"""
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 random
from tempfile import NamedTemporaryFile
import argparse
import time
import torch
import gradio as gr
import os
import numpy as np
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
from audiocraft.data.audio_utils import convert_audio
import subprocess, random, string
MODEL = None
IS_SHARED_SPACE = "musicgen/MusicGen" in os.environ.get('SPACE_ID', '')
INTERRUPTED = False
UNLOAD_MODEL = False
def interrupt():
global INTERRUPTED
INTERRUPTED = True
print('Interrupted!')
def generate_random_string(length):
characters = string.ascii_letters + string.digits
return ''.join(random.choice(characters) for _ in range(length))
def resize_video(input_path, output_path, target_width, target_height):
ffmpeg_cmd = [
'ffmpeg',
'-y',
'-i', input_path,
'-vf', f'scale={target_width}:{target_height}',
'-c:a', 'copy',
output_path
]
subprocess.run(ffmpeg_cmd)
def load_model(version):
print("Loading model", version)
return MusicGen.get_pretrained(version)
def predict(model, text, melody, sample, duration, topk, topp, temperature, cfg_coef, seed, overlap=5, recondition=True, background="./assets/background.png", progress=gr.Progress()):
global MODEL
global INTERRUPTED
INTERRUPTED = False
topk = int(topk)
if MODEL is None or MODEL.name != model:
MODEL = load_model(model)
if duration > MODEL.lm.cfg.dataset.segment_duration and melody is not None:
raise gr.Error("Generating music longer than 30 seconds with melody conditioning is not yet supported!")
output = None
first_chunk = None
total_samples = duration * 50 + 3
segment_duration = duration
if seed < 0:
seed = random.randint(0, 0xffff_ffff_ffff)
torch.manual_seed(seed)
predict.last_progress_update = time.monotonic()
while duration > 0:
if INTERRUPTED:
break
if output is None: # first pass of long or short song
if segment_duration > MODEL.lm.cfg.dataset.segment_duration:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
else:
segment_duration = duration
else: # next pass of long song
if duration + overlap < MODEL.lm.cfg.dataset.segment_duration:
segment_duration = duration + overlap
else:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
print(f'Segment duration: {segment_duration}, duration: {duration}, overlap: {overlap}')
MODEL.set_generation_params(
use_sampling=True,
top_k=topk,
top_p=topp,
temperature=temperature,
cfg_coef=cfg_coef,
duration=segment_duration,
)
def updateProgress(step: int, total: int):
now = time.monotonic()
if now - predict.last_progress_update > 1:
progress((total_samples - duration * 50 - 3 + step, total_samples))
predict.last_progress_update = now
if sample:
def normalize_audio(audio_data):
audio_data = audio_data.astype(np.float32)
max_value = np.max(np.abs(audio_data))
audio_data = audio_data / max_value
return audio_data
globalSR, sampleM = sample[0], sample[1]
sampleM = normalize_audio(sampleM)
sampleM = torch.from_numpy(sampleM).t()
if sampleM.dim() > 1:
sampleM = convert_audio(sampleM, globalSR, 32000, 1)
sampleM = sampleM.to(MODEL.device).float().unsqueeze(0)
if sampleM.dim() == 2:
sampleM = sampleM[None]
sample_length = sampleM.shape[sampleM.dim() - 1] / 32000
if output is None:
next_segment = sampleM
duration -= sample_length
else:
if first_chunk is None and MODEL.name == "melody" and recondition:
first_chunk = output[:, :,
:MODEL.lm.cfg.dataset.segment_duration*MODEL.sample_rate]
last_chunk = output[:, :, -overlap*32000:]
next_segment = MODEL.generate_continuation(last_chunk,
32000, descriptions=[text], progress=updateProgress,
melody_wavs=(first_chunk), resample=False)
duration -= segment_duration - overlap
elif melody:
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
print(melody.shape)
if melody.dim() == 2:
melody = melody[None]
melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
next_segment = MODEL.generate_with_chroma(
descriptions=[text],
melody_wavs=melody,
melody_sample_rate=sr,
progress=updateProgress
)
duration -= segment_duration
else:
if output is None:
next_segment = MODEL.generate(descriptions=[text],
progress=updateProgress)
duration -= segment_duration
else:
if first_chunk is None and MODEL.name == "melody" and recondition:
first_chunk = output[:, :,
:MODEL.lm.cfg.dataset.segment_duration*MODEL.sample_rate]
last_chunk = output[:, :, -overlap*MODEL.sample_rate:]
next_segment = MODEL.generate_continuation(last_chunk,
MODEL.sample_rate, descriptions=[text],
progress=updateProgress, melody_wavs=(first_chunk), resample=False)
duration -= segment_duration - overlap
if output is None:
output = next_segment
else:
output = torch.cat([output[:, :, :-overlap*MODEL.sample_rate], next_segment], 2)
output = output.detach().cpu().float()[0]
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)
waveform_video = gr.make_waveform(file.name, bg_image=background, bg_color="#21b0fe" , bars_color=('#fe218b', '#fed700'), fg_alpha=1.0, bar_count=75)
if background is None or len(background) == 0:
random_string = generate_random_string(12)
random_string = f"{random_string}.mp4"
resize_video(waveform_video, random_string, 900, 300)
waveform_video = random_string
global UNLOAD_MODEL
if UNLOAD_MODEL:
MODEL = None
torch.cuda.empty_cache()
return waveform_video, seed
def ui(**kwargs):
with gr.Blocks() as interface:
gr.Markdown(
"""
# MusicGen
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://arxiv.org/abs/2306.05284)
"""
)
if IS_SHARED_SPACE:
gr.Markdown("""
⚠ This Space doesn't work in this shared UI ⚠
<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>
to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
""")
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Input Text", interactive=True)
melody = gr.Audio(source="upload", type="numpy", label="Melody Condition (optional)", interactive=True)
sample = gr.Audio(source="upload", type="numpy", label="Music Sample (optional)", interactive=True)
with gr.Row():
submit = gr.Button("Generate", variant="primary")
gr.Button("Interrupt").click(fn=interrupt, queue=False)
with gr.Row():
background = gr.Image(source="upload", label="Background", type="filepath", interactive=True)
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=300, value=10, step=1, label="Duration", interactive=True)
with gr.Row():
overlap = gr.Slider(minimum=1, maximum=29, value=5, step=1, label="Overlap", interactive=True)
recondition = gr.Checkbox(False, label='Condition next chunks with the first chunk')
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():
seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
gr.Button('\U0001f3b2\ufe0f').style(full_width=False).click(fn=lambda: -1, outputs=[seed], queue=False)
reuse_seed = gr.Button('\u267b\ufe0f').style(full_width=False)
with gr.Column() as c:
output = gr.Video(label="Generated Music")
seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
submit.click(predict, inputs=[model, text, melody, sample, duration, topk, topp, temperature, cfg_coef, seed, overlap, recondition, background], outputs=[output, seed_used])
def update_recondition(name: str):
enabled = name == 'melody'
return recondition.update(interactive=enabled, value=None if enabled else False)
model.change(fn=update_recondition, inputs=[model], outputs=[recondition])
gr.Examples(
fn=predict,
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(
"""
### More details
The model will generate a short music extract based on the description you provided.
You can generate up to 30 seconds of audio.
We present 4 model variations:
1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
2. Small -- a 300M transformer decoder conditioned on text only.
3. Medium -- a 1.5B transformer decoder conditioned on text only.
4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
When using `melody`, ou 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.
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.
"""
)
# Show the interface
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
interface.queue().launch(**launch_kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen',
type=str,
default='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'
)
args = parser.parse_args()
UNLOAD_MODEL = args.unload_model
ui(
username=args.username,
password=args.password,
inbrowser=args.inbrowser,
server_port=args.server_port,
share=args.share,
listen=args.listen
)