def synthesize(self, step, wav, wavpath,
sampling_rate, frame_period, num_mcep, cpsyn_flag):
wav_name = basename(wavpath)
# print(wav_name)
f0, timeaxis, sp, ap = world_decompose(wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = pitch_conversion(f0=f0,
mean_log_src=self.test_loader.logf0s_mean_src, std_log_src=self.test_loader.logf0s_std_src,
mean_log_target=self.test_loader.logf0s_mean_trg, std_log_target=self.test_loader.logf0s_std_trg)
coded_sp = world_encode_spectral_envelop(sp=sp, fs=sampling_rate, dim=num_mcep)# to dim 36
coded_sp_norm = (coded_sp - self.test_loader.mcep_mean_src) / self.test_loader.mcep_std_src
coded_sp_norm_tensor = torch.FloatTensor(coded_sp_norm.T).unsqueeze_(0).unsqueeze_(1).to(self.device) # [1, 1, D, T]
conds = torch.FloatTensor(self.test_loader.spk_c_trg).to(self.device) # [1, C]
# print(conds.size())
coded_sp_converted_norm = self.G(coded_sp_norm_tensor, conds).data.cpu().numpy()
coded_sp_converted = np.squeeze(coded_sp_converted_norm).T * self.test_loader.mcep_std_trg + self.test_loader.mcep_mean_trg
coded_sp_converted = np.ascontiguousarray(coded_sp_converted) # TODO why need C contiguous
# decoded_sp_converted = world_decode_spectral_envelop(coded_sp = coded_sp_converted, fs = sampling_rate)
wav_transformed = world_speech_synthesis(f0=f0_converted, coded_sp=coded_sp_converted,
ap=ap, fs=sampling_rate, frame_period=frame_period)
librosa.output.write_wav(
join(self.sample_dir,
'{}-{}_{}-vcto-{}.wav'.format(
str(step),
self.test_loader.src_spk,
wav_name.split('.')[0],
self.test_loader.trg_spk
)
),
wav_transformed,
sampling_rate)
if cpsyn_flag:
wav_cpsyn = world_speech_synthesis(f0=f0, coded_sp=coded_sp,
ap=ap, fs=sampling_rate, frame_period=frame_period)
librosa.output.write_wav(
join(self.sample_dir,
'cpsyn-{}_{}'.format(
self.test_loader.src_spk,
wav_name
)
),
wav_cpsyn,
sampling_rate)
def convert(src_wav_dir, trg_wav_file):
all_src_wav_files = glob.glob(f'{src_wav_dir}/*.wav')
# This regex for src_wav_files creates about 20 output files to get a good sample without taking too
# much time or memory. It can be altered (including setting to a single file or all_src_wav_files)
# to create fewer/more output files.
src_wav_files = glob.glob(f'{src_wav_dir}/p???_0[01][0-9].wav')
src_wavs = [
utils.load_wav(src_wav_file, utils.SAMPLING_RATE)
for src_wav_file in src_wav_files
]
trg_wav = utils.load_wav(trg_wav_file, utils.SAMPLING_RATE)
trg_wav_name = splitext(basename(trg_wav_file))[0]
converted_dir = VCTK_PATH.joinpath('converted_audio',
'trg_' + trg_wav_name)
os.makedirs(converted_dir, exist_ok=True)
src_stats = get_stats(all_src_wav_files)
trg_stats = get_stats([trg_wav_file])
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
G = get_model(device)
_, _, trg_sp, _ = utils.world_decompose(wav=trg_wav,
fs=utils.SAMPLING_RATE,
frame_period=utils.FRAME_PERIOD)
trg_coded_sp = utils.world_encode_spectral_envelop(sp=trg_sp,
fs=utils.SAMPLING_RATE,
dim=utils.NUM_MCEP)
trg_coded_sp_norm = (trg_coded_sp - trg_stats['coded_sps_mean']
) / trg_stats['coded_sps_std']
assert trg_coded_sp_norm.shape[0] >= 8192
trg_coded_sp_norm = trg_coded_sp_norm[:8192, :]
trg_coded_sp_norm_tensor = torch.FloatTensor(
trg_coded_sp_norm.T).unsqueeze_(0).unsqueeze_(1).to(device)
trg_embed = G.trg_downsample(trg_coded_sp_norm_tensor)
with torch.no_grad():
for i, src_wav in enumerate(tqdm(src_wavs)):
f0, _, sp, ap = utils.world_decompose(
wav=src_wav,
fs=utils.SAMPLING_RATE,
frame_period=utils.FRAME_PERIOD)
coded_sp = utils.world_encode_spectral_envelop(
sp=sp, fs=utils.SAMPLING_RATE, dim=utils.NUM_MCEP)
f0_converted = utils.pitch_conversion(
f0=f0,
mean_log_src=src_stats['log_f0s_mean'],
std_log_src=src_stats['log_f0s_std'],
mean_log_target=trg_stats['log_f0s_mean'],
std_log_target=trg_stats['log_f0s_std'])
coded_sp_norm = (coded_sp - src_stats['coded_sps_mean']
) / src_stats['coded_sps_std']
coded_sp_norm_tensor = torch.FloatTensor(
coded_sp_norm.T).unsqueeze_(0).unsqueeze_(1).to(device)
# coded_sp_converted_norm = G(coded_sp_norm_tensor, trg_embed).data.cpu().numpy()
coded_sp_converted_norm = G.forward_with_trg_embed(
coded_sp_norm_tensor, trg_embed)
coded_sp_converted_norm = coded_sp_converted_norm.data.cpu().numpy(
)
coded_sp_converted = np.squeeze(coded_sp_converted_norm).T
coded_sp_converted = coded_sp_converted * trg_stats[
'coded_sps_std'] + trg_stats['coded_sps_mean']
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
coded_sp_converted = coded_sp_converted.astype('double')
wav_transformed = utils.world_speech_synthesis(
f0=f0_converted,
coded_sp=coded_sp_converted,
ap=ap,
fs=utils.SAMPLING_RATE,
frame_period=utils.FRAME_PERIOD)
output_path = converted_dir.joinpath(
'src_' + os.path.basename(src_wav_files[i]))
print(f'Saving to {output_path}')
librosa.output.write_wav(output_path, wav_transformed,
utils.SAMPLING_RATE)
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
soundfile.write(
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)
def test(config):
os.makedirs(join(config.convert_dir, str(config.resume_iters)),
exist_ok=True)
sampling_rate, num_mcep, frame_period = 16000, 36, 5
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
G = Generator().to(device)
test_loader = TestDataset(config)
# Restore model
print(f'Loading the trained models from step {config.resume_iters}...')
G_path = join(config.model_save_dir, f'{config.resume_iters}-G.ckpt')
G.load_state_dict(
torch.load(G_path, map_location=lambda storage, loc: storage))
# Read a batch of testdata
test_wavfiles = test_loader.get_batch_test_data(
batch_size=config.num_converted_wavs)
test_wavs = [load_wav(wavfile, sampling_rate) for wavfile in test_wavfiles]
with torch.no_grad():
for idx, wav in enumerate(test_wavs):
print(len(wav))
wav_name = basename(test_wavfiles[idx])
# print(wav_name)
f0, timeaxis, sp, ap = world_decompose(wav=wav,
fs=sampling_rate,
frame_period=frame_period)
f0_converted = pitch_conversion(
f0=f0,
mean_log_src=test_loader.logf0s_mean_src,
std_log_src=test_loader.logf0s_std_src,
mean_log_target=test_loader.logf0s_mean_trg,
std_log_target=test_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 - test_loader.mcep_mean_src
) / test_loader.mcep_std_src
coded_sp_norm_tensor = torch.FloatTensor(
coded_sp_norm.T).unsqueeze_(0).unsqueeze_(1).to(device)
spk_conds = torch.FloatTensor(test_loader.spk_c_trg).to(device)
# print(spk_conds.size())
coded_sp_converted_norm = G(coded_sp_norm_tensor,
spk_conds).data.cpu().numpy()
coded_sp_converted = np.squeeze(
coded_sp_converted_norm
).T * test_loader.mcep_std_trg + test_loader.mcep_mean_trg
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
print("After being fed into G: ", coded_sp_converted.shape)
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]
librosa.output.write_wav(
join(config.convert_dir, str(config.resume_iters),
f'{wav_id}-vcto-{test_loader.trg_spk}.wav'),
wav_transformed, sampling_rate)
if [True, False][0]:
wav_cpsyn = world_speech_synthesis(f0=f0,
coded_sp=coded_sp,
ap=ap,
fs=sampling_rate,
frame_period=frame_period)
librosa.output.write_wav(
join(config.convert_dir, str(config.resume_iters),
f'cpsyn-{wav_name}'), wav_cpsyn, sampling_rate)
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')
# 重启模型
print(f'从步骤{config.resume_model}开始加载训练过的模型...')
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))
# 遍历config.speakers中所有的发音者对
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)
# 载入发音者
data_loader = ConvertDataset(config,
src_spk=config.speakers[i],
trg_spk=config.speakers[j])
print('---------------------------------------')
print('源：', config.speakers[i], '目标：', config.speakers[j])
print('---------------------------------------')
# 读入一批的测试数据
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})，文件长度：{len(wav)}')
wav_name = basename(src_test_wavfiles[idx])
# 转换wav格式文件为MECP数据
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("在喂入数据到G前：", 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)
# 生成转换后的音频数据
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("在喂入数据到G后：", coded_sp_converted.shape)
# 将数据转换回wav数据
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)