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import hydra
import hydra.utils as utils
from pathlib import Path
import torch
import numpy as np
from tqdm import tqdm
import soundfile as sf
from model_encoder import Encoder, Encoder_lf0
from model_decoder import Decoder_ac
from model_encoder import SpeakerEncoder as Encoder_spk
import os
import random
from glob import glob
import subprocess
from spectrogram import logmelspectrogram
import kaldiio
import resampy
import pyworld as pw
def select_wavs(paths, min_dur=2, max_dur=8):
pp = []
for p in paths:
x, fs = sf.read(p)
if len(x)/fs>=min_dur and len(x)/fs<=8:
pp.append(p)
return pp
def extract_logmel(wav_path, mean, std, sr=16000):
# wav, fs = librosa.load(wav_path, sr=sr)
wav, fs = sf.read(wav_path)
if fs != sr:
wav = resampy.resample(wav, fs, sr, axis=0)
fs = sr
#wav, _ = librosa.effects.trim(wav, top_db=15)
# duration = len(wav)/fs
assert fs == 16000
peak = np.abs(wav).max()
if peak > 1.0:
wav /= peak
mel = logmelspectrogram(
x=wav,
fs=fs,
n_mels=80,
n_fft=400,
n_shift=160,
win_length=400,
window='hann',
fmin=80,
fmax=7600,
)
mel = (mel - mean) / (std + 1e-8)
tlen = mel.shape[0]
frame_period = 160/fs*1000
f0, timeaxis = pw.dio(wav.astype('float64'), fs, frame_period=frame_period)
f0 = pw.stonemask(wav.astype('float64'), f0, timeaxis, fs)
f0 = f0[:tlen].reshape(-1).astype('float32')
nonzeros_indices = np.nonzero(f0)
lf0 = f0.copy()
lf0[nonzeros_indices] = np.log(f0[nonzeros_indices]) # for f0(Hz), lf0 > 0 when f0 != 0
mean, std = np.mean(lf0[nonzeros_indices]), np.std(lf0[nonzeros_indices])
lf0[nonzeros_indices] = (lf0[nonzeros_indices] - mean) / (std + 1e-8)
return mel, lf0
@hydra.main(config_path="config/convert.yaml")
def convert(cfg):
src_wav_paths = glob('/Dataset/VCTK-Corpus/wav48_silence_trimmed/p225/*mic1.flac') # modified to absolute wavs path, can select any unseen speakers
src_wav_paths = select_wavs(src_wav_paths)
tar1_wav_paths = glob('/Dataset/VCTK-Corpus/wav48_silence_trimmed/p231/*mic1.flac') # can select any unseen speakers
tar2_wav_paths = glob('/Dataset/VCTK-Corpus/wav48_silence_trimmed/p243/*mic1.flac') # can select any unseen speakers
# tar1_wav_paths = select_wavs(tar1_wav_paths)
# tar2_wav_paths = select_wavs(tar2_wav_paths)
tar1_wav_paths = [sorted(tar1_wav_paths)[0]]
tar2_wav_paths = [sorted(tar2_wav_paths)[0]]
print('len(src):', len(src_wav_paths), 'len(tar1):', len(tar1_wav_paths), 'len(tar2):', len(tar2_wav_paths))
tmp = cfg.checkpoint.split('/')
steps = tmp[-1].split('-')[-1].split('.')[0]
out_dir = f'test/{tmp[-3]}-{tmp[-2]}-{steps}'
out_dir = Path(utils.to_absolute_path(out_dir))
out_dir.mkdir(exist_ok=True, parents=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = Encoder(**cfg.model.encoder)
encoder_lf0 = Encoder_lf0()
encoder_spk = Encoder_spk()
decoder = Decoder_ac(dim_neck=64)
encoder.to(device)
encoder_lf0.to(device)
encoder_spk.to(device)
decoder.to(device)
print("Load checkpoint from: {}:".format(cfg.checkpoint))
checkpoint_path = utils.to_absolute_path(cfg.checkpoint)
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
encoder.load_state_dict(checkpoint["encoder"])
encoder_spk.load_state_dict(checkpoint["encoder_spk"])
decoder.load_state_dict(checkpoint["decoder"])
encoder.eval()
encoder_spk.eval()
decoder.eval()
mel_stats = np.load('./data/mel_stats.npy')
mean = mel_stats[0]
std = mel_stats[1]
feat_writer = kaldiio.WriteHelper("ark,scp:{o}.ark,{o}.scp".format(o=str(out_dir)+'/feats.1'))
for i, src_wav_path in tqdm(enumerate(src_wav_paths, 1)):
if i>10:
break
mel, lf0 = extract_logmel(src_wav_path, mean, std)
if i % 2 == 1:
ref_wav_path = random.choice(tar2_wav_paths)
tar = 'tarMale_'
else:
ref_wav_path = random.choice(tar1_wav_paths)
tar = 'tarFemale_'
ref_mel, _ = extract_logmel(ref_wav_path, mean, std)
mel = torch.FloatTensor(mel.T).unsqueeze(0).to(device)
lf0 = torch.FloatTensor(lf0).unsqueeze(0).to(device)
ref_mel = torch.FloatTensor(ref_mel.T).unsqueeze(0).to(device)
out_filename = os.path.basename(src_wav_path).split('.')[0]
with torch.no_grad():
z, _, _, _ = encoder.encode(mel)
lf0_embs = encoder_lf0(lf0)
spk_embs = encoder_spk(ref_mel)
output = decoder(z, lf0_embs, spk_embs)
logmel = output.squeeze(0).cpu().numpy()
feat_writer[out_filename] = logmel
feat_writer[out_filename+'_src'] = mel.squeeze(0).cpu().numpy().T
feat_writer[out_filename+'_ref'] = ref_mel.squeeze(0).cpu().numpy().T
subprocess.call(['cp', src_wav_path, out_dir])
feat_writer.close()
print('synthesize waveform...')
cmd = ['parallel-wavegan-decode', '--checkpoint', \
'/vocoder/checkpoint-3000000steps.pkl', \
'--feats-scp', f'{str(out_dir)}/feats.1.scp', '--outdir', str(out_dir)]
subprocess.call(cmd)
if __name__ == "__main__":
convert()
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