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 = None
current_model_name = 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, model_name):
global audioldm, current_model_name
if audioldm is None or model_name != current_model_name:
audioldm=build_model(model_name=model_name)
current_model_name = model_name
# print(text, length, guidance_scale)
waveform = text_to_audio(
latent_diffusion=audioldm,
text=text,
seed=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(
"""
"""
)
gr.HTML("""
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
""")
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. 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="Change this value (any integer number) will lead to a different generation result.")
duration = gr.Slider(2.5, 10, value=10, step=2.5, label="Duration (seconds)")
guidance_scale = gr.Slider(0, 4, 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")
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-text-ft", 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").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])
share_button.click(None, [], [], _js=share_js)
gr.HTML('''
''')
gr.Examples([
["A hammer is hitting a wooden surface", 5, 2.5, 45, 3, "audioldm-m-text-ft"],
["Peaceful and calming ambient music with singing bowl and other instruments.", 5, 2.5, 45, 3, "audioldm-m-text-ft"],
["A man is speaking in a small room.", 5, 2.5, 45, 3, "audioldm-m-text-ft"],
["A female is speaking followed by footstep sound", 5, 2.5, 45, 3, "audioldm-m-text-ft"],
["Wooden table tapping sound followed by water pouring sound.", 5, 2.5, 45, 3, "audioldm-m-text-ft"],
],
fn=text2audio,
inputs=[textbox, duration, guidance_scale, seed, n_candidates, model_name],
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 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(concurrency_count=2)
iface.launch(debug=True)
# iface.launch(debug=True, share=True)