom_progress_callback(_progress)
audio_mode = "none"
melody = None
sample = None
if audio:
audio_mode = mode
if mode == "sample":
sample = audio
elif mode == "melody":
melody = audio
text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
texts = []
ind = 0
ind2 = 0
while ind < prompt_amount:
for ind2 in range(int(drag_cat[ind])):
texts.append(text_cat[ind])
ind2 = 0
ind = ind + 1
outs, outs_audio, outs_backup, input_length = _do_predictions(
[texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
tags = [str(texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model), str(topk), str(topp), str(temperature), str(cfg_coef)]
wav_target, mp4_target = save_outputs(outs[0], outs_audio[0], tags);
# Removes the temporary files.
for out in outs:
os.remove(out)
for out in outs_audio:
os.remove(out)
return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target], seed
max_textboxes = 10
def get_available_models():
return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
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 ui_full(launch_kwargs):
with gr.Blocks(title='MusicGen+', theme=theme) as interface:
gr.Markdown(
"""
# MusicGen+ V1.2.7
## An All-in-One MusicGen WebUI
## **NEW VERSION IS OUT:** https://huggingface.co/spaces/GrandaddyShmax/AudioCraft_Plus
#### **Disclaimer:** This will not run on CPU only. Its best to clone this App and run on GPU instance!
**Alternatively**, you can run this for free on a google colab:
https://colab.research.google.com/github/camenduru/MusicGen-colab/blob/main/MusicGen_ClownOfMadness_plus_colab.ipynb
**Or**, run this locally on your PC:
https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
"""
)
with gr.Tab("Text2Audio"):
with gr.Row():
with gr.Column():
with gr.Tab("Generation"):
with gr.Row():
s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompt Segments:")
with gr.Column():
textboxes = []
prompts = []
repeats = []
with gr.Row():
text0 = gr.Text(label="Input Text", interactive=True, scale=3)
prompts.append(text0)
drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
repeats.append(drag0)
for i in range(max_textboxes):
with gr.Row(visible=False) as t:
text = gr.Text(label="Input Text", interactive=True, scale=3)
repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
textboxes.append(t)
prompts.append(text)
repeats.append(repeat)
with gr.Row():
duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
with gr.Row():
overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
with gr.Row():
seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
gr.Button('\U0001f3b2\ufe0f', scale=1).style(full_width=False).click(fn=lambda: -1, outputs=[seed], queue=False)
reuse_seed = gr.Button('\u267b\ufe0f', scale=1).style(full_width=False)
with gr.Tab("Audio"):
with gr.Row():
with gr.Column():
input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
with gr.Row():
trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
trim_end = gr.Number(label="Trim End", value=0, interactive=True)
audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
with gr.Tab("Customization"):
with gr.Row():
with gr.Column():
background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
with gr.Column():
image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
with gr.Row():
height = gr.Number(label="Height", value=512, interactive=True)
width = gr.Number(label="Width", value=768, interactive=True)
with gr.Tab("Settings"):
with gr.Row():
channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
sr_select = gr.Dropdown(["11025", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
with gr.Row():
model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
with gr.Column():
dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
basemodel = gr.Radio(["small", "medium", "large"], label="Base Model", value="medium", interactive=True, scale=1)
with gr.Row():
topk = gr.Number(label="Top-k", value=250, interactive=True)
topp = gr.Number(label="Top-p", value=0, interactive=True)
temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
cfg_coef = gr.Number(label="Classifier Free Guidance", value=5.0, interactive=True)
with gr.Row():
submit = gr.Button("Generate", variant="primary")
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
with gr.Column() as c:
with gr.Tab("Output"):
output = gr.Video(label="Generated Music", scale=0)
with gr.Row():
audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
send_audio = gr.Button("Send to Input Audio")
seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
download = gr.File(label="Generated Files", interactive=False)
with gr.Tab("Wiki"):
gr.Markdown(
"""
- **[Generate (button)]:**
Generates the music with the given settings and prompts.
- **[Interrupt (button)]:**
Stops the music generation as soon as it can, providing an incomplete output.
---
### Generation Tab:
#### Multi-Prompt:
This feature allows you to control the music, adding variation to different time segments.
You have up to 10 prompt segments. the first prompt will always be 30s long
the other prompts will be [30s - overlap].
for example if the overlap is 10s, each prompt segment will be 20s.
- **[Prompt Segments (number)]:**
Amount of unique prompt to generate throughout the music generation.
- **[Prompt/Input Text (prompt)]:**
Here describe the music you wish the model to generate.
- **[Repeat (number)]:**
Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
- **[Duration (number)]:**
How long you want the generated music to be (in seconds).
- **[Overlap (number)]:**
How much each new segment will reference the previous segment (in seconds).
For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
and will generate only 10s of new music. The model can only process 30s of music.
- **[Seed (number)]:**
Your generated music id. If you wish to generate the exact same music,
place the exact seed with the exact prompts
(This way you can also extend specific song that was generated short).
- **[Random Seed (button)]:**
Gives "-1" as a seed, which counts as a random seed.
- **[Copy Previous Seed (button)]:**
Copies the seed from the output seed (if you don't feel like doing it manualy).
---
### Audio Tab:
- **[Input Type (selection)]:**
`File` mode allows you to upload an audio file to use as input
`Mic` mode allows you to use your microphone as input
- **[Input Audio Mode (selection)]:**
`Melody` mode only works with the melody model: it conditions the music generation to reference the melody
`Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
- **[Trim Start and Trim End (numbers)]:**
`Trim Start` set how much you'd like to trim the input audio from the start
`Trim End` same as the above but from the end
- **[Input Audio (audio file)]:**
Input here the audio you wish to use with "melody" or "sample" mode.
---
### Customization Tab:
- **[Background Color (color)]:**
Works only if you don't upload image. Color of the background of the waveform.
- **[Bar Color Start (color)]:**
First color of the waveform bars.
- **[Bar Color End (color)]:**
Second color of the waveform bars.
- **[Background Image (image)]:**
Background image that you wish to be attached to the generated video along with the waveform.
- **[Height and Width (numbers)]:**
Output video resolution, only works with image.
(minimum height and width is 256).
---
### Settings Tab:
- **[Output Audio Channels (selection)]:**
With this you can select the amount of channels that you wish for your output audio.
`mono` is a straightforward single channel audio
`stereo` is a dual channel audio but it will sound more or less like mono
`stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
- **[Output Audio Sample Rate (dropdown)]:**
The output audio sample rate, the model default is 32000.
- **[Model (selection)]:**
Here you can choose which model you wish to use:
`melody` model is based on the medium model with a unique feature that lets you use melody conditioning
`small` model is trained on 300M parameters
`medium` model is trained on 1.5B parameters
`large` model is trained on 3.3B parameters
`custom` model runs the custom model that you provided.
- **[Custom Model (selection)]:**
This dropdown will show you models that are placed in the `models` folder
you must select `custom` in the model options in order to use it.
- **[Refresh (button)]:**
Refreshes the dropdown list for custom model.
- **[Base Model (selection)]:**
Choose here the model that your custom model is based on.
- **[Top-k (number)]:**
is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
- **[Top-p (number)]:**
also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
- **[Temperature (number)]:**
is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
- **[Classifier Free Guidance (number)]:**
refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
"""
)
with gr.Tab("Changelog"):
gr.Markdown(
"""
## Changelog:
### V1.2.7
- When sending generated audio to Input Audio, it will send a backup audio with default settings
(best for continuos generation)
- Added Metadata to generated audio (Thanks to AlexHK ♥)
- Added Audio Info tab that will display the metadata of the input audio
- Added "send to Text2Audio" button in Audio Info tab
- Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
- Added an output area with generated files and download buttons
- Enhanced Stereo effect (Thanks to AlexHK ♥)
### V1.2.6
- Added option to generate in stereo (instead of only mono)
- Added dropdown for selecting output sample rate (model default is 32000)
### V1.2.5a
- Added file cleaner (This comes from the main facebookresearch repo)
- Reorganized a little, moved audio to a seperate tab
### V1.2.5
- Gave a unique lime theme to the webui
- Added additional output for audio only
- Added button to send generated audio to Input Audio
- Added option to trim Input Audio
### V1.2.4
- Added mic input (This comes from the main facebookresearch repo)
### V1.2.3
- Added option to change video size to fit the image you upload
### V1.2.2
- Added Wiki, Changelog and About tabs
### V1.2.1
- Added tabs and organized the entire interface
- Added option to attach image to the output video
- Added option to load fine-tuned models (Yet to be tested)
### V1.2.0
- Added Multi-Prompt
### V1.1.3
- Added customization options for generated waveform
### V1.1.2
- Removed sample length limit: now you can input audio of any length as music sample
### V1.1.1
- Improved music sample audio quality when using music continuation
### V1.1.0
- Rebuilt the repo on top of the latest structure of the main MusicGen repo
- Improved Music continuation feature
### V1.0.0 - Stable Version
- Added Music continuation
"""
)
with gr.Tab("About"):
gr.Markdown(
"""
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
---
### This project was possible thanks to:
#### GrandaddyShmax - https://github.com/GrandaddyShmax
#### Camenduru - https://github.com/camenduru
#### rkfg - https://github.com/rkfg
#### oobabooga - https://github.com/oobabooga
#### AlexHK - https://github.com/alanhk147
"""
)
with gr.Tab("Audio Info"):
with gr.Row():
with gr.Column():
in_audio = gr.File(source="upload", type="file", label="Input Any Audio", interactive=True)
send_gen = gr.Button("Send to Text2Audio", variant="primary")
with gr.Column():
info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
send_gen.click(info_to_params, inputs=[in_audio], outputs=[model, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
in_audio.change(get_audio_info, in_audio, outputs=[info])
reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
submit.click(predict_full, inputs=[model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
def variable_outputs(k):
k = int(k) - 1
return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
def get_size(image):
if image is not None:
img = Image.open(image)
img_height = img.height
img_width = img.width
if (img_height%2) != 0:
img_height = img_height + 1
if (img_width%2) != 0:
img_width = img_width + 1
return img_height, img_width
else:
return 512, 768
image.change(get_size, image, outputs=[height, width])
s.change(variable_outputs, s, textboxes)
interface.queue().launch(**launch_kwargs)
def ui_batched(launch_kwargs):
with gr.Blocks() as demo:
gr.Markdown(
"""
# MusicGen
This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
for longer sequences, more control and no queue.
"""
)
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Describe your music", lines=2, interactive=True)
with gr.Column():
radio = gr.Radio(["file", "mic"], value="file", label="Condition on a melody (optional) File or Mic")
melody = gr.Audio(source="upload", type="numpy", label="File", interactive=True, elem_id="melody-input")
with gr.Row():
submit = gr.Button("Generate")
with gr.Column():
output = gr.Video(label="Generated Music")
submit.click(predict_batched, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
gr.Examples(
fn=predict_batched,
examples=[
[
"An 80s driving pop song with heavy drums and synth pads in the background",
"./assets/bach.mp3",
],
[
"A cheerful country song with acoustic guitars",
"./assets/bolero_ravel.mp3",
],
[
"90s rock song with electric guitar and heavy drums",
None,
],
[
"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
"./assets/bach.mp3",
],
[
"lofi slow bpm electro chill with organic samples",
None,
],
],
inputs=[text, melody],
outputs=[output]
)
demo.queue(max_size=8 * 4).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()
UNLOAD_MODEL = args.unload_model
MOVE_TO_CPU = args.unload_to_cpu
if args.cache:
MODELS = {}
launch_kwargs = {}
launch_kwargs['server_name'] = 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
# Show the interface
if IS_BATCHED:
ui_batched(launch_kwargs)
else:
ui_full(launch_kwargs)