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 # from transformers import AutoModelForCausalLM, AutoTokenizer # import torch # tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") # model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") 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( """

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

[Paper] [Project page]

""" ) 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, community_icon, loading_icon, share_button]) # , share_button, community_icon, loading_icon share_button.click(None, [], [], _js=share_js) gr.HTML('''
''') 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 = 2) iface.launch(debug=True) # iface.launch(debug=True, share=True)