app.py

import random
import spaces
import os
import uuid

os.putenv('PYTORCH_NVML_BASED_CUDA_CHECK','1')
os.putenv('TORCH_LINALG_PREFER_CUSOLVER','1')
alloc_conf_parts = [
    'expandable_segments:True',
    'pinned_use_background_threads:True'  # Specific to pinned memory.
]
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = ','.join(alloc_conf_parts)
os.environ["SAFETENSORS_FAST_GPU"] = "1"
os.putenv('HF_HUB_ENABLE_HF_TRANSFER','1')

import torch
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
torch.backends.cudnn.allow_tf32 = False
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
torch.backends.cuda.preferred_blas_library="cublas"
torch.backends.cuda.preferred_linalg_library="cusolver"
torch.set_float32_matmul_precision("highest")

import torchaudio
from einops import rearrange
import gradio as gr

from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond

model, model_config = get_pretrained_model("ford442/stable-audio-open-1.0")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
model.to(device,torch.float32)

@spaces.GPU(duration=60)
def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7, use_bfloat=False, use_eval=False):
    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}")
    sample_rate = model_config["sample_rate"]
    sample_size = model_config["sample_size"]
    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
    print("Model moved to device.")
    conditioning = [{
        "prompt": prompt,
        "seconds_start": 0,
        "seconds_total": seconds_total
    }]
    print(f"Conditioning: {conditioning}")
    print("Generating audio...")
    if use_bfloat==True:
        model.to(torch.bfloat16)
    if use_eval==True:
        model.eval()
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    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
    )
    print("Audio generated.")
    output = rearrange(output, "b d n -> d (b n)")
    # 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()
    unique_filename = f"output_{uuid.uuid4().hex}.flac"
    print(f"Saving audio to file: {unique_filename}")
    torchaudio.save(
    unique_filename,
    output,
    sample_rate,
    format="flac",
    encoding="PCM_F",
    bits_per_sample=24
    )
    print(f"Audio saved: {unique_filename}")
    return unique_filename


interface = gr.Interface(
    fn=generate_audio,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"),
        gr.Slider(0, 420, value=30, label="Duration in Seconds"),
        gr.Slider(10, 420, value=100, step=10, label="Number of Diffusion Steps"),
        gr.Slider(1.0, 32.0, value=7.0, step=0.1, label="CFG Scale"),
        gr.Checkbox(value=False, label="Use Brainfloat"),
        gr.Checkbox(value=False, label="Use eval()")
    ],
    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=[
    [
        "Create a serene soundscape of a quiet beach at sunset.",  # Text prompt
         45,  # Duration in Seconds
        100,  # Number of Diffusion Steps
        10.0,  # CFG Scale
    ],
    [
        "Generate an energetic and bustling city street scene with distant traffic and close conversations.",  # Text prompt
        30,  # Duration in Seconds
        120,  # Number of Diffusion Steps
        5.0,  # CFG Scale
    ],
    [
        "Simulate a forest ambiance with birds chirping and wind rustling through the leaves.",  # Text prompt
        60,  # Duration in Seconds
        140,  # Number of Diffusion Steps
        7.5,  # CFG Scale
    ],
    [
        "Recreate a gentle rainfall with distant thunder.",  # Text prompt
       
        35,  # Duration in Seconds
        110,  # Number of Diffusion Steps
        8.0,  # CFG Scale
        
    ],
    [
        "Imagine a jazz cafe environment with soft music and ambient chatter.",  # Text prompt
        25,  # Duration in Seconds
        90,  # Number of Diffusion Steps
        6.0,  # CFG Scale
    ],
    ["Rock beat played in a treated studio, session drumming on an acoustic kit.",
        30,  # Duration in Seconds
        100,  # Number of Diffusion Steps
        7.0,  # CFG Scale
     
    ]
])

interface.launch()