Create app.py

app.py

@@ -0,0 +1,231 @@
+import torch
+import torchaudio
+from einops import rearrange
+import gradio as gr
+import spaces
+import os
+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'],
+)
+
+
+def load_model():
+    model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
+    print("Loading model...Done")
+    return model, model_config
+
+@spaces.GPU(duration=120) 
+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}")
+
+    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}")
+
+    print("Generating audio...")
+    output = generate_diffusion_cond(
+        model,
+        steps=steps,
+        cfg_scale=cfg_scale,
+        conditioning=conditioning,
+        sample_size=sample_size,
+        sigma_min=sigma_min_slider,
+        sigma_max=sigma_max_slider,
+        sampler_type=sampler_type_dropdown,#"dpmpp-3m-sde",
+        device=device
+    )
+    print("Audio generated.")
+    output = rearrange(output, "b d n -> d (b n)")
+    print("Audio rearranged.")
+    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    max_length = sample_rate * seconds_total
+    if output.shape[1] > max_length:
+        output = output[:, :max_length]
+        print(f"Audio trimmed to {seconds_total} seconds.")
+    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}")
+    torchaudio.save(unique_filename, output, sample_rate)
+    print(f"Audio saved: {unique_filename}")
+    with open(unique_textfile, "w") as file:
+        file.write(prompt)
+    return unique_filename
+
+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 = [
+    [
+        "serene soundscape of a beach at sunset.",  # Text prompt
+        "dpmpp-2m-sde",  # Sampler type
+        45,  # Duration in Seconds
+        100,  # Number of Diffusion Steps
+        10,  # CFG Scale
+        0.5,  # Sigma min
+        800  # Sigma max
+    ],
+    [
+        "clapping crowd",  # Text prompt
+        "dpmpp-3m-sde",  # Sampler type
+        30,  # Duration in Seconds
+        100,  # Number of Diffusion Steps
+        7,  # CFG Scale
+        0.5,  # Sigma min
+        500  # Sigma max
+    ],
+    [
+        "forest ambiance birds chirping wind rustling.",  # Text prompt
+        "k-dpm-fast",  # Sampler type
+        60,  # Duration in Seconds
+        140,  # Number of Diffusion Steps
+        7.5,  # CFG Scale
+        0.3,  # Sigma min
+        700  # Sigma max
+    ],
+    [
+        "gentle rainfall distant thunder.",  # Text prompt
+        "dpmpp-3m-sde",  # Sampler type
+        35,  # Duration in Seconds
+        110,  # Number of Diffusion Steps
+        8,  # CFG Scale
+        0.1,  # Sigma min
+        500  # Sigma max
+    ],
+    [
+        "cafe environment soft edm techno music ambient chatter.",  # Text prompt
+        "k-lms",  # Sampler type
+        25,  # Duration in Seconds
+        90,  # Number of Diffusion Steps
+        6,  # CFG Scale
+        0.4,  # Sigma min
+        650  # Sigma max
+    ],
+    ["Rock beat drumming acoustic guitar.",
+      "dpmpp-2m-sde",  # Sampler type
+        30,  # Duration in Seconds
+        100,  # Number of Diffusion Steps
+        7,  # CFG Scale
+        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
+    )
+    demo.load(fn=list_all_outputs, inputs=generation_history, outputs=[generation_history, community_list], every=2)
+
+model, model_config = load_model()
+
+demo.launch()