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audiogen_app.py #185

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249 changes: 249 additions & 0 deletions demos/audiogen_app.py
Original file line number Diff line number Diff line change
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# 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 AudioGen, MultiBandDiffusion


MODEL = None # Last used model
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 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')
out = gr.make_waveform(*args, **kwargs)
print("Make a video took", time.time() - be)
return out


def load_model(version='facebook/audiogen-medium'):
global MODEL
print("Loading model", version)
if MODEL is None or MODEL.name != version:
MODEL = AudioGen.get_pretrained(version)


def load_diffusion():
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We don't have a MultiBandDiffusion model for AudioGen, let's remove this bit from the demo for the merge?

global MBD
print("loading MBD")
MBD = MultiBandDiffusion.get_mbd_musicgen()


def _do_predictions(texts, duration, progress=False, **gen_kwargs):
MODEL.set_generation_params(duration=duration, **gen_kwargs)
be = time.time()
target_sr = 32000
target_ac = 1

outputs = MODEL.generate(texts, progress=progress)
if USE_DIFFUSION:
outputs_diffusion = MBD.tokens_to_wav(outputs[1])
outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
outputs = outputs.detach().cpu().float()
pending_videos = []
out_wavs = []
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)
pending_videos.append(pool.submit(make_waveform, file.name))
out_wavs.append(file.name)
file_cleaner.add(file.name)
out_videos = [pending_video.result() for pending_video in pending_videos]
for video in out_videos:
file_cleaner.add(video)
print("batch finished", len(texts), time.time() - be)
print("Tempfiles currently stored: ", len(file_cleaner.files))
return out_videos, out_wavs



def predict_full(model, decoder, text, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
global INTERRUPTING
global USE_DIFFUSION
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)
if decoder == "MultiBand_Diffusion":
USE_DIFFUSION = True
load_diffusion()
else:
USE_DIFFUSION = False
load_model(model)

def _progress(generated, to_generate):
progress((min(generated, to_generate), to_generate))
if INTERRUPTING:
raise gr.Error("Interrupted.")
MODEL.set_custom_progress_callback(_progress)

videos, wavs = _do_predictions(
[text], duration, progress=True,
top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
if USE_DIFFUSION:
return videos[0], wavs[0], videos[1], wavs[1]
return videos[0], wavs[0], None, None
return videos[0], wavs[0]



def toggle_diffusion(choice):
if choice == "MultiBand_Diffusion":
return [gr.update(visible=True)] * 2
else:
return [gr.update(visible=False)] * 2


def ui_full(launch_kwargs):
with gr.Blocks() as interface:
gr.Markdown(
"""
# AudioGen
This is your private demo for [AudioGen](https://github.com/facebookresearch/audiocraft/blob/main/docs/AUDIOGEN.md),
a simple and controllable model for audio generation
"""
)
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Input Text", interactive=True)
with gr.Row():
submit = gr.Button("Submit")
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
with gr.Row():
model = gr.Radio(["facebook/audiogen-medium"], label="Model", value="facebook/audiogen-medium", interactive=True)
with gr.Row():
decoder = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False)
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 Audio")
audio_output = gr.Audio(label="Generated Audio (wav)", type='filepath')
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Would be great to add some examples as well similarly to the MusicGen demo for users to get some ideas about possible prompts.

submit.click(predict_full, inputs=[model, decoder, text, duration, topk, topp, temperature, cfg_coef], outputs=[output, audio_output])

interface.queue().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
ui_full(launch_kwargs)