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local_app.py
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local_app.py
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import gradio as gr
import numpy as np
from audioldm import text_to_audio, build_model
# from share_btn import community_icon_html, loading_icon_html, share_js
model_id = "haoheliu/AudioLDM-S-Full"
audioldm = build_model()
# audioldm=None
# def predict(input, history=[]):
# # tokenize the new input sentence
# new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
# # append the new user input tokens to the chat history
# bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
# # generate a response
# history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
# # convert the tokens to text, and then split the responses into lines
# response = tokenizer.decode(history[0]).split("<|endoftext|>")
# response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list
# return response, history
def text2audio(text, duration, guidance_scale, random_seed, n_candidates):
# print(text, length, guidance_scale)
waveform = text_to_audio(
audioldm,
text,
random_seed,
duration=duration,
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
# iface = gr.Interface(fn=text2audio, inputs=[
# gr.Textbox(value="A man is speaking in a huge room", max_lines=1),
# gr.Slider(2.5, 10, value=5, step=2.5),
# gr.Slider(0, 5, value=2.5, step=0.5),
# gr.Number(value=42)
# ], outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")],
# allow_flagging="never"
# )
# iface.launch(share=True)
css = """
a {
color: inherit;
text-decoration: underline;
}
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: #000000;
background: #000000;
}
input[type='range'] {
accent-color: #000000;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
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;
}
"""
iface = gr.Blocks(css=css)
with iface:
gr.HTML(
"""
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
<a href="https://arxiv.org/abs/2301.12503">[Paper]</a> <a href="https://audioldm.github.io/">[Project page]</a>
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
############# Input
textbox = gr.Textbox(
value="A hammer is hitting a wooden surface",
max_lines=1,
label="Input your text here. Please ensure it is descriptive and of moderate length.",
elem_id="prompt-in",
)
with gr.Accordion("Click to modify detailed configurations", open=False):
seed = gr.Number(
value=42,
label="Change this value (any integer number) will lead to a different generation result.",
)
duration = gr.Slider(
2.5, 10, value=5, step=2.5, label="Duration (seconds)"
)
guidance_scale = gr.Slider(
0,
5,
value=2.5,
step=0.5,
label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)",
)
n_candidates = gr.Slider(
1,
5,
value=3,
step=1,
label="Automatic quality control. 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",
)
############# 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").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],
outputs=[outputs],
)
# share_button.click(None, [], [], _js=share_js)
gr.HTML(
"""
<div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;">
<p>Follow the latest update of AudioLDM on our<a href="https://github.com/haoheliu/AudioLDM" style="text-decoration: underline;" target="_blank"> Github repo</a>
</p>
<br>
<p>Model by <a href="https://twitter.com/LiuHaohe" style="text-decoration: underline;" target="_blank">Haohe Liu</a></p>
<br>
</div>
"""
)
gr.Examples(
[
["A hammer is hitting a wooden surface", 5, 2.5, 45, 3],
[
"Peaceful and calming ambient music with singing bowl and other instruments.",
5,
2.5,
45,
3,
],
["A man is speaking in a small room.", 5, 2.5, 45, 3],
["A female is speaking followed by footstep sound", 5, 2.5, 45, 3],
[
"Wooden table tapping sound followed by water pouring sound.",
5,
2.5,
45,
3,
],
],
fn=text2audio,
inputs=[textbox, duration, guidance_scale, seed, n_candidates],
outputs=[outputs],
cache_examples=True,
)
with gr.Accordion("Additional information", open=False):
gr.HTML(
"""
<div class="acknowledgments">
<p> We build the model with data from <a href="http://research.google.com/audioset/">AudioSet</a>, <a href="https://freesound.org/">Freesound</a> and <a href="https://sound-effects.bbcrewind.co.uk/">BBC Sound Effect library</a>. We share this demo based on the <a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf">UK copyright exception</a> of data for academic research. </p>
</div>
"""
)
# <p>This demo is strictly for research demo purpose only. For commercial use please <a href="[email protected]">contact us</a>.</p>
iface.queue(concurrency_count=3)
# iface.launch(debug=True)
iface.launch(debug=True, share=True)