from huggingface_hub import hf_hub_download
import torch
import os
import gradio as gr
from audioldm2 import text_to_audio, build_model
from share_btn import community_icon_html, loading_icon_html, share_js
os.environ["TOKENIZERS_PARALLELISM"] = "true"
default_checkpoint="audioldm2-full"
audioldm = None
current_model_name = None
def text2audio(
text,
guidance_scale,
random_seed,
n_candidates,
model_name=default_checkpoint,
):
global audioldm, current_model_name
torch.set_float32_matmul_precision("high")
if audioldm is None or model_name != current_model_name:
audioldm = build_model(model_name=model_name)
current_model_name = model_name
audioldm = torch.compile(audioldm)
# print(text, length, guidance_scale)
waveform = text_to_audio(
latent_diffusion=audioldm,
text=text,
seed=random_seed,
duration=10,
guidance_scale=guidance_scale,
n_candidate_gen_per_text=int(n_candidates),
) # [bs, 1, samples]
waveform = [
gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform
]
# waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
if len(waveform) == 1:
waveform = waveform[0]
return waveform
css = """
a {
color: inherit;
text-decoration: underline;
}
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
max-width: 730px !important;
}
.gr-button {
color: white;
border-color: #000000;
background: #000000;
}
input[type='range'] {
accent-color: #000000;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
margin: auto;
padding-top: 1.5rem;
}
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#gallery>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
#advanced-btn {
font-size: .7rem !important;
line-height: 19px;
margin-top: 12px;
margin-bottom: 12px;
padding: 2px 8px;
border-radius: 14px !important;
}
#advanced-options {
margin-bottom: 20px;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.acknowledgments h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
#container-advanced-btns{
display: flex;
flex-wrap: wrap;
justify-content: space-between;
align-items: center;
}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
margin-top: 10px;
margin-left: auto;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
.gr-form{
flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
}
#prompt-container{
gap: 0;
}
#generated_id{
min-height: 700px
}
#setting_id{
margin-bottom: 12px;
text-align: center;
font-weight: 900;
}
#submit-button{
width: 100%;
}
"""
iface = gr.Blocks(css=css)
with iface:
gr.HTML(
"""
"""
)
gr.HTML(
"""
For faster inference without a queue
"""
)
with gr.Group():
with gr.Box():
############# Input
textbox = gr.Textbox(
value="A forest of wind chimes singing a soothing melody in the breeze.",
max_lines=1,
label="Input your prompt here",
info="Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.",
elem_id="prompt-in",
)
with gr.Accordion("Click to modify detailed configurations", open=False):
seed = gr.Number(
value=45,
label="Seed",
info="Change this value (any integer number) will lead to a different generation result."
)
# duration = gr.Slider(
# 10, 10, value=10, step=2.5, label="Duration (seconds)"
# )
guidance_scale = gr.Slider(
0,
6,
value=3.5,
step=0.5,
label="Guidance Scale",
info="Large => better quality and relavancy to text; Small => better diversity"
)
n_candidates = gr.Slider(
1,
3,
value=3,
step=1,
label="Number of candidates",
info="This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation"
)
# model_name = gr.Dropdown(
# ["audioldm-m-text-ft", "audioldm-s-text-ft", "audioldm-m-full","audioldm-s-full-v2", "audioldm-s-full", "audioldm-l-full"], value="audioldm-m-full", label="Choose the model to use. audioldm-m-text-ft and audioldm-s-text-ft are recommanded. -s- means small, -m- means medium and -l- means large",
# )
############# Output
# outputs=gr.Audio(label="Output", type="numpy")
outputs = gr.Video(label="Output", elem_id="output-video")
# with gr.Group(elem_id="container-advanced-btns"):
# # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
# with gr.Group(elem_id="share-btn-container"):
# community_icon = gr.HTML(community_icon_html, visible=False)
# loading_icon = gr.HTML(loading_icon_html, visible=False)
# share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
# outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")]
btn = gr.Button("Submit", elem_id="submit-button").style(full_width=True)
with gr.Group(elem_id="share-btn-container", visible=False):
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Share to community", elem_id="share-btn")
# btn.click(text2audio, inputs=[
# textbox, duration, guidance_scale, seed, n_candidates, model_name], outputs=[outputs])
btn.click(
text2audio,
inputs=[textbox, guidance_scale, seed, n_candidates],
outputs=[outputs],
)
share_button.click(None, [], [], _js=share_js)
gr.HTML(
"""
"""
)
gr.Examples(
[
[
"An excited crowd cheering at a sports game.",
3.5,
45,
3,
default_checkpoint,
],
[
"A cat is meowing for attention.",
3.5,
45,
3,
default_checkpoint,
],
[
"Birds singing sweetly in a blooming garden.",
3.5,
45,
3,
default_checkpoint,
],
[
"A modern synthesizer creating futuristic soundscapes.",
3.5,
45,
3,
default_checkpoint,
],
[
"The vibrant beat of Brazilian samba drums.",
3.5,
45,
3,
default_checkpoint,
],
],
fn=text2audio,
# inputs=[textbox, duration, guidance_scale, seed, n_candidates, model_name],
inputs=[textbox, guidance_scale, seed, n_candidates],
outputs=[outputs],
cache_examples=True,
)
gr.HTML(
"""
Essential Tricks for Enhancing the Quality of Your Generated Audio
1. Try to use more adjectives to describe your sound. For example: "A man is speaking clearly and slowly in a large room" is better than "A man is speaking". This can make sure AudioLDM 2 understands what you want.
2. Try to use different random seeds, which can affect the generation quality significantly sometimes.
3. It's better to use general terms like 'man' or 'woman' instead of specific names for individuals or abstract objects that humans may not be familiar with, such as 'mummy'.
"""
)
with gr.Accordion("Additional information", open=False):
gr.HTML(
"""
"""
)
# This demo is strictly for research demo purpose only. For commercial use please contact us.
iface.queue(max_size=20)
iface.launch(debug=True)
# iface.launch(debug=True, share=True)