This Pull Request upgrades the space with a newer model

This PR uses _Stable Audio Open Zero_ instead of _AudioLDM_. This model can generate up to 47 seconds of sound.

Click on _Merge_ to add this feature.

README.md

@@ -1,5 +1,5 @@
 ---
-title: Audioldm Text To Audio Generation
+title: Stable Audio Open Zero
 emoji: 🔊
 colorFrom: indigo
 colorTo: red
@@ -8,15 +8,10 @@ sdk_version: 4.37.2
 app_file: app.py
 pinned: false
 license: bigscience-openrail-m
-duplicated_from: haoheliu/audioldm-text-to-audio-generation
+tags:
+  - Text-to-Audio
+  - LLM
+short_description: Text-to-Audio Generation
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-## Reference
-Part of the code from this repo is borrowed from the following repos. We would like to thank the authors of them for their contribution. 
-
-> https://github.com/LAION-AI/CLAP
-> https://github.com/CompVis/stable-diffusion
-> https://github.com/v-iashin/SpecVQGAN 
-> https://github.com/toshas/torch-fidelity

app.py

@@ -1,278 +1,110 @@
-import gradio as gr
+import random
 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.float32
-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/>
-    """
+import torchaudio
+from einops import rearrange
+import gradio as gr
+import spaces
+import os
+import uuid
+
+# Importing the model-related functions
+from stable_audio_tools import get_pretrained_model
+from stable_audio_tools.inference.generation import generate_diffusion_cond
+
+# Load the model outside of the GPU-decorated function
+def load_model():
+    print("Loading model...")
+    model, model_config = get_pretrained_model("chaowenguo/stable-audio-open-1.0")
+    print("Model loaded successfully.")
+    return model, model_config
+
+# Function to set up, generate, and process the audio
+@spaces.GPU(duration=120)  # Allocate GPU only when this function is called
+def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
+    print(f"Prompt received: {prompt}")
+    print(f"Settings: Duration={seconds_total}s, Steps={steps}, CFG Scale={cfg_scale}")
+
+    seed = random.randint(0, 2**63 - 1)
+    random.seed(seed)
+    torch.manual_seed(seed)
+    print(f"Using seed: {seed}")
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
+
+    # Fetch the Hugging Face token from the environment variable
+    hf_token = os.getenv('HF_TOKEN')
+    print(f"Hugging Face token: {hf_token}")
+
+    # Use pre-loaded model and configuration
+    model, model_config = load_model()
+    sample_rate = model_config["sample_rate"]
+    sample_size = model_config["sample_size"]
+
+    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
+
+    model = model.to(device)
+    print("Model moved to device.")
+
+    # Set up text and timing conditioning
+    conditioning = [{
+        "prompt": prompt,
+        "seconds_start": 0,
+        "seconds_total": seconds_total
+    }]
+    print(f"Conditioning: {conditioning}")
+
+    # Generate stereo audio
+    print("Generating audio...")
+    output = generate_diffusion_cond(
+        model,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        conditioning=conditioning,
+        sample_size=sample_size,
+        sigma_min=0.3,
+        sigma_max=500,
+        sampler_type="dpmpp-3m-sde",
+        device=device
     )
-
-    with gr.Group():
-        with gr.Blocks():
-            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", elem_id=".gr-Button") # .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)
+    print("Audio generated.")
+
+    # Rearrange audio batch to a single sequence
+    output = rearrange(output, "b d n -> d (b n)")
+    print("Audio rearranged.")
+
+    # Peak normalize, clip, convert to int16
+    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    print("Audio normalized and converted.")
+
+    # Generate a unique filename for the output
+    unique_filename = f"output_{uuid.uuid4().hex}.wav"
+    print(f"Saving audio to file: {unique_filename}")
+
+    # Save to file
+    torchaudio.save(unique_filename, output, sample_rate)
+    print(f"Audio saved: {unique_filename}")
+
+    # Return the path to the generated audio file
+    return unique_filename
+
+# Setting up the Gradio Interface
+interface = gr.Interface(
+    fn=generate_audio,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"),
+        gr.Slider(0, 47, value=5, label="Duration in Seconds"),
+        gr.Slider(10, 150, value=10, step=10, label="Number of Diffusion Steps"),
+        gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
+    ],
+    outputs=gr.Audio(type="filepath", label="Generated Audio"),
+    title="Stable Audio Generator",
+    description="Generate variable-length stereo audio at 44.1kHz from text prompts using Stable Audio Open 1.0."
+)
+
+
+# Pre-load the model to avoid multiprocessing issues
+model, model_config = load_model()
+
+# Launch the Interface
+interface.launch()

requirements.txt

@@ -1,8 +1,3 @@
-git+https://github.com/huggingface/diffusers.git
-git+https://github.com/huggingface/transformers.git
---extra-index-url https://download.pytorch.org/whl/cu113
 torch
-numpy==1.24.3
-pydantic 
-fastapi 
-gradio
+torchaudio
+stable-audio-tools