import matplotlib.pyplot as plt
import numpy as np
from scipy.io.wavfile import write
Load the image
image_path = 'hugging_face.jpg'
image = plt.imread(image_path)
Convert the image to grayscale
grayscale_image = np.mean(image, axis=2)
Define the musical parameters
sample_rate = 44100 # Audio sample rate (Hz)
duration = 0.1 # Duration of each note (seconds)
Define the mapping from pixel values to musical notes
min_note = 40 # MIDI note number for the lowest pixel value
max_note = 80 # MIDI note number for the highest pixel value
Rescale the pixel values to the range [min_note, max_note]
scaled_image = (grayscale_image - np.min(grayscale_image))
scaled_image *= (max_note - min_note) / np.max(scaled_image)
scaled_image += min_note
Generate the audio signal
total_duration = int(duration * sample_rate * grayscale_image.shape[1])
t = np.linspace(0, total_duration / sample_rate, total_duration, endpoint=False)
audio_signal = np.zeros(total_duration)
for i, column in enumerate(scaled_image.T):
start = int(i * duration * sample_rate)
end = int((i + 1) * duration * sample_rate)
audio_signal[start:end] = np.sin(2 * np.pi * column * t[start:end])
Normalize the audio signal
audio_signal /= np.max(np.abs(audio_signal))
audio_signal *= 32767 # Scale the signal to the range of a 16-bit integer
Convert the audio signal to 16-bit integer format
audio_signal = audio_signal.astype(np.int16)
Save the audio signal to a WAV file
output_file = 'hugging_face.wav'
write(output_file, sample_rate, audio_signal)
print(f"Audio file '{output_file}' generated successfully!")
