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Parent(s):
260c2a0
Added gradio app.py
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app.py
ADDED
@@ -0,0 +1,399 @@
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1 |
+
# Copyright (c) Meta Platforms, Inc. and affiliates.
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2 |
+
# All rights reserved.
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3 |
+
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+
# This source code is licensed under the license found in the
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5 |
+
# LICENSE file in the root directory of this source tree.
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+
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+
# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
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8 |
+
# also released under the MIT license.
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9 |
+
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10 |
+
import argparse
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+
from concurrent.futures import ProcessPoolExecutor
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+
import os
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+
from pathlib import Path
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+
import subprocess as sp
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+
from tempfile import NamedTemporaryFile
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+
import time
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+
import typing as tp
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+
import warnings
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19 |
+
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import torch
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+
import gradio as gr
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+
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+
from audiocraft.data.audio_utils import convert_audio
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24 |
+
from audiocraft.data.audio import audio_write
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+
from audiocraft.models import MusicGen
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+
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from demucs import pretrained
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from demucs.apply import apply_model
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from demucs.audio import convert_audio
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30 |
+
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+
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+
MODEL = None # Last used model
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+
IS_BATCHED = False
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+
MAX_BATCH_SIZE = 12
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+
BATCHED_DURATION = 15
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INTERRUPTING = False
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+
# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
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+
_old_call = sp.call
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+
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+
stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
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41 |
+
stem_idx = torch.LongTensor([stem2idx['vocal'], stem2idx['other'], stem2idx['bass']])
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+
demucs_model = pretrained.get_model('htdemucs')
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+
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+
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def _call_nostderr(*args, **kwargs):
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# Avoid ffmpeg vomitting on the logs.
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+
kwargs['stderr'] = sp.DEVNULL
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+
kwargs['stdout'] = sp.DEVNULL
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49 |
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_old_call(*args, **kwargs)
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+
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+
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sp.call = _call_nostderr
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# Preallocating the pool of processes.
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pool = ProcessPoolExecutor(4)
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pool.__enter__()
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+
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+
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def interrupt():
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global INTERRUPTING
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INTERRUPTING = True
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+
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+
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63 |
+
class FileCleaner:
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+
def __init__(self, file_lifetime: float = 3600):
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self.file_lifetime = file_lifetime
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self.files = []
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+
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def add(self, path: tp.Union[str, Path]):
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self._cleanup()
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+
self.files.append((time.time(), Path(path)))
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+
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+
def _cleanup(self):
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now = time.time()
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+
for time_added, path in list(self.files):
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+
if now - time_added > self.file_lifetime:
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+
if path.exists():
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+
path.unlink()
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78 |
+
self.files.pop(0)
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+
else:
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break
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+
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82 |
+
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83 |
+
file_cleaner = FileCleaner()
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84 |
+
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85 |
+
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86 |
+
def make_waveform(*args, **kwargs):
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87 |
+
# Further remove some warnings.
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88 |
+
be = time.time()
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89 |
+
with warnings.catch_warnings():
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90 |
+
warnings.simplefilter('ignore')
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91 |
+
out = gr.make_waveform(*args, **kwargs)
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92 |
+
print("Make a video took", time.time() - be)
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93 |
+
return out
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94 |
+
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95 |
+
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96 |
+
def load_model(version='melody'):
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97 |
+
global MODEL
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98 |
+
print("Loading model", version)
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99 |
+
if MODEL is None or MODEL.name != version:
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100 |
+
MODEL = MusicGen.get_pretrained(version)
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101 |
+
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102 |
+
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103 |
+
def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
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104 |
+
MODEL.set_generation_params(duration=duration, **gen_kwargs)
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105 |
+
print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
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106 |
+
be = time.time()
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107 |
+
processed_melodies = []
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108 |
+
target_sr = 32000
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109 |
+
target_ac = 1
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110 |
+
for melody in melodies:
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111 |
+
if melody is None:
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112 |
+
processed_melodies.append(None)
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113 |
+
else:
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114 |
+
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
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115 |
+
if melody.dim() == 1:
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116 |
+
melody = melody[None]
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117 |
+
melody = melody[..., :int(sr * duration)]
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118 |
+
melody = convert_audio(melody, sr, target_sr, target_ac)
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119 |
+
processed_melodies.append(melody)
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120 |
+
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121 |
+
if any(m is not None for m in processed_melodies):
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122 |
+
outputs = MODEL.generate_with_chroma(
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123 |
+
descriptions=texts,
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124 |
+
melody_wavs=processed_melodies,
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125 |
+
melody_sample_rate=target_sr,
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126 |
+
progress=progress,
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127 |
+
)
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128 |
+
else:
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129 |
+
outputs = MODEL.generate(texts, progress=progress)
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130 |
+
|
131 |
+
outputs = outputs.detach().cpu().float()
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132 |
+
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133 |
+
out_files = []
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134 |
+
for output in outputs:
|
135 |
+
# Demucs
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136 |
+
print("Running demucs")
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137 |
+
wav = convert_audio(output, MODEL.sample_rate, demucs_model.samplerate, demucs_model.audio_channels)
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138 |
+
wav = wav.unsqueeze(0)
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139 |
+
stems = apply_model(demucs_model, wav)
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140 |
+
stems = stems[:, stem_idx] # extract stem
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141 |
+
stems = stems.sum(1) # merge extracted stems
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142 |
+
stems = convert_audio(stems, demucs_model.samplerate, MODEL.sample_rate, 1)
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143 |
+
output = stems[0]
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144 |
+
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
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145 |
+
audio_write(
|
146 |
+
file.name, output, MODEL.sample_rate, strategy="loudness",
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147 |
+
loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
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148 |
+
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149 |
+
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150 |
+
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151 |
+
out_files.append(pool.submit(make_waveform, file.name))
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152 |
+
file_cleaner.add(file.name)
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153 |
+
res = [out_file.result() for out_file in out_files]
|
154 |
+
for file in res:
|
155 |
+
file_cleaner.add(file)
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156 |
+
print("batch finished", len(texts), time.time() - be)
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157 |
+
print("Tempfiles currently stored: ", len(file_cleaner.files))
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158 |
+
return res
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159 |
+
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160 |
+
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161 |
+
def predict_batched(texts, melodies):
|
162 |
+
max_text_length = 512
|
163 |
+
texts = [text[:max_text_length] for text in texts]
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164 |
+
load_model('melody')
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165 |
+
res = _do_predictions(texts, melodies, BATCHED_DURATION)
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166 |
+
return [res]
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167 |
+
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168 |
+
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169 |
+
def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
|
170 |
+
global INTERRUPTING
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171 |
+
INTERRUPTING = False
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172 |
+
if temperature < 0:
|
173 |
+
raise gr.Error("Temperature must be >= 0.")
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174 |
+
if topk < 0:
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175 |
+
raise gr.Error("Topk must be non-negative.")
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176 |
+
if topp < 0:
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177 |
+
raise gr.Error("Topp must be non-negative.")
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178 |
+
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179 |
+
topk = int(topk)
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180 |
+
load_model(model)
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181 |
+
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182 |
+
def _progress(generated, to_generate):
|
183 |
+
progress((generated, to_generate))
|
184 |
+
if INTERRUPTING:
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185 |
+
raise gr.Error("Interrupted.")
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186 |
+
MODEL.set_custom_progress_callback(_progress)
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187 |
+
|
188 |
+
outs = _do_predictions(
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189 |
+
[text], [melody], duration, progress=True,
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190 |
+
top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
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191 |
+
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192 |
+
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193 |
+
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194 |
+
return outs[0]
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195 |
+
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196 |
+
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197 |
+
def toggle_audio_src(choice):
|
198 |
+
if choice == "mic":
|
199 |
+
return gr.update(source="microphone", value=None, label="Microphone")
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200 |
+
else:
|
201 |
+
return gr.update(source="upload", value=None, label="File")
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202 |
+
|
203 |
+
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204 |
+
def ui_full(launch_kwargs):
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205 |
+
with gr.Blocks() as interface:
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206 |
+
gr.Markdown(
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207 |
+
"""
|
208 |
+
# MusicGen
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209 |
+
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
|
210 |
+
a simple and controllable model for music generation
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211 |
+
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
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212 |
+
"""
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213 |
+
)
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214 |
+
with gr.Row():
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215 |
+
with gr.Column():
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216 |
+
with gr.Row():
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217 |
+
text = gr.Text(label="Input Text", interactive=True)
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218 |
+
with gr.Column():
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219 |
+
radio = gr.Radio(["file", "mic"], value="file",
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220 |
+
label="Condition on a melody (optional) File or Mic")
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221 |
+
melody = gr.Audio(source="upload", type="numpy", label="File",
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222 |
+
interactive=True, elem_id="melody-input")
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223 |
+
with gr.Row():
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224 |
+
submit = gr.Button("Submit")
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225 |
+
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
|
226 |
+
_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
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227 |
+
with gr.Row():
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228 |
+
model = gr.Radio(["melody", "medium", "small", "large"],
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229 |
+
label="Model", value="melody", interactive=True)
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230 |
+
with gr.Row():
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231 |
+
duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
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232 |
+
with gr.Row():
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233 |
+
topk = gr.Number(label="Top-k", value=250, interactive=True)
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234 |
+
topp = gr.Number(label="Top-p", value=0, interactive=True)
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235 |
+
temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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236 |
+
cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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237 |
+
with gr.Column():
|
238 |
+
output = gr.Video(label="Generated Music")
|
239 |
+
submit.click(predict_full,
|
240 |
+
inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
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241 |
+
outputs=[output])
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242 |
+
radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
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243 |
+
gr.Markdown(
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244 |
+
"""
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245 |
+
### More details
|
246 |
+
|
247 |
+
The model will generate a short music extract based on the description you provided.
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248 |
+
The model can generate up to 30 seconds of audio in one pass. It is now possible
|
249 |
+
to extend the generation by feeding back the end of the previous chunk of audio.
|
250 |
+
This can take a long time, and the model might lose consistency. The model might also
|
251 |
+
decide at arbitrary positions that the song ends.
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252 |
+
|
253 |
+
**WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
|
254 |
+
An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
|
255 |
+
are generated each time.
|
256 |
+
|
257 |
+
We present 4 model variations:
|
258 |
+
1. Melody -- a music generation model capable of generating music condition
|
259 |
+
on text and melody inputs. **Note**, you can also use text only.
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260 |
+
2. Small -- a 300M transformer decoder conditioned on text only.
|
261 |
+
3. Medium -- a 1.5B transformer decoder conditioned on text only.
|
262 |
+
4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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263 |
+
|
264 |
+
When using `melody`, ou can optionaly provide a reference audio from
|
265 |
+
which a broad melody will be extracted. The model will then try to follow both
|
266 |
+
the description and melody provided.
|
267 |
+
|
268 |
+
You can also use your own GPU or a Google Colab by following the instructions on our repo.
|
269 |
+
See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
|
270 |
+
for more details.
|
271 |
+
"""
|
272 |
+
)
|
273 |
+
|
274 |
+
interface.queue().launch(**launch_kwargs)
|
275 |
+
|
276 |
+
|
277 |
+
def ui_batched(launch_kwargs):
|
278 |
+
with gr.Blocks() as demo:
|
279 |
+
gr.Markdown(
|
280 |
+
"""
|
281 |
+
# MusicGen
|
282 |
+
|
283 |
+
This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
|
284 |
+
a simple and controllable model for music generation
|
285 |
+
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
|
286 |
+
<br/>
|
287 |
+
<a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
|
288 |
+
style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
|
289 |
+
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
|
290 |
+
src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
|
291 |
+
for longer sequences, more control and no queue.</p>
|
292 |
+
"""
|
293 |
+
)
|
294 |
+
with gr.Row():
|
295 |
+
with gr.Column():
|
296 |
+
with gr.Row():
|
297 |
+
text = gr.Text(label="Describe your music", lines=2, interactive=True)
|
298 |
+
with gr.Column():
|
299 |
+
radio = gr.Radio(["file", "mic"], value="file",
|
300 |
+
label="Condition on a melody (optional) File or Mic")
|
301 |
+
melody = gr.Audio(source="upload", type="numpy", label="File",
|
302 |
+
interactive=True, elem_id="melody-input")
|
303 |
+
with gr.Row():
|
304 |
+
submit = gr.Button("Generate")
|
305 |
+
with gr.Column():
|
306 |
+
output = gr.Video(label="Generated Music")
|
307 |
+
submit.click(predict_batched, inputs=[text, melody],
|
308 |
+
outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
|
309 |
+
radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
|
310 |
+
# gr.Examples(
|
311 |
+
# fn=predict_batched,
|
312 |
+
# examples=[
|
313 |
+
# [
|
314 |
+
# "An 80s driving pop song with heavy drums and synth pads in the background",
|
315 |
+
# "./assets/bach.mp3",
|
316 |
+
# ],
|
317 |
+
# [
|
318 |
+
# "A cheerful country song with acoustic guitars",
|
319 |
+
# "./assets/bolero_ravel.mp3",
|
320 |
+
# ],
|
321 |
+
# [
|
322 |
+
# "90s rock song with electric guitar and heavy drums",
|
323 |
+
# None,
|
324 |
+
# ],
|
325 |
+
# [
|
326 |
+
# "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
|
327 |
+
# "./assets/bach.mp3",
|
328 |
+
# ],
|
329 |
+
# [
|
330 |
+
# "lofi slow bpm electro chill with organic samples",
|
331 |
+
# None,
|
332 |
+
# ],
|
333 |
+
# ],
|
334 |
+
# inputs=[text, melody],
|
335 |
+
# outputs=[output]
|
336 |
+
# )
|
337 |
+
gr.Markdown("""
|
338 |
+
### More details
|
339 |
+
|
340 |
+
The model will generate 12 seconds of audio based on the description you provided.
|
341 |
+
You can optionaly provide a reference audio from which a broad melody will be extracted.
|
342 |
+
The model will then try to follow both the description and melody provided.
|
343 |
+
All samples are generated with the `melody` model.
|
344 |
+
|
345 |
+
You can also use your own GPU or a Google Colab by following the instructions on our repo.
|
346 |
+
|
347 |
+
See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
|
348 |
+
for more details.
|
349 |
+
""")
|
350 |
+
|
351 |
+
demo.queue(max_size=8 * 4).launch(**launch_kwargs)
|
352 |
+
|
353 |
+
|
354 |
+
if __name__ == "__main__":
|
355 |
+
parser = argparse.ArgumentParser()
|
356 |
+
parser.add_argument(
|
357 |
+
'--listen',
|
358 |
+
type=str,
|
359 |
+
default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
|
360 |
+
help='IP to listen on for connections to Gradio',
|
361 |
+
)
|
362 |
+
parser.add_argument(
|
363 |
+
'--username', type=str, default='', help='Username for authentication'
|
364 |
+
)
|
365 |
+
parser.add_argument(
|
366 |
+
'--password', type=str, default='', help='Password for authentication'
|
367 |
+
)
|
368 |
+
parser.add_argument(
|
369 |
+
'--server_port',
|
370 |
+
type=int,
|
371 |
+
default=0,
|
372 |
+
help='Port to run the server listener on',
|
373 |
+
)
|
374 |
+
parser.add_argument(
|
375 |
+
'--inbrowser', action='store_true', help='Open in browser'
|
376 |
+
)
|
377 |
+
parser.add_argument(
|
378 |
+
'--share', action='store_true', help='Share the gradio UI'
|
379 |
+
)
|
380 |
+
|
381 |
+
args = parser.parse_args()
|
382 |
+
|
383 |
+
launch_kwargs = {}
|
384 |
+
launch_kwargs['server_name'] = args.listen
|
385 |
+
|
386 |
+
if args.username and args.password:
|
387 |
+
launch_kwargs['auth'] = (args.username, args.password)
|
388 |
+
if args.server_port:
|
389 |
+
launch_kwargs['server_port'] = args.server_port
|
390 |
+
if args.inbrowser:
|
391 |
+
launch_kwargs['inbrowser'] = args.inbrowser
|
392 |
+
if args.share:
|
393 |
+
launch_kwargs['share'] = args.share
|
394 |
+
|
395 |
+
# Show the interface
|
396 |
+
if IS_BATCHED:
|
397 |
+
ui_batched(launch_kwargs)
|
398 |
+
else:
|
399 |
+
ui_full(launch_kwargs)
|
setup.py
CHANGED
@@ -34,7 +34,8 @@ setup(
|
|
34 |
"soundfile",
|
35 |
"flask",
|
36 |
"flask-socketio",
|
37 |
-
"audiocraft@git+https://github.com/facebookresearch/audiocraft"
|
|
|
38 |
],
|
39 |
include_package_data=True,
|
40 |
)
|
|
|
34 |
"soundfile",
|
35 |
"flask",
|
36 |
"flask-socketio",
|
37 |
+
"audiocraft@git+https://github.com/facebookresearch/audiocraft",
|
38 |
+
"gradio"
|
39 |
],
|
40 |
include_package_data=True,
|
41 |
)
|