# Adapted app.py from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/main/app.py import gradio as gr import torch from diffusers import AudioLDMPipeline from transformers import AutoProcessor, ClapModel # replace with cuda code from AudioLDM's original app.py if using GPU device = "cpu" torch_dtype = torch.float32 # load AudioLDM Diffuser Pipeline pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm-m-full", torch_dtype=torch_dtype).to(device) pipe.unet = torch.compile(pipe.unet) # omit CLAP model because we'll only generate one waveform, no scoring generator = torch.Generator(device) # modified from audioldm app.py to omit n_candidates def text2audio(text, negative_prompt, duration, guidance_scale, random_seed): 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=1, generator=generator.manual_seed(int(random_seed)), )["audios"] waveform = waveforms[0] return gr.make_waveform((16000, waveform), bg_image="bg.png") # duplicate CSS config 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) # modified html to only include vital parts with iface: gr.HTML( """

AudioLDM Animals: Text-to-Audio Generation with Latent Diffusion Models (hopefully) Fine-Tuned for animal sounds

[Paper] [Original Project page] [🧨 Diffusers]

""" ) with gr.Group(): with gr.Box(): textbox = gr.Textbox( value="A dog is barking", 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", ) outputs = gr.Video(label="Output", elem_id="output-video") btn = gr.Button("Submit").style(full_width=True) btn.click( text2audio, inputs=[textbox, negative_textbox, duration, guidance_scale, seed], outputs=[outputs], ) iface.queue(max_size=1).launch(debug=True)