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convert.py
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import argparse
import glob
import torch
import librosa
import numpy as np
import os
from os.path import join, basename
from shutil import copy
from data_loader import to_categorical
from model import Generator
from utils import world_decompose, pitch_conversion, world_encode_spectral_envelop, world_speech_synthesis, wav_padding
class ConvertDataset(object):
"""Dataset for conversion."""
def __init__(self, config, src_spk, trg_spk):
speakers = config.speakers
spk2idx = dict(zip(speakers, range(len(speakers))))
assert trg_spk in speakers, f'The trg_spk should be chosen from {speakers}, but you choose {trg_spk}.'
self.src_spk = src_spk
self.trg_spk = trg_spk
# Source speaker locations.
self.src_spk_stats = np.load(join(config.train_data_dir, f'{self.src_spk}_stats.npz'))
self.src_wav_dir = f'{config.wav_dir}/{self.src_spk}'
self.trg_wav_dir = f'{config.wav_dir}/{self.trg_spk}'
self.src_wav_files = sorted(glob.glob(join(self.src_wav_dir, '*.wav')))
self.trg_wav_files = sorted(glob.glob(join(self.trg_wav_dir, '*.wav')))
# Target speaker locations.
self.trg_spk_stats = np.load(join(config.train_data_dir, f'{self.trg_spk}_stats.npz'))
self.logf0s_mean_src = self.src_spk_stats['log_f0s_mean']
self.logf0s_std_src = self.src_spk_stats['log_f0s_std']
self.logf0s_mean_trg = self.trg_spk_stats['log_f0s_mean']
self.logf0s_std_trg = self.trg_spk_stats['log_f0s_std']
self.mcep_mean_src = self.src_spk_stats['coded_sps_mean']
self.mcep_std_src = self.src_spk_stats['coded_sps_std']
self.mcep_mean_trg = self.trg_spk_stats['coded_sps_mean']
self.mcep_std_trg = self.trg_spk_stats['coded_sps_std']
self.spk_idx_src, self.spk_idx_trg = spk2idx[src_spk], spk2idx[trg_spk]
spk_cat_src = to_categorical([self.spk_idx_src], num_classes=len(speakers))
spk_cat_trg = to_categorical([self.spk_idx_trg], num_classes=len(speakers))
self.spk_c_org = spk_cat_src
self.spk_c_trg = spk_cat_trg
def get_batch_test_data(self, batch_size=4):
batch_data = []
i = 0
while i != batch_size:
wav_file = self.src_wav_files[i]
filename = basename(wav_file)
num = filename.split('.')[0].split('_')[1]
for j in range(len(self.trg_wav_files)):
trg_wav_file = self.trg_wav_files[j]
trg_filename = basename(trg_wav_file)
trg_num = trg_filename.split('.')[0].split('_')[1]
if num == trg_num:
batch_data.append(wav_file)
break
elif j == len(self.trg_wav_files) - 1:
batch_size += 1
i += 1
return batch_data
def load_wav(wavfile, sr=16000):
wav, _ = librosa.load(wavfile, sr=sr, mono=True)
return wav_padding(wav, sr=sr, frame_period=5, multiple=4)
def convert(config):
os.makedirs(join(config.convert_dir, config.resume_model), exist_ok=True)
sampling_rate, num_mcep, frame_period = config.sampling_rate, 36, 5
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Restore model
print(f'Loading the trained models from step {config.resume_model}...')
generator = Generator(num_speakers=config.num_speakers).to(device)
g_path = join(config.model_save_dir, f'{config.resume_model}-G.ckpt')
generator.load_state_dict(torch.load(g_path, map_location=lambda storage, loc: storage))
# for all possible speaker pairs in config.speakers
for i in range(0, len(config.speakers)):
for j in range(0, len(config.speakers)):
if i != j:
target_dir = join(config.convert_dir,
str(config.resume_model),
f'{config.speakers[i]}_to_{config.speakers[j]}')
os.makedirs(target_dir, exist_ok=True)
# Load speakers
data_loader = ConvertDataset(config, src_spk=config.speakers[i], trg_spk=config.speakers[j])
print('---------------------------------------')
print('Source: ', config.speakers[i], ' Target: ', config.speakers[j])
print('---------------------------------------')
# Read a batch of testdata
src_test_wavfiles = data_loader.get_batch_test_data(batch_size=config.num_converted_wavs)
src_test_wavs = [load_wav(wavfile, sampling_rate) for wavfile in src_test_wavfiles]
with torch.no_grad():
for idx, wav in enumerate(src_test_wavs):
print(f'({idx}), file length: {len(wav)}')
wav_name = basename(src_test_wavfiles[idx])
# convert wav to mceps
f0, _, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = pitch_conversion(f0=f0,
mean_log_src=data_loader.logf0s_mean_src,
std_log_src=data_loader.logf0s_std_src,
mean_log_target=data_loader.logf0s_mean_trg,
std_log_target=data_loader.logf0s_std_trg)
coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep)
print("Before being fed into G: ", coded_sp.shape)
coded_sp_norm = (coded_sp - data_loader.mcep_mean_src) / data_loader.mcep_std_src
coded_sp_norm_tensor = torch.FloatTensor(coded_sp_norm.T).unsqueeze_(0).unsqueeze_(1).to(device)
spk_conds = torch.FloatTensor(data_loader.spk_c_trg).to(device)
# Include org_conds if using src and target domain codes.
org_conds = torch.FloatTensor(data_loader.spk_c_org).to(device)
# generate converted speech
coded_sp_converted_norm = generator(coded_sp_norm_tensor, spk_conds).data.cpu().numpy()
coded_sp_converted = np.squeeze(coded_sp_converted_norm).T * data_loader.mcep_std_trg + data_loader.mcep_mean_trg
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
print("After being fed into G: ", coded_sp_converted.shape)
# convert back to wav
wav_transformed = world_speech_synthesis(f0=f0_converted,
coded_sp=coded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period)
wav_id = wav_name.split('.')[0]
# SAVE TARGET SYNTHESIZED
librosa.output.write_wav(join(target_dir, f'{wav_id}-vcto-{data_loader.trg_spk}.wav'),
wav_transformed,
sampling_rate)
# SAVE COPY OF TARGET REFERENCE
wav_num = wav_name.split('.')[0].split('_')[1]
copy(f'{config.wav_dir}/{config.speakers[j]}/{config.speakers[j]}_{wav_num}.wav', target_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Model configuration.
parser.add_argument('--num_converted_wavs', type=int, default=8, help='Number of wavs to convert.')
parser.add_argument('--resume_model', type=str, default=None, help='Model to resume for testing.')
parser.add_argument('--speakers', type=str, nargs='+', required=True, help='Speakers to be converted.')
# Directories.
parser.add_argument('--train_data_dir', type=str, default='./data/mc/train', help='Path to train data directory.')
parser.add_argument('--test_data_dir', type=str, default='./data/mc/test', help='Path to test data directory.')
parser.add_argument('--wav_dir', type=str, default="./data/VCTK-Corpus/wav16", help='Path to wav data directory.')
parser.add_argument('--model_save_dir', type=str, default='./models', help='Path to model save directory.')
parser.add_argument('--convert_dir', type=str, default='./converted', help='Patht to converted wavs directory.')
parser.add_argument('--sampling_rate', type=int, default=22050, help='Sampling rate for converted wavs.')
config = parser.parse_args()
# no. of spks
config.num_speakers = len(config.speakers)
print(config)
if config.resume_model is None:
raise RuntimeError("Please specify the step number for resuming.")
if len(config.speakers) < 2:
raise RuntimeError("Need at least 2 speakers to convert audio.")
convert(config)