import gradio as gr
import random
import torch
import torchaudio
from torch import inference_mode
from tempfile import NamedTemporaryFile
import numpy as np
from models import load_model
import utils
from inversion_utils import inversion_forward_process, inversion_reverse_process
def randomize_seed_fn(seed, randomize_seed):
if randomize_seed:
seed = random.randint(0, np.iinfo(np.int32).max)
torch.manual_seed(seed)
return seed
def invert(x0, prompt_src, num_diffusion_steps, cfg_scale_src): # , ldm_stable):
ldm_stable.model.scheduler.set_timesteps(num_diffusion_steps, device=device)
with inference_mode():
w0 = ldm_stable.vae_encode(x0)
# find Zs and wts - forward process
_, zs, wts = inversion_forward_process(ldm_stable, w0, etas=1,
prompts=[prompt_src],
cfg_scales=[cfg_scale_src],
prog_bar=True,
num_inference_steps=num_diffusion_steps,
numerical_fix=True)
return zs, wts
def sample(zs, wts, steps, prompt_tar, tstart, cfg_scale_tar): # , ldm_stable):
# reverse process (via Zs and wT)
tstart = torch.tensor(tstart, dtype=torch.int)
skip = steps - tstart
w0, _ = inversion_reverse_process(ldm_stable, xT=wts, skips=steps - skip,
etas=1., prompts=[prompt_tar],
neg_prompts=[""], cfg_scales=[cfg_scale_tar],
prog_bar=True,
zs=zs[:int(steps - skip)])
# vae decode image
with inference_mode():
x0_dec = ldm_stable.vae_decode(w0)
if x0_dec.dim() < 4:
x0_dec = x0_dec[None, :, :, :]
with torch.no_grad():
audio = ldm_stable.decode_to_mel(x0_dec)
f = NamedTemporaryFile("wb", suffix=".wav", delete=False)
torchaudio.save(f.name, audio, sample_rate=16000)
return f.name
def edit(input_audio,
model_id: str,
do_inversion: bool,
wts: gr.State, zs: gr.State, saved_inv_model: str,
source_prompt="",
target_prompt="",
steps=200,
cfg_scale_src=3.5,
cfg_scale_tar=12,
t_start=90,
randomize_seed=True):
global ldm_stable, current_loaded_model
print(f'current loaded model: {ldm_stable.model_id}')
if model_id != current_loaded_model:
print(f'Changing model to {model_id}...')
current_loaded_model = model_id
ldm_stable = None
ldm_stable = load_model(model_id, device, steps)
# If the inversion was done for a different model, we need to re-run the inversion
if not do_inversion and (saved_inv_model is None or saved_inv_model != model_id):
do_inversion = True
x0 = utils.load_audio(input_audio, ldm_stable.get_fn_STFT(), device=device)
if do_inversion or randomize_seed: # always re-run inversion
zs_tensor, wts_tensor = invert(x0=x0, prompt_src=source_prompt,
num_diffusion_steps=steps,
cfg_scale_src=cfg_scale_src)
wts = gr.State(value=wts_tensor)
zs = gr.State(value=zs_tensor)
saved_inv_model = model_id
do_inversion = False
output = sample(zs.value, wts.value, steps, prompt_tar=target_prompt, tstart=t_start,
cfg_scale_tar=cfg_scale_tar)
return output, wts, zs, saved_inv_model, do_inversion
current_loaded_model = "cvssp/audioldm2-music"
# current_loaded_model = "cvssp/audioldm2-music"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ldm_stable = load_model(current_loaded_model, device, 200) # deafult model
def get_example():
case = [
['Examples/Beethoven.wav',
'',
'A recording of an arcade game soundtrack.',
90,
'cvssp/audioldm2-music',
'27s',
'Examples/Beethoven_arcade.wav',
],
['Examples/Beethoven.wav',
'A high quality recording of wind instruments and strings playing.',
'A high quality recording of a piano playing.',
90,
'cvssp/audioldm2-music',
'27s',
'Examples/Beethoven_piano.wav',
],
['Examples/ModalJazz.wav',
'Trumpets playing alongside a piano, bass and drums in an upbeat old-timey cool jazz song.',
'A banjo playing alongside a piano, bass and drums in an upbeat old-timey cool country song.',
90,
'cvssp/audioldm2-music',
'106s',
'Examples/ModalJazz_banjo.wav',],
['Examples/Cat.wav',
'',
'A dog barking.',
150,
'cvssp/audioldm2-large',
'10s',
'Examples/Cat_dog.wav',]
]
return case
intro = """
Zero-Shot Text-Based Audio Editing Using DDPM Inversion
Demo for the text-based editing method introduced in:
Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion
Instructions:
Provide an input audio and a target prompt to edit the audio.
Tstart is used to control the tradeoff between fidelity to the original signal and text-adhearance.
Lower value -> favor fidelity. Higher value -> apply a stronger edit.
Make sure that you use an AudioLDM2 version that is suitable for your input audio.
For example, use the music version for music and the large version for general audio.
You can additionally provide a source prompt to guide even further the editing process.
Longer input will take more time.
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
"""
with gr.Blocks(css='style.css') as demo:
def reset_do_inversion():
do_inversion = gr.State(value=True)
return do_inversion
gr.HTML(intro)
wts = gr.State()
zs = gr.State()
saved_inv_model = gr.State()
# current_loaded_model = gr.State(value="cvssp/audioldm2-music")
# ldm_stable = load_model("cvssp/audioldm2-music", device, 200)
# ldm_stable = gr.State(value=ldm_stable)
do_inversion = gr.State(value=True) # To save some runtime when editing the same thing over and over
with gr.Row():
with gr.Column():
src_prompt = gr.Textbox(label="Source Prompt", lines=2, interactive=True, info= "Optional: Describe the original audio input",
placeholder="A recording of a happy upbeat classical music piece",)
input_audio = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Input Audio",
interactive=True, scale=1)
with gr.Column():
tar_prompt = gr.Textbox(label="Target Prompt", info="Describe your desired edited output", placeholder="a recording of a happy upbeat arcade game soundtrack",
lines=2, interactive=True)
output_audio = gr.Audio(label="Edited Audio", interactive=False, scale=1)
with gr.Row():
with gr.Column():
submit = gr.Button("Edit")
with gr.Row():
t_start = gr.Slider(minimum=30, maximum=160, value=110, step=1, label="T-start", interactive=True, scale=3,
info="Higher T-start -> stronger edit. Lower T-start -> more similar to original audio.")
model_id = gr.Dropdown(label="AudioLDM2 Version", choices=["cvssp/audioldm2",
"cvssp/audioldm2-large",
"cvssp/audioldm2-music"],
info="Choose a checkpoint suitable for your intended audio and edit.",
value="cvssp/audioldm2-music", interactive=True, type="value", scale=2)
with gr.Accordion("Advanced Options", open=False):
with gr.Row():
cfg_scale_src = gr.Number(value=3, minimum=0.5, maximum=25, precision=None,
label="Source Guidance Scale", interactive=True, scale=1)
cfg_scale_tar = gr.Number(value=12, minimum=0.5, maximum=25, precision=None,
label="Target Guidance Scale", interactive=True, scale=1)
steps = gr.Number(value=200, precision=0, minimum=20, maximum=1000,
label="Num Diffusion Steps", interactive=True, scale=1)
with gr.Row():
seed = gr.Number(value=0, precision=0, label="Seed", interactive=True)
randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
length = gr.Number(label="Length", interactive=False, visible=False)
def change_tstart_range(steps):
t_start.maximum = int(160/200 * steps)
t_start.minimum = int(30/200 * steps)
if t_start.value > t_start.maximum:
t_start.value = t_start.maximum
if t_start.value < t_start.minimum:
t_start.value = t_start.minimum
return t_start
submit.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=[seed], queue=False).then(
fn=edit,
inputs=[input_audio,
model_id,
do_inversion,
# current_loaded_model, ldm_stable,
wts, zs, saved_inv_model,
src_prompt,
tar_prompt,
steps,
cfg_scale_src,
cfg_scale_tar,
t_start,
randomize_seed
],
outputs=[output_audio, wts, zs, saved_inv_model, do_inversion] # , current_loaded_model, ldm_stable],
)
# If sources changed we have to rerun inversion
input_audio.change(fn=reset_do_inversion, outputs=[do_inversion])
src_prompt.change(fn=reset_do_inversion, outputs=[do_inversion])
model_id.change(fn=reset_do_inversion, outputs=[do_inversion])
steps.change(fn=change_tstart_range, inputs=[steps], outputs=[t_start])
gr.Examples(
label="Examples",
examples=get_example(),
inputs=[input_audio, src_prompt, tar_prompt, t_start, model_id, length, output_audio],
outputs=[output_audio]
)
demo.queue()
demo.launch()