import gradio as gr
import torch
from diffusers import AudioLDMPipeline
from share_btn import community_icon_html, loading_icon_html, share_js
from transformers import AutoProcessor, ClapModel
# make Space compatible with CPU duplicates
if torch.cuda.is_available():
device = "cuda"
torch_dtype = torch.float16
else:
device = "cpu"
torch_dtype = torch.float32
# load the diffusers pipeline
repo_id = "cvssp/audioldm-m-full"
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device)
pipe.unet = torch.compile(pipe.unet)
# CLAP model (only required for automatic scoring)
clap_model = ClapModel.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full").to(device)
processor = AutoProcessor.from_pretrained("sanchit-gandhi/clap-htsat-unfused-m-full")
generator = torch.Generator(device)
def text2audio(text, negative_prompt, duration, guidance_scale, random_seed, n_candidates):
if text is None:
raise gr.Error("Please provide a text input.")
waveforms = pipe(
text,
audio_length_in_s=duration,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_waveforms_per_prompt=n_candidates if n_candidates else 1,
generator=generator.manual_seed(int(random_seed)),
)["audios"]
if waveforms.shape[0] > 1:
waveform = score_waveforms(text, waveforms)
else:
waveform = waveforms[0]
return gr.make_waveform((16000, waveform), bg_image="bg.png")
def score_waveforms(text, waveforms):
inputs = processor(text=text, audios=list(waveforms), return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits_per_text = clap_model(**inputs).logits_per_text # this is the audio-text similarity score
probs = logits_per_text.softmax(dim=-1) # we can take the softmax to get the label probabilities
most_probable = torch.argmax(probs) # and now select the most likely audio waveform
waveform = waveforms[most_probable]
return waveform
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> <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm">[🧨
Diffusers]</a>
</p>
</div>
"""
)
gr.HTML(
"""
<p>This is the demo for AudioLDM, powered by 🧨 Diffusers. Demo uses the checkpoint <a
href="https://huggingface.co/cvssp/audioldm-m-full"> audioldm-m-full </a>. For faster inference without waiting in
queue, you may duplicate the space and upgrade to a GPU in the settings. <br/> <a
href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation?duplicate=true"> <img
style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> <p/>
"""
)
with gr.Group():
with gr.Box():
textbox = gr.Textbox(
value="A hammer is hitting a wooden surface",
max_lines=1,
label="Input text",
info="Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.",
elem_id="prompt-in",
)
negative_textbox = gr.Textbox(
value="low quality, average quality",
max_lines=1,
label="Negative prompt",
info="Enter a negative prompt not to guide the audio generation. Selecting appropriate negative prompts can improve the audio quality significantly.",
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(2.5, 10, value=5, step=2.5, label="Duration (seconds)")
guidance_scale = gr.Slider(
0,
4,
value=2.5,
step=0.5,
label="Guidance scale",
info="Large => better quality and relevancy to text; Small => better diversity",
)
n_candidates = gr.Slider(
1,
3,
value=3,
step=1,
label="Number waveforms to generate",
info="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",
)
outputs = gr.Video(label="Output", elem_id="output-video")
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, negative_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>. Code and demo by 🤗 Hugging Face.</p> <br>
</div>
"""
)
gr.Examples(
[
["A hammer is hitting a wooden surface", "low quality, average quality", 5, 2.5, 45, 3],
["Peaceful and calming ambient music with singing bowl and other instruments.", "low quality, average quality", 5, 2.5, 45, 3],
],
fn=text2audio,
inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
outputs=[outputs],
cache_examples=True,
)
gr.HTML(
"""
<div class="acknowledgements"> <p>Essential Tricks for Enhancing the Quality of Your Generated
Audio</p> <p>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.</p> <p>2. Try to use different random seeds, which can affect the generation
quality significantly sometimes.</p> <p>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'.</p> <p>4. Using a negative prompt to not guide the diffusion process can improve the
audio quality significantly. Try using negative prompts like 'low quality'.</p> </div>
"""
)
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="haoheliu@gmail.com">contact us</a>.</p>
iface.queue(max_size=10).launch(debug=True)