import gradio as gr import matplotlib.pyplot as plt import librosa import numpy as np from PIL import Image, ImageDraw, ImageFont from moviepy.editor import * from moviepy.video.io.VideoFileClip import VideoFileClip def make_bars_image(height_values, index, new_height): # Define the size of the image width = 1024 height = new_height # Create a new image with a transparent background image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0)) # Get the image drawing context draw = ImageDraw.Draw(image) # Define the rectangle width and spacing rect_width = 4 spacing = 4 # Define the list of height values for the rectangles #height_values = [20, 40, 60, 80, 100, 80, 60, 40] num_bars = len(height_values) # Calculate the total width of the rectangles and the spacing total_width = num_bars * rect_width + (num_bars - 1) * spacing # Calculate the starting position for the first rectangle start_x = int((width - total_width) / 2) # Define the buffer size buffer_size = int(80 * 2) # Draw the rectangles from left to right x = start_x for i, height in enumerate(height_values): # Define the rectangle coordinates y0 = buffer_size y1 = height + buffer_size x0 = x x1 = x + rect_width # Draw the rectangle draw.rectangle([x0, y0, x1, y1], fill='white') # Move to the next rectangle position if i < num_bars - 1: x += rect_width + spacing # Rotate the image by 180 degrees image = image.rotate(180) # Mirror the image image = image.transpose(Image.FLIP_LEFT_RIGHT) # Save the image image.save('audio_bars_'+ str(index) + '.png') return 'audio_bars_'+ str(index) + '.png' def db_to_height(db_value): # Scale the dB value to a range between 0 and 1 scaled_value = (db_value + 80) / 80 # Convert the scaled value to a height between 0 and 100 height = scaled_value * 50 return height def infer(title, audio_in, image_in, output_video_path): # Load the audio file audio_path = audio_in audio_data, sr = librosa.load(audio_path) # Get the duration in seconds duration = librosa.get_duration(y=audio_data, sr=sr) # Extract the audio data for the desired time start_time = 0 # start time in seconds end_time = duration # end time in seconds start_index = int(start_time * sr) end_index = int(end_time * sr) audio_data = audio_data[start_index:end_index] # Compute the short-time Fourier transform hop_length = 1024 stft = librosa.stft(audio_data, hop_length=hop_length) spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max) # Get the frequency values freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0]) # Select the indices of the frequency values that correspond to the desired frequencies n_freqs = 114 freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int) # Extract the dB values for the desired frequencies db_values = [] for i in range(spectrogram.shape[1]): db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i]))) # Print the dB values for the first time frame print(db_values[0]) proportional_values = [] for frame in db_values: proportional_frame = [db_to_height(db) for f, db in frame] proportional_values.append(proportional_frame) print(proportional_values[0]) print("AUDIO CHUNK: " + str(len(proportional_values))) # Open the background image background_image = Image.open(image_in) # Resize the image while keeping its aspect ratio bg_width, bg_height = background_image.size aspect_ratio = bg_width / bg_height new_width = 1024 new_height = int(new_width / aspect_ratio) resized_bg = background_image.resize((new_width, new_height)) # Apply black cache for better visibility of the white text bg_cache = Image.open('black_cache.png') # Resize black_cache image to fit with the width black_cache_width, black_cache_height = bg_cache.size new_bc_width = 1024 new_bc_height = black_cache_height * 2 bg_cache = bg_cache.resize((new_bc_width, new_bc_height), Image.LANCZOS) resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache) # Create a new ImageDraw object draw = ImageDraw.Draw(resized_bg) # Define the text to be added text = title font = ImageFont.truetype("Lato-Regular.ttf", 16) text_color = (255, 255, 255) # white color # Calculate the position of the text #text_width, text_height = draw.textsize(text, font=font) x = int(30 * 2) y = new_height - (70 * 2) # Draw the text on the image draw.text((x, y), text, fill=text_color, font=font) # Save the resized image resized_bg.save('resized_background.jpg') generated_frames = [] for i, frame in enumerate(proportional_values): bars_img = make_bars_image(frame, i, new_height) bars_img = Image.open(bars_img) # Paste the audio bars image on top of the background image fresh_bg = Image.open('resized_background.jpg') fresh_bg.paste(bars_img, (0, 0), mask=bars_img) # Save the image fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg') generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg') print(generated_frames) # Create a video clip from the images clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time)) audio_clip = AudioFileClip(audio_in) clip = clip.set_audio(audio_clip) # Set the output codec codec = 'libx264' audio_codec = 'aac' # Save the video to a file clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec) retimed_clip = VideoFileClip("my_video.mp4") # Set the desired frame rate new_fps = 25 # Create a new clip with the new frame rate new_clip = retimed_clip.set_fps(new_fps) # Save the new clip as a new video file new_clip.write_videofile(output_video_path, codec=codec, audio_codec=audio_codec) # Visualize the audio bars plt.figure(figsize=(10, 4)) librosa.display.specshow(spectrogram, sr=sr, x_axis='time', y_axis='log') plt.colorbar(format='%+2.0f dB') plt.title('Audio Bars Visualization') # Save the image as a JPG file output_path = 'image_out.jpg' plt.savefig(output_path, dpi=300, bbox_inches='tight') #test make image bars #bars_img = make_bars_image(proportional_values[0]) return output_video_path, 'image_out.jpg' gr.Interface(fn=infer, inputs=[gr.Textbox(placeholder='FIND A GOOD TITLE'), gr.Audio(source='upload', type='filepath'), gr.Image(source='upload', type='filepath'), gr.Textbox(label="Output video path", value="my_final_video.mp4", visible=False)], outputs=[gr.Video(label='video result'), gr.Image(label='spectrogram image')], title='Animated Audio Visualizer', description='

Upload an audio file, upload a background image, choose a good title, click submit.

').launch()