create app.py

app.py

@@ -0,0 +1,262 @@
+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,
+        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>
+            </div>
+        """
+    )
+
+    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,
+                    4,
+                    value=2.5,
+                    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/soulabi"
+                    style="text-decoration: underline;" target="_blank"> Github repo</a> </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="[email protected]">contact us</a>.</p>
+
+iface.queue(max_size=10).launch(debug=True)