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from multiprocessing import cpu_count
import os
import sys
from scipy import signal
from scipy.io import wavfile
import librosa
import numpy as np
now_directory = os.getcwd()
sys.path.append(now_directory)
from rvc.lib.utils import load_audio
from rvc.train.slicer import Slicer
experiment_directory = sys.argv[1]
input_root = sys.argv[2]
sampling_rate = int(sys.argv[3])
percentage = float(sys.argv[4])
num_processes = cpu_count()
import multiprocessing
class PreProcess:
def __init__(self, sr, exp_dir, per=3.0):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.b_high, self.a_high = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
self.per = per
self.overlap = 0.3
self.tail = self.per + self.overlap
self.max_amplitude = 0.9
self.alpha = 0.75
self.exp_dir = exp_dir
self.gt_wavs_dir = f"{exp_dir}/0_gt_wavs"
self.wavs16k_dir = f"{exp_dir}/1_16k_wavs"
os.makedirs(self.exp_dir, exist_ok=True)
os.makedirs(self.gt_wavs_dir, exist_ok=True)
os.makedirs(self.wavs16k_dir, exist_ok=True)
def normalize_and_write(self, tmp_audio, idx0, idx1):
tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.5:
print(f"{idx0}-{idx1}-{tmp_max}-filtered")
return
tmp_audio = (tmp_audio / tmp_max * (self.max_amplitude * self.alpha)) + (
1 - self.alpha
) * tmp_audio
wavfile.write(
f"{self.gt_wavs_dir}/{idx0}_{idx1}.wav",
self.sr,
tmp_audio.astype(np.float32),
)
tmp_audio = librosa.resample(
tmp_audio, orig_sr=self.sr, target_sr=16000
) # , res_type="soxr_vhq"
wavfile.write(
f"{self.wavs16k_dir}/{idx0}_{idx1}.wav",
16000,
tmp_audio.astype(np.float32),
)
def process_audio(self, path, idx0):
try:
audio = load_audio(path, self.sr)
audio = signal.lfilter(self.b_high, self.a_high, audio)
idx1 = 0
for audio_segment in self.slicer.slice(audio):
i = 0
while 1:
start = int(self.sr * (self.per - self.overlap) * i)
i += 1
if len(audio_segment[start:]) > self.tail * self.sr:
tmp_audio = audio_segment[
start : start + int(self.per * self.sr)
]
self.normalize_and_write(tmp_audio, idx0, idx1)
idx1 += 1
else:
tmp_audio = audio_segment[start:]
idx1 += 1
break
self.normalize_and_write(tmp_audio, idx0, idx1)
except Exception as error:
print(f"{path}: {error}")
def process_audio_multiprocessing(self, infos):
for path, idx0 in infos:
self.process_audio(path, idx0)
def process_audio_multiprocessing_input_directory(self, input_root, num_processes):
try:
infos = [
(f"{input_root}/{name}", idx)
for idx, name in enumerate(sorted(list(os.listdir(input_root))))
]
processes = []
for i in range(num_processes):
p = multiprocessing.Process(
target=self.process_audio_multiprocessing,
args=(infos[i::num_processes],),
)
processes.append(p)
p.start()
for i in range(num_processes):
processes[i].join()
except Exception as error:
print(error)
def preprocess_training_set(input_root, sr, num_processes, exp_dir, per):
pp = PreProcess(sr, exp_dir, per)
print("Starting preprocessing...")
pp.process_audio_multiprocessing_input_directory(input_root, num_processes)
print("Preprocessing completed!")
if __name__ == "__main__":
preprocess_training_set(
input_root, sampling_rate, num_processes, experiment_directory, percentage
)
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