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.float32
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,
num_inference_steps=100,
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(
"""
"""
)
gr.HTML(
"""
This is the demo for AudioLDM, powered by 🧨 Diffusers. Demo uses the checkpoint audioldm-m-full . For faster inference without waiting in
queue, you may duplicate the space and upgrade to a GPU in the settings.
"""
)
with gr.Group():
with gr.Blocks():
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,
5,
value=3.5,
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", elem_id=".gr-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, negative_textbox, duration, guidance_scale, seed, n_candidates],
outputs=[outputs],
)
share_button.click(None, [], [], js=share_js)
gr.HTML(
"""
"""
)
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],
["A man is speaking in a small room.", "low quality, average quality", 5, 2.5, 45, 3],
["A female is speaking followed by footstep sound", "low quality, average quality", 5, 2.5, 45, 3],
["Wooden table tapping sound followed by water pouring sound.", "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(
"""
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'.
4. Using a negative prompt to not guide the diffusion process can improve the
audio quality significantly. Try using negative prompts like 'low quality'.
"""
)
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=10).launch(debug=True)