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106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
import os
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import argparse
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import torch
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import json
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from glob import glob
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from pyworld import pyworld
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from tqdm import tqdm
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from scipy.io import wavfile
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import utils
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from mel_processing import mel_spectrogram_torch
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#import h5py
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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import parselmouth
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import librosa
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import numpy as np
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def get_f0(path,p_len=None, f0_up_key=0):
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x, _ = librosa.load(path, 32000)
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if p_len is None:
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p_len = x.shape[0]//320
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else:
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assert abs(p_len-x.shape[0]//320) < 3, (path, p_len, x.shape)
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time_step = 320 / 32000 * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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f0 = parselmouth.Sound(x, 32000).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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pad_size=(p_len - len(f0) + 1) // 2
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if(pad_size>0 or p_len - len(f0) - pad_size>0):
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f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
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f0bak = f0.copy()
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f0 *= pow(2, f0_up_key / 12)
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak
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def resize2d(x, target_len):
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source = np.array(x)
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source[source<0.001] = np.nan
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target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
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res = np.nan_to_num(target)
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return res
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def compute_f0(path, c_len):
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x, sr = librosa.load(path, sr=32000)
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f0, t = pyworld.dio(
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x.astype(np.double),
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fs=sr,
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f0_ceil=800,
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frame_period=1000 * 320 / sr,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, 32000)
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for index, pitch in enumerate(f0):
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f0[index] = round(pitch, 1)
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assert abs(c_len - x.shape[0]//320) < 3, (c_len, f0.shape)
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return None, resize2d(f0, c_len)
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def process(filename):
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tqdm.write(filename)
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save_name = filename+".soft.pt"
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if not os.path.exists(save_name):
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devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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wav, _ = librosa.load(filename, sr=16000)
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wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
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c = utils.get_hubert_content(hmodel, wav)
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torch.save(c.cpu(), save_name)
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else:
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c = torch.load(save_name)
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f0path = filename+".f0.npy"
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if not os.path.exists(f0path):
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cf0, f0 = compute_f0(filename, c.shape[-1] * 2)
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np.save(f0path, f0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--in_dir", type=str, default="dataset/32k", help="path to input dir")
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args = parser.parse_args()
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print("Loading hubert for content...")
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hmodel = utils.get_hubert_model(0 if torch.cuda.is_available() else None)
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print("Loaded hubert.")
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filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
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for filename in tqdm(filenames):
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process(filename)
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