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| import numpy as np | |
| import soundfile as sf | |
| import librosa | |
| import os | |
| from scipy.signal import butter, filtfilt, stft, istft | |
| # Step 1: Load audio files | |
| def load_audio(audio_path): | |
| audio, sr = librosa.load(audio_path, sr=48000) | |
| #audio, fs = sf.read(audio_path) | |
| return audio, sr | |
| # Step 2: Detect effective signal bandwidth | |
| def detect_bandwidth_org(signal, fs, energy_threshold=0.95): | |
| f, t, Zxx = stft(signal, fs=fs) | |
| psd = np.abs(Zxx)**2 | |
| total_energy = np.sum(psd) | |
| cumulative_energy = np.cumsum(np.sum(psd, axis=1)) / total_energy | |
| f_low = f[np.argmax(cumulative_energy > (1 - energy_threshold))] | |
| f_high = f[np.argmax(cumulative_energy >= energy_threshold)] | |
| return f_low, f_high | |
| def detect_bandwidth(signal, fs, energy_threshold=0.99): | |
| f, t, Zxx = stft(signal, fs=fs) | |
| psd = np.abs(Zxx)**2 | |
| total_energy = np.sum(psd) | |
| cumulative_energy = np.cumsum(np.sum(psd, axis=1)) / total_energy | |
| # Exclude DC component (0 Hz) | |
| valid_indices = np.where(f > 0)[0] | |
| f_low = f[valid_indices][np.argmax(cumulative_energy[valid_indices] > (1 - energy_threshold))] | |
| f_high = f[np.argmax(cumulative_energy >= energy_threshold)] | |
| return f_low, f_high | |
| # Step 3: Apply bandpass and lowpass filters | |
| def bandpass_filter(signal, fs, f_low, f_high): | |
| nyquist = 0.5 * fs | |
| low = f_low / nyquist | |
| high = f_high / nyquist | |
| b, a = butter(N=4, Wn=[low, high], btype='band') | |
| return filtfilt(b, a, signal) | |
| def lowpass_filter(signal, fs, cutoff): | |
| nyquist = 0.5 * fs | |
| cutoff_normalized = cutoff / nyquist | |
| b, a = butter(N=4, Wn=cutoff_normalized, btype='low') | |
| return filtfilt(b, a, signal) | |
| def highpass_filter(signal, fs, cutoff): | |
| nyquist = 0.5 * fs | |
| cutoff_normalized = cutoff / nyquist | |
| b, a = butter(N=4, Wn=cutoff_normalized, btype='high') | |
| return filtfilt(b, a, signal) | |
| # Step 4: Replace bandwidth | |
| def replace_bandwidth(signal1, signal2, fs, f_low, f_high): | |
| # Extract effective band from signal1 | |
| #effective_band = bandpass_filter(signal1, fs, f_low, f_high) | |
| effective_band = lowpass_filter(signal1, fs, f_high) | |
| # Extract lowpass band from signal2 | |
| #signal2_lowpass = lowpass_filter(signal2, fs, f_high) | |
| signal2_highpass = highpass_filter(signal2, fs, f_high) | |
| # Match lengths of the two signals | |
| min_length = min(len(effective_band), len(signal2_highpass)) | |
| effective_band = effective_band[:min_length] | |
| signal2_highpass = signal2_highpass[:min_length] | |
| # Combine the two signals | |
| return signal2_highpass + effective_band | |
| # Step 5: Smooth transitions | |
| def smooth_transition(signal1, signal2, fs, transition_band=100): | |
| fade = np.linspace(0, 1, int(transition_band * fs / 1000)) | |
| crossfade = np.concatenate([fade, np.ones(len(signal1) - len(fade))]) | |
| min_length = min(len(signal1), len(signal2)) | |
| smoothed_signal = (1 - crossfade) * signal2[:min_length] + crossfade * signal1[:min_length] | |
| return smoothed_signal | |
| # Step 6: Save audio | |
| def save_audio(file_path, audio, fs): | |
| sf.write(file_path, audio, fs) | |
| def bandwidth_sub(low_bandwidth_audio, high_bandwidth_audio, fs=48000): | |
| # Detect effective bandwidth of the first signal | |
| f_low, f_high = detect_bandwidth(low_bandwidth_audio, fs) | |
| # Replace the lower frequency of the second audio | |
| substituted_audio = replace_bandwidth(low_bandwidth_audio, high_bandwidth_audio, fs, f_low, f_high) | |
| # Optional: Smooth the transition | |
| smoothed_audio = smooth_transition(substituted_audio, low_bandwidth_audio, fs) | |
| return smoothed_audio | |
| # Main process | |
| if __name__ == "__main__": | |
| low_spectra_dir = 'LJSpeech_22k' | |
| upper_spectra_dir = 'LJSpeech_22k_hifi-sr_speech_g_03925000' | |
| output_dir = upper_spectra_dir+'_restored' | |
| if not os.path.exists(output_dir): | |
| os.mkdir(output_dir) | |
| filelist = [file for file in os.listdir(low_spectra_dir) if file.endswith('.wav')] | |
| for audio_name in filelist: | |
| audio1, fs1 = load_audio(low_spectra_dir + "/" + audio_name) # Source for effective bandwidth | |
| audio2, fs2 = load_audio(upper_spectra_dir + "/" + audio_name.replace('.wav', '_generated.wav')) # Target audio to replace lower frequencies | |
| if fs1 != 48000 or fs2 != 48000: | |
| raise ValueError("Both audio files must have a sampling rate of 48 kHz.") | |
| # Detect effective bandwidth of the first signal | |
| f_low, f_high = detect_bandwidth(audio1, fs1) | |
| print(f"Effective bandwidth: {f_low} Hz to {f_high} Hz") | |
| # Replace the lower frequency of the second audio | |
| replaced_audio = replace_bandwidth(audio1, audio2, fs2, f_low, f_high) | |
| # Optional: Smooth the transition | |
| smoothed_audio = smooth_transition(replaced_audio, audio1, fs1) | |
| # Save the result | |
| save_audio(output_dir+"/"+audio_name, smoothed_audio, fs2) | |