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import torch
import numpy as np
import gradio as gr
from diffusers import StableAudioPipeline
# 1. Load pipeline on CPU initialized with FP16
pipe = StableAudioPipeline.from_pretrained(
"stabilityai/stable-audio-open-1.0",
torch_dtype=torch.float16,
token=True
)
@spaces.GPU(duration=120)
def generate_audio(
prompt: str,
negative_prompt: str,
seconds: float,
num_steps: int,
guidance_scale: float,
seed: int,
num_variations: int
):
# Move model to CUDA inside ZeroGPU function
pipe.to("cuda")
# Handle seed generation for reproducibility or dynamic variation
if seed == -1:
seed = torch.randint(0, 2**32 - 1, (1,)).item()
generator = torch.Generator(device="cuda").manual_seed(int(seed))
# Run inference with parameters
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
audio_start_in_s=0.0,
audio_end_in_s=seconds,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
num_waveforms_per_prompt=num_variations,
generator=generator
)
# Convert audio tensor outputs to tuple format for Gradio (Sample Rate, Audio Array)
audio_outputs = []
for waveform in output.audios:
# Transpose tensor from (channels, samples) to (samples, channels)
audio_data = waveform.T.cpu().numpy()
audio_outputs.append((44100, audio_data))
# Pad list to match max 4 variation outputs in Gradio UI
while len(audio_outputs) < 4:
audio_outputs.append(None)
return (*audio_outputs, f"Used Seed: {int(seed)}")
# Build Gradio UI
with gr.Blocks(title="Stable Audio Studio Pro") as demo:
gr.Markdown("# 🎵 Stable Audio Generator (With Inference & Variation Controls)")
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(
label="Prompt",
value="Heavy metal door slam in an echoing dungeon, crisp transient impact",
lines=2
)
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="low quality, distortion, noise, background speech",
lines=2
)
# --- Inference Parameters ---
with gr.Accordion("⚙️ Inference Parameters", open=True):
seconds = gr.Slider(minimum=1, maximum=47, value=10, step=0.5, label="Duration (Seconds)")
num_steps = gr.Slider(minimum=20, maximum=200, value=100, step=5, label="Inference Steps")
guidance_scale = gr.Slider(minimum=1.0, maximum=15.0, value=7.0, step=0.5, label="Guidance Scale (CFG)")
# --- Variations Configuration ---
with gr.Accordion("🎲 Variations & Seed Settings", open=True):
num_variations = gr.Slider(minimum=1, maximum=4, value=2, step=1, label="Number of Variations")
seed = gr.Number(label="Seed (-1 for Random)", value=-1, precision=0)
generate_btn = gr.Button("Generate Sound Effects", variant="primary")
seed_info = gr.Textbox(label="Active Seed Used", interactive=False)
# --- Output Audio Variations ---
with gr.Column(scale=1):
gr.Markdown("### Generated Audio Variations")
audio_out_1 = gr.Audio(label="Variation 1")
audio_out_2 = gr.Audio(label="Variation 2")
audio_out_3 = gr.Audio(label="Variation 3")
audio_out_4 = gr.Audio(label="Variation 4")
generate_btn.click(
fn=generate_audio,
inputs=[
prompt,
negative_prompt,
seconds,
num_steps,
guidance_scale,
seed,
num_variations
],
outputs=[
audio_out_1,
audio_out_2,
audio_out_3,
audio_out_4,
seed_info
]
)
demo.launch() |