# 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 typing as tp
import warnings
import torch
import gradio as gr
from audiocraft.data.audio_utils import convert_audio
from audiocraft.data.audio import audio_write
from audiocraft.models import MusicGen
MODEL = None # Last used model
IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
MAX_BATCH_SIZE = 6
BATCHED_DURATION = 15
INTERRUPTING = False
# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
_old_call = sp.call
def _call_nostderr(*args, **kwargs):
# Avoid ffmpeg vomitting on the logs.
kwargs['stderr'] = sp.DEVNULL
kwargs['stdout'] = sp.DEVNULL
_old_call(*args, **kwargs)
sp.call = _call_nostderr
# Preallocating the pool of processes.
pool = ProcessPoolExecutor(3)
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')
out = gr.make_waveform(*args, **kwargs)
print("Make a video took", time.time() - be)
return out
def load_model(version='melody'):
global MODEL
print("Loading model", version)
if MODEL is None or MODEL.name != version:
MODEL = MusicGen.get_pretrained(version)
def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
MODEL.set_generation_params(duration=duration, **gen_kwargs)
print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
be = time.time()
processed_melodies = []
target_sr = 32000
target_ac = 1
for melody in melodies:
if melody is None:
processed_melodies.append(None)
else:
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
if melody.dim() == 1:
melody = melody[None]
melody = melody[..., :int(sr * duration)]
melody = convert_audio(melody, sr, target_sr, target_ac)
processed_melodies.append(melody)
if any(m is not None for m in processed_melodies):
outputs = MODEL.generate_with_chroma(
descriptions=texts,
melody_wavs=processed_melodies,
melody_sample_rate=target_sr,
progress=progress,
)
else:
outputs = MODEL.generate(texts, progress=progress)
outputs = outputs.detach().cpu().float()
out_files = []
for output in outputs:
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
audio_write(
file.name, output, MODEL.sample_rate, strategy="loudness",
loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
out_files.append(pool.submit(make_waveform, file.name))
file_cleaner.add(file.name)
res = [out_file.result() for out_file in out_files]
for file in res:
file_cleaner.add(file)
print("batch finished", len(texts), time.time() - be)
print("Tempfiles currently stored: ", len(file_cleaner.files))
return res
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 predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
global INTERRUPTING
INTERRUPTING = False
if temperature < 0:
raise gr.Error("Temperature must be >= 0.")
if topk < 0:
raise gr.Error("Topk must be non-negative.")
if topp < 0:
raise gr.Error("Topp must be non-negative.")
topk = int(topk)
load_model(model)
def _progress(generated, to_generate):
progress((generated, to_generate))
if INTERRUPTING:
raise gr.Error("Прервано.")
MODEL.set_custom_progress_callback(_progress)
outs = _do_predictions(
[text], [melody], duration, progress=True,
top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
return outs[0]
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() as interface:
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Input Text", 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("Submit")
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
_ = gr.Button("Прервать").click(fn=interrupt, queue=False)
with gr.Row():
model = gr.Radio(["melody", "medium", "small", "large"],
label="Model", value="melody", interactive=True)
with gr.Row():
duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", 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.Column():
output = gr.Video(label="Generated Music")
submit.click(predict_full,
inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
outputs=[output])
radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
gr.Examples(
fn=predict_full,
examples=[
[
"An 80s driving pop song with heavy drums and synth pads in the background",
"./assets/bach.mp3",
"melody"
],
[
"A cheerful country song with acoustic guitars",
"./assets/bolero_ravel.mp3",
"melody"
],
[
"90s rock song with electric guitar and heavy drums",
None,
"medium"
],
[
"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
"./assets/bach.mp3",
"melody"
],
[
"lofi slow bpm electro chill with organic samples",
None,
"medium",
],
],
inputs=[text, melody, model],
outputs=[output]
)
gr.Markdown(
"""
### Информация
Модель сгенерирует короткий музыкальный фрагмент на основе предоставленного вами описания.
Модель может генерировать до 30 секунд звука за один проход. Теперь можно продлить генерацию, вернув конец предыдущего фрагмента аудио.
Это может занять много времени, и модель может потерять согласованность. Модель также может решить в произвольных позициях, что песня заканчивается.
**ПРЕДУПРЕЖДЕНИЕ:** При выборе длительности генерация займет много времени (от 2 минут, до ~10 минут).
С ранее сгенерированным фрагментом сохраняется перекрытие в 12 секунд, и каждый раз генерируется 18 "новых" секунд.
Модели:
1. Melody -- модель генерации музыки, способная генерировать музыкальное состояни на текстовых и мелодических входах. **Примечание**, вы также можете использовать только текст.
2. Small -- 300-метровый трансформаторный декодер, работающий только с текстом.
3. Medium -- трансформаторный декодер 1,5В, работающий только с текстом.
4. Large -- преобразовательный декодер 3.3B, работающий только с текстом (может работать для самых длинных последовательностей).
При использовании `Melody` вы можете дополнительно предоставить эталонный звук, из которого будет извлечена общая мелодия. Затем модель попытается следовать как описанию, так и предоставленной мелодии.
"""
)
interface.queue().launch(**launch_kwargs)
def ui_batched(launch_kwargs):
with gr.Blocks() as demo:
gr.Markdown(
"""
# MusicGen
This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
for longer sequences, more control and no queue.
"""
)
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Describe your music", lines=2, interactive=True)
with gr.Column():
radio = gr.Radio(["file", "mic"], value="file",
label="Condition on a melody (optional) File or Mic")
melody = gr.Audio(source="upload", type="numpy", label="File",
interactive=True, elem_id="melody-input")
with gr.Row():
submit = gr.Button("Generate")
with gr.Column():
output = gr.Video(label="Generated Music")
submit.click(predict_batched, inputs=[text, melody],
outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
gr.Examples(
fn=predict_batched,
examples=[
[
"An 80s driving pop song with heavy drums and synth pads in the background",
"./assets/bach.mp3",
],
[
"A cheerful country song with acoustic guitars",
"./assets/bolero_ravel.mp3",
],
[
"90s rock song with electric guitar and heavy drums",
None,
],
[
"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
"./assets/bach.mp3",
],
[
"lofi slow bpm electro chill with organic samples",
None,
],
],
inputs=[text, melody],
outputs=[output]
)
demo.queue(max_size=8 * 4).launch(**launch_kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen',
type=str,
default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
help='IP to listen on for connections to Gradio',
)
parser.add_argument(
'--username', type=str, default='', help='Username for authentication'
)
parser.add_argument(
'--password', type=str, default='', help='Password for authentication'
)
parser.add_argument(
'--server_port',
type=int,
default=0,
help='Port to run the server listener on',
)
parser.add_argument(
'--inbrowser', action='store_true', help='Open in browser'
)
parser.add_argument(
'--share', action='store_true', help='Share the gradio UI'
)
args = parser.parse_args()
launch_kwargs = {}
launch_kwargs['server_name'] = args.listen
if args.username and args.password:
launch_kwargs['auth'] = (args.username, args.password)
if args.server_port:
launch_kwargs['server_port'] = args.server_port
if args.inbrowser:
launch_kwargs['inbrowser'] = args.inbrowser
if args.share:
launch_kwargs['share'] = args.share
# Show the interface
if IS_BATCHED:
ui_batched(launch_kwargs)
else:
ui_full(launch_kwargs)