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import os
import argparse
import torch
import json
from glob import glob
from pyworld import pyworld
from tqdm import tqdm
from scipy.io import wavfile
import cluster
#import h5py
import logging
import utils
logging.getLogger('numba').setLevel(logging.WARNING)
import parselmouth
import librosa
import numpy as np
sampling_rate = 44100
hop_length = 512
def get_f0(path,p_len=None, f0_up_key=0):
x, sr = librosa.load(path, sr=None)
assert sr == sampling_rate
if p_len is None:
p_len = x.shape[0]//hop_length
else:
assert abs(p_len-x.shape[0]//hop_length) < 3, (path, p_len, x.shape)
time_step = hop_length / sampling_rate * 1000
f0_min = 50
f0_max = 1100
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
time_step=time_step / 1000, voicing_threshold=0.6,
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
pad_size=(p_len - len(f0) + 1) // 2
if(pad_size>0 or p_len - len(f0) - pad_size>0):
f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
f0bak = f0.copy()
f0 *= pow(2, f0_up_key / 12)
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
f0_coarse = np.rint(f0_mel).astype(np.int)
return f0_coarse, f0bak
def resize2d(x, target_len):
source = np.array(x)
source[source<0.001] = np.nan
target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
res = np.nan_to_num(target)
return res
def compute_f0(path, c_len=None):
x, sr = librosa.load(path, sr=None)
assert sr == sampling_rate
if c_len is None:
c_len = x.shape[0]//hop_length
f0, t = pyworld.dio(
x.astype(np.double),
fs=sr,
f0_ceil=800,
frame_period=1000 * hop_length / sr,
)
f0 = pyworld.stonemask(x.astype(np.double), f0, t, sampling_rate)
for index, pitch in enumerate(f0):
f0[index] = round(pitch, 1)
assert abs(c_len - x.shape[0]//hop_length) < 3, (c_len, f0.shape)
return None, resize2d(f0, c_len)
def process(filename):
print(filename)
f0path = filename+".f0.npy"
if not os.path.exists(f0path):
cf0, f0 = compute_f0(filename)
np.save(f0path, f0)
else:
f0 = np.load(f0path)
c_len = f0.shape[0]
save_name = filename+".discrete.npy"
if not os.path.exists(save_name):
devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wav, sr = librosa.load(filename+".16k.wav",sr=None)
assert sr == 16000
wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
c = utils.get_cn_hubert_units(hmodel, wav).cpu().squeeze(0)
c = utils.repeat_expand_2d(c, c_len).numpy()
c = cluster.get_cluster_result(c.transpose())
np.save(save_name,c)
else:
c = np.load(save_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--in_dir", type=str, default="dataset/", help="path to input dir")
args = parser.parse_args()
print("Loading hubert for content...")
hmodel = utils.load_cn_model(0 if torch.cuda.is_available() else None)
print("Loaded hubert.")
filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
filenames = [i for i in filenames if not i.endswith(".16k.wav")]
for filename in tqdm(filenames):
process(filename)
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