Spaces:
Runtime error
Runtime error
| 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 | |
| # make Space compatible with CPU duplicates | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| torch_dtype = torch.float16 | |
| else: | |
| device = "cpu" | |
| torch_dtype = torch.float32 | |
| # load the diffusers pipeline | |
| 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 (only required for automatic scoring) | |
| 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, | |
| num_inference_steps=100, | |
| 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 # this is the audio-text similarity score | |
| probs = logits_per_text.softmax(dim=-1) # we can take the softmax to get the label probabilities | |
| most_probable = torch.argmax(probs) # and now select the most likely audio waveform | |
| 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, | |
| 5, | |
| value=3.5, | |
| 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], | |
| ["A man is speaking in a small room.", "low quality, average quality", 5, 2.5, 45, 3], | |
| ["A female is speaking followed by footstep sound", "low quality, average quality", 5, 2.5, 45, 3], | |
| ["Wooden table tapping sound followed by water pouring sound.", "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> | |
| """ | |
| ) | |
| # <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(max_size=10).launch(debug=True) | |