change.py

import gradio as gr
import pyworld
import numpy as np
from scipy.io import wavfile
from wsola import WSOLA
from scipy.signal import firwin, lfilter, resample, filtfilt
from numpy.fft import fft, ifft
import librosa
import soundfile as sf

##########################
#       叠频             #
##########################

def shift_pitch(signal, fs, f_ratio):
    peaks = find_peaks(signal, fs)
    new_signal = psola(signal, peaks, f_ratio)
    return new_signal


def find_peaks(signal, fs, max_hz=950, min_hz=75, analysis_win_ms=40, max_change=1.005, min_change=0.995):
    N = len(signal)
    min_period = fs // max_hz
    max_period = fs // min_hz

    # compute pitch periodicity
    sequence = int(analysis_win_ms / 1000 * fs)  # analysis sequence length in samples
    periods = compute_periods_per_sequence(signal, sequence, min_period, max_period)

    # simple hack to avoid octave error: assume that the pitch should not vary much, restrict range
    mean_period = np.mean(periods)
    max_period = int(mean_period * 1.1)
    min_period = int(mean_period * 0.9)
    periods = compute_periods_per_sequence(signal, sequence, min_period, max_period)

    # find the peaks
    peaks = [np.argmax(signal[:int(periods[0]*1.1)])]
    while True:
        prev = peaks[-1]
        idx = prev // sequence  # current autocorrelation analysis window
        if prev + int(periods[idx] * max_change) >= N:
            break
        # find maximum near expected location
        peaks.append(prev + int(periods[idx] * min_change) +
                np.argmax(signal[prev + int(periods[idx] * min_change): prev + int(periods[idx] * max_change)]))
    return np.array(peaks)


def compute_periods_per_sequence(signal, sequence, min_period, max_period):
    offset = 0  # current sample offset
    periods = []  # period length of each analysis sequence
    N = len(signal)

    while offset < N:
        fourier = fft(signal[offset: offset + sequence])
        fourier[0] = 0  # remove DC component
        autoc = ifft(fourier * np.conj(fourier)).real
        autoc_peak = min_period + np.argmax(autoc[min_period: max_period])
        periods.append(autoc_peak)
        offset += sequence
    return periods


def psola(signal, peaks, f_ratio):
    N = len(signal)
    # Interpolate
    new_signal = np.zeros(N)
    # print('len(peaks) * f_ratio->',len(peaks) * f_ratio)
    new_peaks_ref = np.linspace(0, len(peaks) - 1, int(len(peaks) * f_ratio))
    new_peaks = np.zeros(len(new_peaks_ref)).astype(int)

    for i in range(len(new_peaks)):
        weight = new_peaks_ref[i] % 1
        left = np.floor(new_peaks_ref[i]).astype(int)
        right = np.ceil(new_peaks_ref[i]).astype(int)
        new_peaks[i] = int(peaks[left] * (1 - weight) + peaks[right] * weight)

    # PSOLA
    for j in range(len(new_peaks)):
        # find the corresponding old peak index
        i = np.argmin(np.abs(peaks - new_peaks[j]))
        # get the distances to adjacent peaks
        P1 = [new_peaks[j] if j == 0 else new_peaks[j] - new_peaks[j-1],
              N - 1 - new_peaks[j] if j == len(new_peaks) - 1 else new_peaks[j+1] - new_peaks[j]]
        # edge case truncation
        if peaks[i] - P1[0] < 0:
            P1[0] = peaks[i]
        if peaks[i] + P1[1] > N - 1:
            P1[1] = N - 1 - peaks[i]
        # linear OLA window
        window = list(np.linspace(0, 1, P1[0] + 1)[1:]) + list(np.linspace(1, 0, P1[1] + 1)[1:])
        # center window from original signal at the new peak
        new_signal[new_peaks[j] - P1[0]: new_peaks[j] + P1[1]] += window * signal[peaks[i] - P1[0]: peaks[i] + P1[1]]
    return new_signal


##########################
#       变频             #
##########################


# 低通滤波
def low_cut_filter(x, fs, cutoff=70):
    nyquist = fs // 2
    norm_cutoff = cutoff / nyquist

    # low cut filter
    fil = firwin(255, norm_cutoff, pass_zero=False)
    lcf_x = lfilter(fil, 1, x)

    return lcf_x

# 高频修复    
def high_frequency_completion(x, transformed,f0rate,par):
	x = np.array(x, dtype=np.float64)
	
	f0, time_axis = pyworld.harvest(x, par['fs'], f0_floor=par['minf0'],
                                        f0_ceil=par['maxf0'], frame_period=par['shiftms'])
	spc = pyworld.cheaptrick(x, f0, time_axis, par['fs'],
							 fft_size=par['fftl'])
	ap = pyworld.d4c(x, f0, time_axis, par['fs'], fft_size=par['fftl'])
	
    # 利用0基频进行语音还原
	uf0 = np.zeros(len(f0))
	unvoice_anasyn = pyworld.synthesize(uf0, spc, ap,
                                 par['fs'], frame_period=par['shiftms'])
	
    # 高通滤波 获取原语音中的高频细节
	fil = firwin(255, f0rate, pass_zero=False)
	HPFed_unvoice_anasyn = filtfilt(fil, 1, unvoice_anasyn)

	if len(HPFed_unvoice_anasyn) > len(transformed):
		return transformed + HPFed_unvoice_anasyn[:len(transformed)]
	else:
		transformed[:len(HPFed_unvoice_anasyn)] += HPFed_unvoice_anasyn
		return transformed    

def transform_f0(x,f0rate,config): 
    if f0rate < 1.0:
        completion = True
    else:
        completion = False
        
    fs = config["fs"]
    x = low_cut_filter(x, fs, cutoff=70)
    
    # 利用 wsola 调速
    wsola = WSOLA(config["fs"], 1 / f0rate, shiftms=10)
    wsolaed = wsola.duration_modification(x)
    
    # 利用 resample 调频
    xlen = len(x)
    transformed = resample(wsolaed, xlen)
    
    # 基频变低 进行高频修正
    if completion:
        transformed = high_frequency_completion(x, transformed, f0rate,config)
    
    return transformed

with gr.Blocks() as interface:
    with gr.Row():
        wav_path = gr.Audio(source='microphone',type='filepath')
        
        with gr.Column():
            minf0 = gr.Slider(50, 300, 70, step=10, label="minf0")
            turn_tune = gr.Slider(0.2, 3, 1.5, step=0.1, label="turn_tune")
        with gr.Column():
            maxf0 = gr.Slider(500, 1100, 700, step=10, label="maxf0")
            shiftms = gr.Slider(1, 50, 10, step=1, label="shiftms")
        with gr.Column():
            fr = gr.Slider(0.1, 15, 1, step=0.1, label="fr")

    with gr.Row():
        audio_output = gr.Audio(type='filepath')
    
    section_btn1 = gr.Button("change")
    
    # 图片模型训练
    def change(wav_path,turn_tune,minf0,maxf0,shiftms,fr):
        fs, x = wavfile.read(wav_path)
        x = np.array(x, dtype=np.float64)
        outfile = str(wav_path).split('.')[0] + '-output.wav'
        
        config = {}
        config["fs"] = fs
        config["minf0"] = minf0
        config["maxf0"] = maxf0
        config["shiftms"] = shiftms
        config["fftl"] =1024

        wav_slow = transform_f0(x,turn_tune,config)
        wavfile.write(outfile, fs, wav_slow.astype(np.int16))
        fr = float(fr)
        print('fr->',fr)
        if fr != 1:
            orig_signal, fs = librosa.load(outfile, sr=None)
            N = len(orig_signal)
            f_ratio = fr ** (-2 / 12)
            new_signal = shift_pitch(orig_signal, fs, f_ratio)
            sf.write(outfile,new_signal,fs)

        return outfile
    
    section_btn1.click(change, inputs=[wav_path,turn_tune,minf0,maxf0,shiftms,fr], outputs=[audio_output])

interface.launch(show_api=False)