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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) |