|
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 |
|
|
|
|
|
|
|
if torch.cuda.is_available(): |
|
device = "cuda" |
|
torch_dtype = torch.float16 |
|
else: |
|
device = "cpu" |
|
torch_dtype = torch.float32 |
|
|
|
|
|
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 = 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 |
|
probs = logits_per_text.softmax(dim=-1) |
|
most_probable = torch.argmax(probs) |
|
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> |
|
""" |
|
) |
|
|
|
|
|
iface.queue(max_size=10).launch(debug=True) |
|
|
