Spaces:
Runtime error
Runtime error
File size: 10,846 Bytes
41ceddd 33d62f7 83dc4c8 541c07d c55c219 958907e 41ceddd 39711bd c55c219 41ceddd c55c219 83dc4c8 39711bd 83dc4c8 c2362ff 39711bd 4e9d8a1 541c07d f45148f bdab1da 858c11b 4eab478 c2362ff 4eab478 858c11b 8f019f2 c2362ff 858c11b c55c219 27e435d 858c11b c2362ff c55c219 4e9d8a1 c55c219 858c11b c2362ff f45148f 4e9d8a1 858c11b f45148f 858c11b 993e22c 858c11b 993e22c 858c11b 541c07d db19271 541c07d f45148f 83dc4c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 |
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])) for wave in waveform]
# waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
if(len(waveform) == 1):
waveform = waveform[0]
return waveform # ,gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
# 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 = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: black;
background: black;
}
input[type='range'] {
accent-color: black;
}
.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 {
display: none;
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%;
}
.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;
}
#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
#component-16{border-top-width: 1px!important;margin-top: 1em}
.image_duplication{position: absolute; width: 100px; left: 50px}
"""
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>
"""
)
gr.HTML("""
<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in 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():
############# 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.")
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")
# 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)
btn.click(text2audio, inputs=[textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs]) # , share_button, community_icon, loading_icon
# 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>
''')
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(concurrency_count = 3)
iface.launch(debug=True)
# iface.launch(debug=True, share=True) |