🎤🎶 Elevate Your Sound Game: Recording and Processing Audio with Python! 🎧🔊

import os import librosa import librosa.display import matplotlib.pyplot as plt import warnings import numpy as np warnings.filterwarnings("ignore", category=RuntimeWarning) def load_audio(file_path, duration=None, offset=0): audio_data, sample_rate = librosa.load(file_path, duration=duration, offset=offset) return audio_data, sample_rate def plot_waveform(audio_data, sample_rate, output_path): plt.figure(figsize=(10, 4)) librosa.display.waveshow(audio_data, sr=sample_rate) plt.title("Waveform") plt.xlabel("Time (seconds)") plt.ylabel("Amplitude") plt.savefig(os.path.join(output_path, "waveform.png")) plt.show() plt.close() def plot_spectrogram(audio_data, sample_rate, output_path): plt.figure(figsize=(10, 4)) spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate) librosa.display.specshow(librosa.power_to_db(spectrogram, ref=np.max), y_axis='mel', x_axis='time') plt.colorbar(format='%+2.0f dB') plt.title("Spectrogram") plt.savefig(os.path.join(output_path, "spectrogram.png")) plt.show() plt.close() if __name__ == "__main__": input_file = "recorded_voice_sounddevice.wav" output_path = "." # You can change this to the desired output directory audio_data, sample_rate = load_audio(input_file) # Print some information about the audio file print(f"Audio data shape: {audio_data.shape}") print(f"Sample rate: {sample_rate} Hz") # Plot the audio waveform and spectrogram plot_waveform(audio_data, sample_rate, output_path) plot_spectrogram(audio_data, sample_rate, output_path) print("Plots saved as 'waveform.png' and 'spectrogram.png' in the current directory.")