File size: 4,760 Bytes
200e88e
 
 
9639ae1
 
 
 
 
 
200e88e
 
b95f305
9639ae1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
200e88e
9639ae1
 
 
1e126d1
9639ae1
 
 
 
200e88e
9639ae1
200e88e
9639ae1
 
200e88e
 
 
9639ae1
1e126d1
200e88e
 
9639ae1
200e88e
 
9639ae1
200e88e
 
9639ae1
 
200e88e
 
 
 
 
 
 
9639ae1
200e88e
 
 
9639ae1
200e88e
9639ae1
200e88e
9639ae1
200e88e
9639ae1
 
 
200e88e
9639ae1
 
 
200e88e
9639ae1
 
 
 
 
 
200e88e
9639ae1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
200e88e
9639ae1
 
 
 
 
 
 
200e88e
9639ae1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
200e88e
9639ae1
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
import torch
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("stabilityai/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}")

    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
    )
    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=30, 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")
    ],
    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,  # 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,  # 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,  # 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,  # 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,  # CFG Scale
     
    ]
])


# Pre-load the model to avoid multiprocessing issues
model, model_config = load_model()

# Launch the Interface
interface.launch()