import numpy as np import matplotlib.pyplot as plt from PIL import Image, ImageDraw, ImageFont import librosa import librosa.display import gradio as gr import soundfile as sf import os import logging import tempfile # Constants DEFAULT_FONT_PATH = "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" DEFAULT_SAMPLE_RATE = 22050 # Setup logging logging.basicConfig(level=logging.INFO) # Function for creating a spectrogram image with text def text_to_spectrogram_image(text, base_width=512, height=256, max_font_size=80, margin=10, letter_spacing=5): try: font = ImageFont.truetype(DEFAULT_FONT_PATH, max_font_size) except IOError: logging.warning(f"Font not found at {DEFAULT_FONT_PATH}. Using default font.") font = ImageFont.load_default() draw = ImageDraw.Draw(Image.new('L', (1, 1))) text_width = sum(draw.textbbox((0, 0), char, font=font)[2] - draw.textbbox((0, 0), char, font=font)[0] + letter_spacing for char in text) - letter_spacing text_height = draw.textbbox((0, 0), text[0], font=font)[3] - draw.textbbox((0, 0), text[0], font=font)[1] # Adjust width and height based on text size width = max(base_width, text_width + margin * 2) height = max(height, text_height + margin * 2) image = Image.new('L', (width, height), 'black') draw = ImageDraw.Draw(image) text_x = (width - text_width) // 2 text_y = (height - text_height) // 2 for char in text: draw.text((text_x, text_y), char, font=font, fill='white') char_bbox = draw.textbbox((0, 0), char, font=font) text_x += char_bbox[2] - char_bbox[0] + letter_spacing image = np.array(image) image = np.where(image > 0, 255, image) return image # Converting an image to audio def spectrogram_image_to_audio(image, sr=DEFAULT_SAMPLE_RATE): flipped_image = np.flipud(image) S = flipped_image.astype(np.float32) / 255.0 * 100.0 y = librosa.griffinlim(S) return y # Function for creating an audio file and spectrogram from text def create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing): spec_image = text_to_spectrogram_image(text, base_width, height, max_font_size, margin, letter_spacing) y = spectrogram_image_to_audio(spec_image) with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio: audio_path = temp_audio.name sf.write(audio_path, y, DEFAULT_SAMPLE_RATE) # Create spectrogram from audio S = librosa.feature.melspectrogram(y=y, sr=DEFAULT_SAMPLE_RATE) S_dB = librosa.power_to_db(S, ref=np.max) plt.figure(figsize=(10, 4)) librosa.display.specshow(S_dB, sr=DEFAULT_SAMPLE_RATE, x_axis='time', y_axis='mel') plt.axis('off') plt.tight_layout(pad=0) with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_spectrogram: spectrogram_path = temp_spectrogram.name plt.savefig(spectrogram_path, bbox_inches='tight', pad_inches=0, transparent=True) plt.close() return audio_path, spectrogram_path # Function for displaying the spectrogram of an audio file def display_audio_spectrogram(audio_path): y, sr = librosa.load(audio_path, sr=None) S = librosa.feature.melspectrogram(y=y, sr=sr) S_dB = librosa.power_to_db(S, ref=np.max) plt.figure(figsize=(10, 4)) librosa.display.specshow(S_dB, sr=sr, x_axis='time', y_axis='mel') plt.axis('off') plt.tight_layout(pad=0) with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_spectrogram: spectrogram_path = temp_spectrogram.name plt.savefig(spectrogram_path, bbox_inches='tight', pad_inches=0, transparent=True) plt.close() return spectrogram_path # Converting a downloaded image to an audio spectrogram def image_to_spectrogram_audio(image_path, sr=DEFAULT_SAMPLE_RATE): image = Image.open(image_path).convert('L') image = np.array(image) y = spectrogram_image_to_audio(image, sr) with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio: img2audio_path = temp_audio.name sf.write(img2audio_path, y, sr) return img2audio_path # Gradio interface def gradio_interface_fn(text, base_width, height, max_font_size, margin, letter_spacing): logging.info(f"Generating audio and spectrogram for text:\n{text}\n") audio_path, spectrogram_path = create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing) return audio_path, spectrogram_path def gradio_image_to_audio_fn(upload_image): logging.info(f"Converting image to audio:\n{upload_image}\n") return image_to_spectrogram_audio(upload_image) def gradio_decode_fn(upload_audio): logging.info(f"Generating spectrogram for audio:\n{upload_audio}\n") return display_audio_spectrogram(upload_audio) with gr.Blocks(title='Audio Steganography', theme=gr.themes.Soft(primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg")) as txt2spec: with gr.Tab("Text to Spectrogram"): with gr.Group(): text = gr.Textbox(lines=2, placeholder="Enter your text:", label="Text") with gr.Row(variant='panel'): base_width = gr.Slider(value=512, label="Image Width", visible=False) height = gr.Slider(value=256, label="Image Height", visible=False) max_font_size = gr.Slider(minimum=10, maximum=130, step=5, value=80, label="Font size") margin = gr.Slider(minimum=0, maximum=50, step=1, value=10, label="Indent") letter_spacing = gr.Slider(minimum=0, maximum=50, step=1, value=5, label="Letter spacing") generate_button = gr.Button("Generate") with gr.Column(variant='panel'): with gr.Group(): output_audio = gr.Audio(type="filepath", label="Generated audio") output_spectrogram = gr.Image(type="filepath", label="Spectrogram") generate_button.click(gradio_interface_fn, inputs=[text, base_width, height, max_font_size, margin, letter_spacing], outputs=[output_audio, output_spectrogram]) with gr.Tab("Image to Spectrogram"): with gr.Group(): with gr.Row(variant='panel'): upload_image = gr.Image(type="filepath", label="Upload image") convert_button = gr.Button("Convert to audio") with gr.Column(variant='panel'): output_audio_from_image = gr.Audio(type="filepath", label="Generated audio") convert_button.click(gradio_image_to_audio_fn, inputs=[upload_image], outputs=[output_audio_from_image]) with gr.Tab("Audio Spectrogram"): with gr.Group(): with gr.Row(variant='panel'): upload_audio = gr.Audio(type="filepath", label="Upload audio", scale=3) decode_button = gr.Button("Show spectrogram", scale=2) with gr.Column(variant='panel'): decoded_image = gr.Image(type="filepath", label="Audio Spectrogram") decode_button.click(gradio_decode_fn, inputs=[upload_audio], outputs=[decoded_image]) txt2spec.launch(share=True)