|
""" |
|
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. |
|
""" |
|
|
|
import argparse |
|
from concurrent.futures import ProcessPoolExecutor |
|
import subprocess as sp |
|
from tempfile import NamedTemporaryFile |
|
import time |
|
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 |
|
|
|
_old_call = sp.call |
|
|
|
|
|
def _call_nostderr(*args, **kwargs): |
|
|
|
kwargs['stderr'] = sp.DEVNULL |
|
kwargs['stdout'] = sp.DEVNULL |
|
_old_call(*args, **kwargs) |
|
|
|
|
|
sp.call = _call_nostderr |
|
pool = ProcessPoolExecutor(3) |
|
pool.__enter__() |
|
|
|
|
|
def make_waveform(*args, **kwargs): |
|
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(): |
|
print("Loading model") |
|
return MusicGen.get_pretrained("melody") |
|
|
|
|
|
def predict(texts, melodies): |
|
global MODEL |
|
if MODEL is None: |
|
MODEL = load_model() |
|
|
|
duration = 12 |
|
max_text_length = 512 |
|
texts = [text[:max_text_length] for text in texts] |
|
MODEL.set_generation_params(duration=duration) |
|
|
|
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) |
|
|
|
outputs = MODEL.generate_with_chroma( |
|
descriptions=texts, |
|
melody_wavs=processed_melodies, |
|
melody_sample_rate=target_sr, |
|
progress=False |
|
) |
|
|
|
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)) |
|
res = [[out_file.result() for out_file in out_files]] |
|
print("batch finished", len(texts), time.time() - be) |
|
return res |
|
|
|
|
|
def ui(**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). |
|
<br/> |
|
<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"> |
|
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> |
|
for longer sequences, more control and no queue.</p> |
|
""" |
|
) |
|
with gr.Row(): |
|
with gr.Column(): |
|
with gr.Row(): |
|
text = gr.Text(label="Describe your music", lines=2, interactive=True) |
|
melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True) |
|
with gr.Row(): |
|
submit = gr.Button("Generate") |
|
with gr.Column(): |
|
output = gr.Video(label="Generated Music") |
|
submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8) |
|
gr.Examples( |
|
fn=predict, |
|
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 optionaly 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. |
|
""") |
|
|
|
|
|
launch_kwargs = {} |
|
username = kwargs.get('username') |
|
password = kwargs.get('password') |
|
server_port = kwargs.get('server_port', 0) |
|
inbrowser = kwargs.get('inbrowser', False) |
|
share = kwargs.get('share', False) |
|
server_name = kwargs.get('listen') |
|
|
|
launch_kwargs['server_name'] = server_name |
|
|
|
if username and password: |
|
launch_kwargs['auth'] = (username, password) |
|
if server_port > 0: |
|
launch_kwargs['server_port'] = server_port |
|
if inbrowser: |
|
launch_kwargs['inbrowser'] = inbrowser |
|
if share: |
|
launch_kwargs['share'] = share |
|
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', |
|
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() |
|
|
|
ui( |
|
username=args.username, |
|
password=args.password, |
|
inbrowser=args.inbrowser, |
|
server_port=args.server_port, |
|
share=args.share, |
|
listen=args.listen |
|
) |
|
|
