make it multiplayer (#3)

- make it multiplayer (9f88b4455c91e04313c6be362fd8ac3713b4d5bf)
- Update README.md (a2fa3a420dbb1f91b30a5b8f7b4398333f2c6d89)


Co-authored-by: Apolinário from multimodal AI art <multimodalart@users.noreply.huggingface.co>

README.md

@@ -1,5 +1,5 @@
 ---
-title: Stableaudio Open 1.0
+title: Stable Audio Live Multiplayer
 emoji: 💻
 colorFrom: pink
 colorTo: red

app.py

@@ -10,6 +10,13 @@ import uuid
 from stable_audio_tools import get_pretrained_model
 from stable_audio_tools.inference.generation import generate_diffusion_cond
 
+PAGE_SIZE = 10
+FILE_DIR_PATH = "/data"
+
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+
 # Load the model outside of the GPU-decorated function
 def load_model():
     
@@ -19,7 +26,7 @@ def load_model():
 
 # 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, sampler_type_dropdown, seconds_total=30, steps=100, cfg_scale=7,sigma_min_slider=0.3,sigma_max_slider=500):
+def generate_audio(prompt, sampler_type_dropdown, seconds_total=30, steps=100, cfg_scale=7,sigma_min_slider=0.3,sigma_max_slider=500, progress=gr.Progress(track_tqdm=True)):
     print(f"Prompt received: {prompt}")
     print(f"Settings: Duration={seconds_total}s, Steps={steps}, CFG Scale={cfg_scale}")
 
@@ -76,34 +83,54 @@ def generate_audio(prompt, sampler_type_dropdown, seconds_total=30, steps=100, c
         print(f"Audio trimmed to {seconds_total} seconds.")
 
     # Generate a unique filename for the output
-    unique_filename = f"output_{uuid.uuid4().hex}.wav"
+    random_uuid = uuid.uuid4().hex
+    unique_filename = f"/data/output_{random_uuid}.wav"
+    unique_textfile = f"/data/output_{random_uuid}.txt"
     print(f"Saving audio to file: {unique_filename}")
 
     # Save to file
     torchaudio.save(unique_filename, output, sample_rate)
     print(f"Audio saved: {unique_filename}")
 
+    with open(unique_textfile, "w") as file:
+        file.write(prompt)
+    
     # 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.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"], label="Sampler type", value="dpmpp-3m-sde"),
-        gr.Slider(0, 47, value=30, step=1, label="Duration in Seconds"),
-        gr.Slider(10, 150, value=100, step=10, label="Number of Diffusion Steps"),
-        gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale"),        
-        gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.3, label="Sigma min"),
-        gr.Slider(minimum=0.0, maximum=1000.0, step=0.1, value=500, label="Sigma max"),
-
-    ],
-    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.",
-    examples=[
+def list_all_outputs(generation_history):
+    directory_path = FILE_DIR_PATH
+    files_in_directory = os.listdir(directory_path)
+    wav_files = [os.path.join(directory_path, file) for file in files_in_directory if file.endswith('.wav')]
+    wav_files.sort(key=lambda x: os.path.getmtime(os.path.join(directory_path, x)), reverse=True)
+    history_list = generation_history.split(',') if generation_history else []
+    updated_files = [file for file in wav_files if file not in history_list]
+    updated_history = updated_files + history_list
+    return ','.join(updated_history), gr.update(visible=True)
+
+def increase_list_size(list_size):
+    return list_size+PAGE_SIZE
+
+css = '''
+#live_gen:before {
+    content: '';
+    animation: svelte-z7cif2-pulseStart 1s cubic-bezier(.4,0,.6,1), svelte-z7cif2-pulse 2s cubic-bezier(.4,0,.6,1) 1s infinite;
+    border: 2px solid var(--color-accent);
+    background: transparent;
+    z-index: var(--layer-1);
+    pointer-events: none;
+    position: absolute;
+    height: 100%;
+    width: 100%;
+    border-radius: 7px;
+}
+#live_gen_items{
+    max-height: 570px;
+    overflow-y: scroll;
+}
+'''
+
+examples = [
     [
         "Create a serene soundscape of a quiet beach at sunset.",  # Text prompt
         "dpmpp-2m-sde",  # Sampler type
@@ -157,12 +184,70 @@ interface = gr.Interface(
         0.3,  # Sigma min
         500  # Sigma max
     ]
-    ]
-)
+]
+with gr.Blocks(theme=theme, css=css) as demo:
+    gr.Markdown("# Stable Audio Multiplayer Live")
+    gr.Markdown("Generate audio with text, share and learn from others how to best prompt this new model")
+    generation_history = gr.Textbox(visible=False)
+    list_size = gr.Number(value=PAGE_SIZE, visible=False)
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your text prompt here")
+            btn_run = gr.Button("Generate")
+            with gr.Accordion("Parameters", open=True): 
+                with gr.Row():
+                    duration = gr.Slider(0, 47, value=20, step=1, label="Duration in Seconds")
+        
+                with gr.Accordion("Advanced parameters", open=False):
+                    steps = gr.Slider(10, 150, value=80, step=10, label="Number of Diffusion Steps")
+                    sampler_type = gr.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms", 
+                                                "k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"], 
+                                                label="Sampler type", value="dpmpp-3m-sde")
+                    with gr.Row():
+                        cfg_scale = gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
+                        sigma_min = gr.Slider(0.0, 5.0, step=0.01, value=0.3, label="Sigma min")
+                        sigma_max = gr.Slider(0.0, 1000.0, step=0.1, value=500, label="Sigma max")
+        with gr.Column() as output_list:
+            output = gr.Audio(type="filepath", label="Generated Audio")
+            with gr.Column(elem_id="live_gen") as community_list:
+                gr.Markdown("# Community generations")
+                with gr.Column(elem_id="live_gen_items"):
+                    @gr.render(inputs=[generation_history, list_size])
+                    def show_output_list(generation_history, list_size):
+                        history_list = generation_history.split(',') if generation_history else []
+                        history_list_latest = history_list[:list_size]
+                        for generation in history_list_latest:
+                            generation_prompt_file = generation.replace('.wav', '.txt')
+                            with open(generation_prompt_file, 'r') as file:
+                                generation_prompt = file.read()
+                            with gr.Group():
+                                gr.Markdown(value=f"### {generation_prompt}")
+                                gr.Audio(value=generation)
+                        
+                        
+                load_more = gr.Button("Load more")
+                load_more.click(fn=increase_list_size, inputs=list_size, outputs=list_size)    
+    
+    gr.Examples(
+        fn=generate_audio,
+        examples=examples,
+        inputs=[prompt, sampler_type, duration, steps, cfg_scale, sigma_min, sigma_max],
+        outputs=output,
+        cache_examples="lazy"
+    )
+    gr.on(
+        triggers=[btn_run.click, prompt.submit],
+        fn=generate_audio, 
+        inputs=[prompt, sampler_type, duration, steps, cfg_scale, sigma_min, sigma_max],
+        outputs=output
+    )
+    btn_run.click(
+        generate_audio, 
+        inputs=[prompt, sampler_type, duration, steps, cfg_scale, sigma_min, sigma_max],
+        outputs=output
+    )
+    demo.load(fn=list_all_outputs, inputs=generation_history, outputs=[generation_history, community_list], every=2)
 
-# Pre-load the model to avoid multiprocessing issues
 model, model_config = load_model()
 
-# Launch the Interface
-interface.queue(max_size=10).launch()
-
+demo.launch()