Swap to HF diffusers

app.py

@@ -1,197 +1,139 @@
 import gradio as gr
-import numpy as np
-from audioldm import text_to_audio, build_model
+import torch
+from diffusers import AudioLDMPipeline
 from share_btn import community_icon_html, loading_icon_html, share_js
 
-model_id="haoheliu/AudioLDM-S-Full"
+from transformers import AutoProcessor, ClapModel
 
-audioldm = None
-current_model_name = None
 
-# def predict(input, history=[]):
-#     # tokenize the new input sentence
-#     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+# make Space compatible with CPU duplicates
+if torch.cuda.is_available():
+    device = "cuda"
+    torch_dtype = torch.float16
+else:
+    device = "cpu"
+    torch_dtype = torch.float32
 
-#     # append the new user input tokens to the chat history
-#     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+# 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)
 
-#     # generate a response 
-#     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+# 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")
 
-#     # 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, model_name="audioldm-m-text-ft"):
-    global audioldm, current_model_name
-    
-    if audioldm is None or model_name != current_model_name:
-        audioldm=build_model(model_name=model_name)
-        current_model_name = model_name
-        
-    # print(text, length, guidance_scale)
-    waveform = text_to_audio(
-        latent_diffusion=audioldm,
-        text=text,
-        seed=random_seed,
-        duration=duration,
+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,
-        n_candidate_gen_per_text=int(n_candidates),
-    )  # [bs, 1, samples]
-    waveform = [
-        gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform
-    ]
-    # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
-    if(len(waveform) == 1):
-      waveform = waveform[0]
-    return waveform
+        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")
+
 
-# 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)
+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 {
+            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'] {
+        } .gr-button {
+            color: white; border-color: #000000; background: #000000;
+        } input[type='range'] {
             accent-color: #000000;
-        }
-        .dark input[type='range'] {
+        } .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 {
+        } .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 {
+        } .details:hover {
             text-decoration: underline;
-        }
-        .gr-button {
+        } .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;
+        } .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 {
+        } #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 {
+        } .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 {
+        } .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 {
+        } .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 {
+        } @keyframes spin {
             from {
                 transform: rotate(0deg);
-            }
-            to {
+            } 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 * {
+        } #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 {
+        } #share-btn-container div:nth-child(-n+2){
+            width: auto !important; min-height: 0px !important;
+        } #share-btn-container .wrap {
             display: none !important;
-        }
-        .gr-form{
+        } .gr-form{
             flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-        }
-        #prompt-container{
+        } #prompt-container{
             gap: 0;
-        }
-        #generated_id{
+        } #generated_id{
             min-height: 700px
-        }
-        #setting_id{
-          margin-bottom: 12px;
-          text-align: center;
-          font-weight: 900;
+        } #setting_id{
+          margin-bottom: 12px; text-align: center; font-weight: 900;
         }
 """
 iface = gr.Blocks(css=css)
@@ -202,56 +144,72 @@ with iface:
             <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;
+                  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>
+              </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("""
-        <h1 style="font-weight: 900; margin-bottom: 7px;">
-        AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
-        </h1>
-        <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in 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/>
-    """)
+    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():
-            ############# Input
-            textbox = gr.Textbox(value="A hammer is hitting a wooden surface", max_lines=1, label="Input your text here. Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.", elem_id="prompt-in")
+            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="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 (Large => better quality and relavancy to text; Small => better diversity)")
-              n_candidates = gr.Slider(1, 3, 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")
-              # model_name = gr.Dropdown(
-              #       ["audioldm-m-text-ft", "audioldm-s-text-ft", "audioldm-m-full","audioldm-s-full-v2", "audioldm-s-full", "audioldm-l-full"], value="audioldm-m-full", label="Choose the model to use. audioldm-m-text-ft and audioldm-s-text-ft are recommanded. -s- means small, -m- means medium and -l- means large",
-              #   )
-            ############# 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")]
+                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):
@@ -259,51 +217,61 @@ with iface:
             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, model_name], outputs=[outputs])
-        btn.click(text2audio, inputs=[
-                  textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs])
-        
+        btn.click(
+            text2audio,
+            inputs=[textbox, negative_textbox, duration, guidance_scale, seed, n_candidates],
+            outputs=[outputs],
+        )
+
         share_button.click(None, [], [], _js=share_js)
-        gr.HTML('''
+        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></p>
-                    <br>
+                    <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", 5, 2.5, 45, 3, "audioldm-m-full"],
-            ["Peaceful and calming ambient music with singing bowl and other instruments.", 5, 2.5, 45, 3, "audioldm-m-full"],
-            ["A man is speaking in a small room.", 5, 2.5, 45, 3, "audioldm-m-full"],
-            ["A female is speaking followed by footstep sound", 5, 2.5, 45, 3, "audioldm-m-full"],
-            ["Wooden table tapping sound followed by water pouring sound.", 5, 2.5, 45, 3, "audioldm-m-full"],
-        ],
+        """
+        )
+        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, duration, guidance_scale, seed, n_candidates, model_name],
-            inputs=[textbox, duration, guidance_scale, seed, n_candidates],
+            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>
-                </div>
-                ''')
+        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>
+                    <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)
-# iface.launch(debug=True, share=True)