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 # 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( """

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

[Paper] [Project page]

""" ) 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.
Duplicate Space

""") 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.", elem_id="prompt-in") 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", 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) as share_group: 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], outputs=[outputs, share_group]) share_button.click(None, [], [], _js=share_js) gr.HTML('''

''') gr.Examples([ ["A hammer is hitting a wooden surface", 5, 2.5, 45, 3], ["Peaceful and calming ambient music with singing bowl and other instruments.", 5, 2.5, 45, 3], ["A man is speaking in a small room.", 5, 2.5, 45, 3], ["A female is speaking followed by footstep sound", 5, 2.5, 45, 3], ["Wooden table tapping sound followed by water pouring sound.", 5, 2.5, 45, 3], ], fn=text2audio, inputs=[textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs], cache_examples=True, ) with gr.Accordion("Additional information", open=False): gr.HTML( """

We build the model with data from AudioSet, Freesound and BBC Sound Effect library. We share this demo based on the UK copyright exception of data for academic research.

""" ) #

This demo is strictly for research demo purpose only. For commercial use please contact us.

iface.queue(concurrency_count=3) iface.launch(debug=True) # iface.launch(debug=True, share=True)