# 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 argparse
from concurrent.futures import ProcessPoolExecutor
import os
from pathlib import Path
import subprocess as sp
from tempfile import NamedTemporaryFile
import time
import warnings
import glob
import re
from PIL import Image
from pydub import AudioSegment
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 AudioGen, MusicGen, MultiBandDiffusion
from audiocraft.utils import ui
import random, string
version = "2.0.0a"
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
UNLOAD_MODEL = False
MOVE_TO_CPU = False
IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
print(IS_BATCHED)
MAX_BATCH_SIZE = 12
BATCHED_DURATION = 15
INTERRUPTING = False
MBD = None
# 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
]
sp.run(ffmpeg_cmd)
def _call_nostderr(*args, **kwargs):
# Avoid ffmpeg vomiting 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='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
global MODEL, MODELS
print("Loading model", version)
if MODELS is None:
if version == 'GrandaddyShmax/musicgen-custom':
MODEL = MusicGen.get_pretrained(base_model)
file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
MODEL.lm.load_state_dict(torch.load(file_path))
else:
if gen_type == "music":
MODEL = MusicGen.get_pretrained(version)
elif gen_type == "audio":
MODEL = AudioGen.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 != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
print("Loading model %s from disk" % version)
if gen_type == "music":
result = MusicGen.get_pretrained(version)
elif gen_type == "audio":
result = AudioGen.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:
if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
if not audio_path.name.endswith(".json"):
with taglib.File(audio_path.name, save_on_exit=False) as song:
if 'COMMENT' not in song.tags:
return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
json_string = song.tags['COMMENT'][0]
data = json.loads(json_string)
global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
key = str("\nKey: " + data['key']) if 'key' in data else ""
scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
if info == "":
return "No tags found. Either the file is not generated by V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
return info
else:
with open(audio_path.name) as json_file:
data = json.load(json_file)
#if 'global_prompt' not in data:
#return "No tags found. Either the file is not generated by V1.2.8a and higher or the tags are corrupted."
global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
key = str("\nKey: " + data['key']) if 'key' in data else ""
scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
if info == "":
return "No tags found. Either the file is not generated by V1.2.7 and higher or the tags are corrupted."
return info
else:
return "Only .wav ,.mp4 and .json files are supported"
else:
return None
def info_to_params(audio_path):
if audio_path is not None:
if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
if not audio_path.name.endswith(".json"):
with taglib.File(audio_path.name, save_on_exit=False) as song:
if 'COMMENT' not in song.tags:
return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
json_string = song.tags['COMMENT'][0]
data = json.loads(json_string)
struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
model = data['model'] if 'model' in data else "large"
custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
base_model = data['base_model'] if 'base_model' in data else "medium"
decoder = data['decoder'] if 'decoder' in data else "Default"
if 'texts' not in data:
unique_prompts = 1
text = ["", "", "", "", "", "", "", "", "", ""]
repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
else:
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])
audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
duration = int(data['duration']) if 'duration' in data else 10
topk = float(data['topk']) if 'topk' in data else 250
topp = float(data['topp']) if 'topp' in data else 0
temperature = float(data['temperature']) if 'temperature' in data else 1.0
cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
seed = int(data['seed']) if 'seed' in data else -1
overlap = int(data['overlap']) if 'overlap' in data else 12
channel = data['channel'] if 'channel' in data else "stereo"
sr_select = data['sr_select'] if 'sr_select' in data else "48000"
return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_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], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
else:
with open(audio_path.name) as json_file:
data = json.load(json_file)
struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
model = data['model'] if 'model' in data else "large"
custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
base_model = data['base_model'] if 'base_model' in data else "medium"
decoder = data['decoder'] if 'decoder' in data else "Default"
if 'texts' not in data:
unique_prompts = 1
text = ["", "", "", "", "", "", "", "", "", ""]
repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
else:
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])
audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
duration = int(data['duration']) if 'duration' in data else 10
topk = float(data['topk']) if 'topk' in data else 250
topp = float(data['topp']) if 'topp' in data else 0
temperature = float(data['temperature']) if 'temperature' in data else 1.0
cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
seed = int(data['seed']) if 'seed' in data else -1
overlap = int(data['overlap']) if 'overlap' in data else 12
channel = data['channel'] if 'channel' in data else "stereo"
sr_select = data['sr_select'] if 'sr_select' in data else "48000"
return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_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], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
else:
return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
else:
return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
def info_to_params_a(audio_path):
if audio_path is not None:
if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
if not audio_path.name.endswith(".json"):
with taglib.File(audio_path.name, save_on_exit=False) as song:
if 'COMMENT' not in song.tags:
return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
json_string = song.tags['COMMENT'][0]
data = json.loads(json_string)
struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
decoder = data['decoder'] if 'decoder' in data else "Default"
if 'texts' not in data:
unique_prompts = 1
text = ["", "", "", "", "", "", "", "", "", ""]
repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
else:
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])
duration = int(data['duration']) if 'duration' in data else 10
topk = float(data['topk']) if 'topk' in data else 250
topp = float(data['topp']) if 'topp' in data else 0
temperature = float(data['temperature']) if 'temperature' in data else 1.0
cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
seed = int(data['seed']) if 'seed' in data else -1
overlap = int(data['overlap']) if 'overlap' in data else 12
channel = data['channel'] if 'channel' in data else "stereo"
sr_select = data['sr_select'] if 'sr_select' in data else "48000"
return decoder, struc_prompt, global_prompt, 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], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
else:
with open(audio_path.name) as json_file:
data = json.load(json_file)
struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
decoder = data['decoder'] if 'decoder' in data else "Default"
if 'texts' not in data:
unique_prompts = 1
text = ["", "", "", "", "", "", "", "", "", ""]
repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
else:
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])
duration = int(data['duration']) if 'duration' in data else 10
topk = float(data['topk']) if 'topk' in data else 250
topp = float(data['topp']) if 'topp' in data else 0
temperature = float(data['temperature']) if 'temperature' in data else 1.0
cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
seed = int(data['seed']) if 'seed' in data else -1
overlap = int(data['overlap']) if 'overlap' in data else 12
channel = data['channel'] if 'channel' in data else "stereo"
sr_select = data['sr_select'] if 'sr_select' in data else "48000"
return decoder, struc_prompt, global_prompt, 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], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
else:
return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
else:
return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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 load_diffusion():
global MBD
if MBD is None:
print("loading MBD")
MBD = MultiBandDiffusion.get_mbd_
()
def unload_diffusion():
global MBD
if MBD is not None:
print("unloading MBD")
MBD = None
def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
if gen_type == "music":
maximum_size = 29.5
elif gen_type == "audio":
maximum_size = 9.5
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 = []
if gen_type == "music":
target_sr = 32000
elif gen_type == "audio":
target_sr = 16000
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:
if gen_type == "music":
outputs = MODEL.generate_continuation(
prompt=sampleM,
prompt_sample_rate=globalSR,
descriptions=texts,
progress=progress,
return_tokens=USE_DIFFUSION
)
elif gen_type == "audio":
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)
if gen_type == "music":
outputs = MODEL.generate_continuation(
prompt=sampleM,
prompt_sample_rate=globalSR,
descriptions=texts,
progress=progress,
return_tokens=USE_DIFFUSION
)
elif gen_type == "audio":
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):
if gen_type == "music":
outputs = MODEL.generate_with_chroma(
descriptions=texts,
melody_wavs=processed_melodies,
melody_sample_rate=target_sr,
progress=progress,
return_tokens=USE_DIFFUSION
)
elif gen_type == "audio":
outputs = MODEL.generate_with_chroma(
descriptions=texts,
melody_wavs=processed_melodies,
melody_sample_rate=target_sr,
progress=progress
)
else:
if gen_type == "music":
outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
elif gen_type == "audio":
outputs = MODEL.generate(texts, progress=progress)
if USE_DIFFUSION:
print("outputs: " + str(outputs))
outputs_diffusion = MBD.tokens_to_wav(outputs[1])
outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
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_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))
out_audios.append(file.name)
file_cleaner.add(file.name)
print(f'wav: {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(f'video: {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 = {
"global_prompt": tags[0],
"bpm": tags[1],
"key": tags[2],
"scale": tags[3],
"texts": tags[4],
"duration": tags[5],
"overlap": tags[6],
"seed": tags[7],
"audio_mode": tags[8],
"input_length": tags[9],
"channel": tags[10],
"sr_select": tags[11],
"model": tags[12],
"custom_model": tags[13],
"base_model": tags[14],
"decoder": tags[15],
"topk": tags[16],
"topp": tags[17],
"temperature": tags[18],
"cfg_coef": tags[19],
"generator": tags[20],
"version": version
}
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 }
json_file = open(tags[7] + '.json', 'w')
json_file.write(json_string)
json_file.close()
return json_file.name;
def save_outputs(mp4, wav_tmp, tags, gen_type):
# 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, gen_type,'wav')
mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
os.makedirs(wav_directory, exist_ok=True)
os.makedirs(mp4_directory, exist_ok=True)
os.makedirs(json_directory, exist_ok=True)
filename = str(tags[7]) + '.wav'
target = os.path.join(wav_directory, filename)
counter = 1
while os.path.exists(target):
filename = str(tags[7]) + f'({counter})' + '.wav'
target = os.path.join(wav_directory, filename)
counter += 1
shutil.copyfile(wav_tmp, target); # make copy of original file
json_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);
target=target.replace('mp4', 'json'); # change the extension to json
json_target=target; # store the json target
with open(target, 'w') as f: # open a writable file object
shutil.copyfile(json_file, target); # make copy of original file
os.remove(json_file)
return wav_target, mp4_target, json_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 s2t(seconds, seconds2):
# convert seconds to time format
# seconds - time in seconds
# return time in format 00:00
m, s = divmod(seconds, 60)
m2, s2 = divmod(seconds2, 60)
if seconds != 0 and seconds < seconds2:
s = s + 1
return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
# calculate the time of generation
# overlap - overlap in seconds
# d0-d9 - drag
# return time in seconds
d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
calc = []
tracks = []
time = 0
s = s - 1
max_time = duration
max_limit = 0
if gen_type == "music":
max_limit = 30
elif gen_type == "audio":
max_limit = 10
track_add = max_limit - overlap
tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
for i in range(1, 10):
tracks.append(d_amount[i] * track_add)
if tracks[0] >= max_time or s == 0:
calc.append(s2t(time, max_time))
time = max_time
else:
calc.append(s2t(time, tracks[0]))
time = tracks[0]
for i in range(1, 10):
if time + tracks[i] >= max_time or i == s:
calc.append(s2t(time, max_time))
time = max_time
else:
calc.append(s2t(time, time + tracks[i]))
time = time + tracks[i]
return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, 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
global USE_DIFFUSION
INTERRUPTING = False
if gen_type == "audio":
custom_model = None
base_model = "medium"
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 decoder == "MultiBand_Diffusion":
USE_DIFFUSION = True
load_diffusion()
else:
USE_DIFFUSION = False
unload_diffusion()
if gen_type == "music":
model_shrt = model
model = "GrandaddyShmax/-" + model
elif gen_type == "audio":
model_shrt = model
model = "GrandaddyShmax/audiogen-" + model
base_model_shrt = base_model
base_model = "GrandaddyShmax/-" + base_model
if MODEL is None or MODEL.name != (model):
load_model(model, custom_model, base_model, gen_type)
else:
if MOVE_TO_CPU:
MODEL.to('cuda')
if seed < 0:
seed = random.randint(0, 0xffff_ffff_ffff)
torch.manual_seed(seed)
def _progress(generated, to_generate):
progress((min(generated, to_generate), to_generate))
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
base_model = "none" if model != "custom" else base_model
custom_model = "none" if model != "custom" else custom_model
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 = []
raw_texts = []
ind = 0
ind2 = 0
while ind < prompt_amount:
for ind2 in range(int(drag_cat[ind])):
if not struc_prompt:
texts.append(text_cat[ind])
global_prompt = "none"
bpm = "none"
key = "none"
scale = "none"
raw_texts.append(text_cat[ind])
else:
if gen_type == "music":
bpm_str = str(bpm) + " bpm"
key_str = ", " + str(key) + " " + str(scale)
global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
elif gen_type == "audio":
bpm_str = ""
key_str = ""
global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
texts.append(bpm_str + key_str + global_str + texts_str)
raw_texts.append(text_cat[ind])
ind2 = 0
ind = ind + 1
outs, outs_audio, outs_backup, input_length = _do_predictions(
gen_type, [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(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
# 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, json_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='TulipAI's culturaFX', theme=theme) as interface:
gr.Markdown(
"""
# TulipAI's culturaFX
###
An AI audio platform, that transforms user prompts into dynamic, cultural soundscapes and narratives. Designed for Gen Z content creators, gamers, sound designers, and podcast producers, our platform offers personalized, ethically sourced audio experiences.
"""
)
with gr.Tab("Generate"):
gr.Markdown(
"""
### culturaFX
Check the "Wiki" to learn how to take the most out of TulipAI culturaFX Sound Effects Generation Tool.
"""
)
with gr.Tab("Generate Sound Effects"):
with gr.Row():
#with gr.Column():
with gr.Tab("Generation"):
with gr.Column():
textboxes_a = []
prompts_a = []
repeats_a = []
calcs_a = []
with gr.Row():
text0_a = gr.Text(label="Global Prompt", interactive=True, scale=4)
prompts_a.append(text0_a)
drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
repeats_a.append(drag0_a)
calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
calcs_a.append(calc0_a)
with gr.Accordion("Structured Prompt (Optional)", open=False):
with gr.Row():
struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
#global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
global_prompt_a = text0_a
with gr.Row():
s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
for i in range(max_textboxes):
with gr.Row(visible=False) as t_a:
text_a = gr.Text(label="Input Text", interactive=True, scale=3)
repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
textboxes_a.append(t_a)
prompts_a.append(text_a)
repeats_a.append(repeat_a)
calcs_a.append(calc_a)
overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
to_calc_a = gr.Button("Calculate Timings", variant="secondary")
with gr.Row():
duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
with gr.Row():
seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
with gr.Tab("Audio"):
with gr.Row():
with gr.Column():
input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
with gr.Row():
trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
with gr.Tab("Customization"):
with gr.Row():
with gr.Column():
background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
with gr.Column():
image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
with gr.Row():
height_a = gr.Number(label="Height", value=512, interactive=True)
width_a = gr.Number(label="Width", value=768, interactive=True)
with gr.Tab("Settings"):
with gr.Row():
channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
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", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
with gr.Row():
struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
with gr.Row():
model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
with gr.Row():
topk_a = gr.Number(label="Top-k", value=250, interactive=True)
topp_a = gr.Number(label="Top-p", value=0, interactive=True)
temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
with gr.Row():
submit_a = gr.Button("Generate", variant="primary")
_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
with gr.Row():
with gr.Tab("Output"):
output_a = gr.Video(label="Generated Audio", scale=0)
with gr.Row():
audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
send_audio_a = gr.Button("Send to Input Audio")
seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
download_a = gr.File(label="Generated Files", interactive=False)
with gr.Tab("Wiki"):
gr.Markdown(
"""
- **[Generate (button)]:**
Generates the audio with the given settings and prompts.
- **[Interrupt (button)]:**
Stops the audio generation as soon as it can, providing an incomplete output.
---
### Generation Tab:
#### Structure Prompts:
This feature helps reduce repetetive prompts by allowing you to set global prompts
that will be used for all prompt segments.
- **[Structure Prompts (checkbox)]:**
Enable/Disable the structure prompts feature.
- **[Global Prompt (text)]:**
Here write the prompt that you wish to be used for all prompt segments.
#### Multi-Prompt:
This feature allows you to control the audio, adding variation to different time segments.
You have up to 10 prompt segments. the first prompt will always be 10s long
the other prompts will be [10s - overlap].
for example if the overlap is 2s, each prompt segment will be 8s.
- **[Prompt Segments (number)]:**
Amount of unique prompt to generate throughout the audio generation.
- **[Prompt/Input Text (prompt)]:**
Here describe the audio 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).
- **[Time (text)]:**
The time of the prompt segment.
- **[Calculate Timings (button)]:**
Calculates the timings of the prompt segments.
- **[Duration (number)]:**
How long you want the generated audio to be (in seconds).
- **[Overlap (number)]:**
How much each new segment will reference the previous segment (in seconds).
For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
and will generate only 8s of new audio. The model can only process 10s of music.
- **[Seed (number)]:**
Your generated audio id. If you wish to generate the exact same audio,
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
- **[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.
---
### 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.
- **[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("MusicGen"):
gr.Markdown(
"""
### MusicGen
Check the "Wiki" to learn how to take the most out of TulipAI Soundscapes Music Generation Tool.
"""
)
with gr.Tab("Generate Music"):
with gr.Row():
with gr.Column():
with gr.Tab("Generation"):
with gr.Accordion("Structure Prompts", open=False):
with gr.Column():
with gr.Row():
struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
with gr.Row():
global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
with gr.Row():
s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
#s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
with gr.Column():
textboxes = []
prompts = []
repeats = []
calcs = []
with gr.Row():
text0 = gr.Text(label="Input Text", interactive=True, scale=4)
prompts.append(text0)
drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
repeats.append(drag0)
calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
calcs.append(calc0)
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)
calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
textboxes.append(t)
prompts.append(text)
repeats.append(repeat)
calcs.append(calc)
to_calc = gr.Button("Calculate Timings", variant="secondary")
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).click(fn=lambda: -1, outputs=[seed], queue=False)
reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
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", "16000", "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", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
with gr.Row():
decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
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=3.0, 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:
#### Structure Prompts:
This feature helps reduce repetetive prompts by allowing you to set global prompts
that will be used for all prompt segments.
- **[Structure Prompts (checkbox)]:**
Enable/Disable the structure prompts feature.
- **[BPM (number)]:**
Beats per minute of the generated music.
- **[Key (dropdown)]:**
The key of the generated music.
- **[Scale (dropdown)]:**
The scale of the generated music.
- **[Global Prompt (text)]:**
Here write the prompt that you wish to be used for all prompt segments.
#### 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).
- **[Time (text)]:**
The time of the prompt segment.
- **[Calculate Timings (button)]:**
Calculates the timings of the prompt segments.
- **[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.
- **[Decoder (selection)]:**
Choose here the decoder that you wish to use:
`Default` is the default decoder
`MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
- **[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("Audio Info"):
gr.Markdown(
"""
### Audio Info
"""
)
with gr.Row():
with gr.Column():
in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
with gr.Row():
send_gen = gr.Button("Send to MusicGen", variant="primary")
send_gen_a = gr.Button("Send to AudioGen", variant="primary")
with gr.Column():
info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
with gr.Tab("About"):
with gr.Row():
with gr.Column():
gen_type = gr.Text(value="music", interactive=False, visible=False)
gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
gr.Markdown(
"""
# Soundscapes by TulipAI
Welcome to Soundscapes - TulipAI’s flagship Audio Storytelling Toolkit. Designed with modern content creators in mind, our AI-driven platform generates audio sound effects in just minutes tailored to your unique needs.
## PERFECT FOR:
- Podcasters aiming to immerse their listeners.
- Audiobooks sound engineers
- Audio engineers seeking that elusive sound.
- Producers wanting to enrich their auditory experience.
- Sound designers craving innovative tools.
- YouTubers desiring to elevate their content.
"""
)
with gr.Column():
#gr.Image(shape=(5,5))
gr.Image(shape=(5,5), value = "https://tulipai.co/assets/images/image01.png")
send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, 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], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
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=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, 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)
to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)'''
gen_type = gr.Text(value="music", interactive=False, visible=False)
gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
#send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=True)
to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
#in_audio.change(get_audio_info, in_audio, outputs=[info])
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])
#image_a.change(get_size, image_a, outputs=[height_a, width_a])
#s.change(variable_outputs, s, textboxes)
s_a.change(variable_outputs, s_a, textboxes_a)
interface.queue().launch(**launch_kwargs)
#interface.queue().launch(share=True)
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")
audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
submit.click(predict_batched, inputs=[text, melody],
outputs=[output, audio_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]
)
gr.Markdown("""
### More details
The model will generate 12 seconds of audio based on the description you provided.
You can optionally provide a reference audio from which a broad melody will be extracted.
The model will then try to follow both the description and melody provided.
All samples are generated with the `melody` model.
You can also use your own GPU or a Google Colab by following the instructions on our repo.
See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
for more details.
""")
demo.queue(max_size=8 * 4).launch(**launch_kwargs)
#demo.queue().launch(share=True)
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:
global USE_DIFFUSION
USE_DIFFUSION = False
ui_batched(launch_kwargs)
else:
ui_full(launch_kwargs)