def save_world_wav(feats, model_name, filename):
# feats = [f0, sp, ap, sp_coded, labels]
if isinstance(feats[3], torch.Tensor):
feats[3] = feats[3].cpu().numpy()
if hp.normalise_mels:
feats[3] = _unnormalise_coded_sp(feats[3])
path = os.path.join(hp.sample_set_dir, model_name)
if not os.path.exists(path):
os.makedirs(path)
path = os.path.join(path, filename)
# print("Made path.")
feats[3] = np.ascontiguousarray(feats[3], dtype=np.float64)
# print("Made contiguous.")
# print(feats[3].shape)
decoded_sp = decode_spectral_envelope(feats[3], hp.sr, fft_size=hp.n_fft)
# print("Decoded.")
# f0_converted = norm.pitch_conversion(f0, speaker, target)
wav = synthesize(feats[0], decoded_sp, feats[1], hp.sr)
# Audio(wav,rate=hp.sr)
# librosa.display.waveplot(y=wav, sr=hp.sr)
# print("Sythesized wav.")
save_wav(wav, path)
def world_decode_spectral_envelop(coded_sp, fs):
# 将之前编码降维后的数据恢复以前的维度
fftlen = pyworld.get_cheaptrick_fft_size(fs)
decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
return decoded_sp
def world_decode_spectral_envelop(coded_sp, fs):
fftlen = pyworld.get_cheaptrick_fft_size(fs)
# coded_sp = coded_sp.astype(np.float32)
# coded_sp = np.ascontiguousarray(coded_sp)
decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
return decoded_sp
def world_decode_spectral_env(spectral_env_mel, settings):
mfcc = dct(spectral_env_mel) / np.sqrt(settings['coded_dim'] * 2)
fftlen = pyworld.get_cheaptrick_fft_size(settings['sample_rate'])
spectral_env = pyworld.decode_spectral_envelope(mfcc,
settings['sample_rate'],
fftlen)
return spectral_env
def synthesis(ori_path, aim_sp, aim_spkid):
print('synthesizing ...')
wav, _ = librosa.load(ori_path, sr=hp.SR, mono=True, dtype=np.float64)
f0, timeaxis = pw.harvest(wav, hp.SR)
sp_per_timeaxis_before = pw.cheaptrick(wav,
f0,
timeaxis,
hp.SR,
fft_size=hp.N_FFT) # 1024 压缩到 513 维
# ori_decoded_sp = pw.decode_spectral_envelope(ori_sp, hp.SR, fft_size=hp.N_FFT)
# print('f0.shape = ')
# print(f0)
ap = pw.d4c(wav, f0, timeaxis, hp.SR, fft_size=hp.N_FFT)
aim_decoded_sp = pw.decode_spectral_envelope(
aim_sp, hp.SR, fft_size=hp.N_FFT) # 转换/解码 后的sp：
print('解码后的513维度的aim_decoded_sp = ')
print(aim_decoded_sp.shape)
print(aim_decoded_sp[399][:])
synwav = pw.synthesize(f0, aim_decoded_sp, ap, hp.SR)
print(f'synthesize done. path : ./convert_to_{aim_spkid}_test1.wav')
librosa.output.write_wav(f'./convert_to_{aim_spkid}_test1.wav',
synwav,
sr=hp.SR)
def convertFeaturesIntoWav(f0seq, MCEPseq, APseq, fs, frame_period=5.0):
contNumpy_MCEPseq = np.ascontiguousarray(MCEPseq.T, dtype=np.float64)
fftlen = pyworld.get_cheaptrick_fft_size(fs)
spectrogram = pyworld.decode_spectral_envelope(contNumpy_MCEPseq, fs,
fftlen)
# print(f"dtypes. f0seq:{f0seq.dtype}, spectrogram:{spectrogram.dtype}, APseq:{APseq.dtype}")
wav = pyworld.synthesize(f0seq, spectrogram, APseq, fs, frame_period)
return wav.astype(np.float32)
def world_decode_spectral_envelop(coded_sp, fs):
fftlen = pyworld.get_cheaptrick_fft_size(fs)
#coded_sp = coded_sp.astype(np.float32)
#coded_sp = np.ascontiguousarray(coded_sp)
decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
return decoded_sp
def test(self):
"""Translate speech using StarGAN ."""
# Load the trained generator.
self.restore_model(self.test_iters)
norm = Normalizer()
# Set data loader.
d, speaker = TestSet(self.test_dir).test_data(self.src_speaker)
targets = self.trg_speaker
for target in targets:
print(target)
assert target in speakers
label_t = self.spk_enc.transform([target])[0]
if label_t == [0]: label_t = [1, 0]
elif label_t == [1]: label_t = [0, 1]
label_t = np.asarray([label_t])
with torch.no_grad():
for filename, content in d.items():
f0 = content['f0']
ap = content['ap']
sp_norm_pad = self.pad_coded_sp(content['coded_sp_norm'])
convert_result = []
for start_idx in range(0,
sp_norm_pad.shape[1] - FRAMES + 1,
FRAMES):
one_seg = sp_norm_pad[:, start_idx:start_idx + FRAMES]
one_seg = torch.FloatTensor(one_seg).to(self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = torch.FloatTensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg, l).data.cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:, 0:content['coded_sp_norm'].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap, SAMPLE_RATE)
name = f'{speaker}-{target}_iter{self.test_iters}_{filename}'
path = os.path.join(self.result_dir, name)
print(f'[save]:{path}')
librosa.output.write_wav(path, wav, SAMPLE_RATE)
def inv_world_spectrogram(f0, sp, ap, sr=_sr, **kwargs):
"""world声码器频谱转为语音。"""
frame_period = kwargs.get("frame_period", pw.default_frame_period)
f0_floor = kwargs.get("f0_floor", pw.default_f0_floor)
fft_size = kwargs.get("fft_size", pw.get_cheaptrick_fft_size(sr, f0_floor))
sp_dec = pw.decode_spectral_envelope(sp, sr, fft_size=fft_size)
ap_dec = pw.decode_aperiodicity(ap, sr, fft_size=fft_size)
y = pw.synthesize(f0, sp_dec, ap_dec, sr, frame_period=frame_period)
return y
def mcep2wav(mcep, f0, ap):
f0 = f0.astype(np.float64)
ap = ap.astype(np.float64)
mcep = mcep.astype(np.float64)
decoded_sp = pyworld.decode_spectral_envelope(mcep,
sampling_rate,
fft_size=n_fft)
wav = pyworld.synthesize(f0, decoded_sp, ap, sampling_rate)
return wav
def worldDecodeSpectralEnvelop(coded_sp: np.ndarray,
fs: int = SAMPLE_RATE) -> np.ndarray:
'''
MCEPsをスペクトル包絡に戻す
Parameters
----------
coded_sp: np.ndarray
MCEPsのデータ
fs: int, default SAMPLE_RATE
サンプリング周波数
Returns
-------
decoded_sp: np.ndarray
スペクトル包絡
'''
fftlen = pyworld.get_cheaptrick_fft_size(fs)
decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
return decoded_sp
def save_world_wav(feats, filename):
# feats = [f0, sp, ap, sp_coded, labels]
if isinstance(feats[3], torch.Tensor):
feats[3] = feats[3].cpu().numpy()
if hp.normalise:
feats[3] = _unnormalise_coded_sp(feats[3])
# path = os.path.join(hp.sample_set_dir, model_name)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
# path = os.path.join(path, filename)
feats[3] = np.ascontiguousarray(feats[3], dtype=np.float64)
decoded_sp = decode_spectral_envelope(feats[3], hp.sr, fft_size=hp.n_fft)
wav = synthesize(feats[0], decoded_sp, feats[1], hp.sr)
save_wav(wav, filename)
def synthesis(ori_path, aim_sp, aim_spkid):
print('synthesizing ...')
wav, _ = librosa.load(ori_path, sr=hp.SR, mono=True, dtype=np.float64)
f0, timeaxis = pw.harvest(wav, hp.SR, frame_period=10)
sp_per_timeaxis_before = pw.cheaptrick(wav,
f0,
timeaxis,
hp.SR,
fft_size=hp.N_FFT) # 1024 压缩到 513 维
ap = pw.d4c(wav, f0, timeaxis, hp.SR, fft_size=hp.N_FFT)
aim_decoded_sp = pw.decode_spectral_envelope(
aim_sp, hp.SR, fft_size=hp.N_FFT) # 转换/解码 后的sp：维度从60变成513
print('line23: f0.shape = ' + str(f0.shape) + 'aim_decoded_sp.shape = ' +
str(aim_decoded_sp.shape) + 'ap.shape = ' + str(ap.shape))
print('\n line26 : aim_sp.shape = ' + str(aim_sp.shape))
synwav = pw.synthesize(f0, aim_decoded_sp, ap, hp.SR)
print(f'synthesize done. path : ./convert_to_{aim_spkid}_test1.wav')
librosa.output.write_wav(f'./convert_to_{aim_spkid}_test1.wav',
synwav,
sr=hp.SR)
def world_decode_spectral_envelop(coded_sp, fs):
# Decode Mel-cepstral to sp
fftlen = pyworld.get_cheaptrick_fft_size(fs)
decoded_sp = pyworld.decode_spectral_envelope(coded_sp, fs, fftlen)
return decoded_sp
def train(self):
# 衰减的学习率缓存
g_lr = self.g_lr
d_lr = self.d_lr
c_lr = self.c_lr
# 开始训练步骤数为0
start_iters = 0
# 如果存在就跳过
if self.resume_iters:
pass
# 调用定义的个性化标准化方法
norm = Normalizer()
# iter用来生成迭代器，这里用来迭代加载数据集
data_iter = iter(self.data_loader)
print('开始训练......')
# 记录当前时间，now函数取当前时间
start_time = datetime.now()
# 利用总迭代次数来进行遍历
for i in range(start_iters, self.num_iters):
# =================================================================================== #
# 1.预处理输入数据 #
# =================================================================================== #
# 获取真实的图像和对应标签标签
try:
# next方法为迭代下一个迭代器
# 利用自定义的加载器获取真实x值，发音者标签在组中索引与源标签
x_real, speaker_idx_org, label_org = next(data_iter)
except:
# 如果迭代器有问题就再转换为迭代器一次然后迭代
data_iter = iter(self.data_loader)
x_real, speaker_idx_org, label_org = next(data_iter)
# 随机生成目标域标签
# torch.randperm返回一个从0到参数-1范围的随机数组
# 因为标签二值化了，所以这里的标签是10组成的，所以一共有label_org.size(0)个标签
# 获得的是随机索引
rand_idx = torch.randperm(label_org.size(0))
# 根据随机数作为源标签的索引作为目标标签数
label_trg = label_org[rand_idx]
# 同理得到随机目标发音者
speaker_idx_trg = speaker_idx_org[rand_idx]
# to表示使用cpu或者gpu运行
x_real = x_real.to(self.device) # 输入数据
label_org = label_org.to(self.device) # 源域one-hot格式标签
label_trg = label_trg.to(self.device) # 目标域ont-hot格式标签
speaker_idx_org = speaker_idx_org.to(self.device) # 源域标签
speaker_idx_trg = speaker_idx_trg.to(self.device) # 目标域标签
# =================================================================================== #
# 2.训练判别器 #
# =================================================================================== #
# 用真实音频数据计算损失
# nn.CrossEntropyLoss()为交叉熵损失函数，但是不是普通的形式，而是主要是将softmax-log-NLLLoss合并到一块得到的结果。
CELoss = nn.CrossEntropyLoss()
# 调用分类器计算真实数据
cls_real = self.C(x_real)
# 计算对应的域分类损失，即用交叉熵实现
cls_loss_real = CELoss(input=cls_real, target=speaker_idx_org)
# 重置缓冲区，具体实现在下面
self.reset_grad()
# tensor.backward为自动求导函数
cls_loss_real.backward()
# optimizer.step这个方法会更新模型所有的参数以提升学习率，一般在backward函数后根据其计算的梯度来更新参数
self.c_optimizer.step()
# 记录中
loss = {}
# 从真实域分类损失张量中获取元素值
# item()得到一个元素张量里面的元素值
loss['C/C_loss'] = cls_loss_real.item()
# 基于源数据的D判断结果
out_r = self.D(x_real, label_org)
# 用假音频帧计算损失
# 根据真实样本与目标标签生成生成样本
x_fake = self.G(x_real, label_trg)
# detach截断反向传播的梯度流，从而让梯度不影响判别器D
# 基于生成样本的D判断结果
out_f = self.D(x_fake.detach(), label_trg)
# torch.nn.Function.binary_cross_entropy_with_logits度量目标逻辑和输出逻辑之间的二进制交叉熵的函数
# 接受任意形状的输入，target要求与输入形状一致。切记：target的值必须在[0,N-1]之间，其中N为类别数，否则会出现莫名其妙的错误，比如loss为负数。
# 计算其实就是交叉熵，不过输入不要求在0，1之间，该函数会自动添加sigmoid运算
# 返回一个填充了标量值1的张量，其大小与输入相同。torch.ones_like(input)
# 相当于torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)
# binary_cross_entropy_with_logits和binary_cross_entropy的区别
# 有一个（类）损失函数名字中带了with_logits. 而这里的logits指的是,该损失函数已经内部自带了计算logit的操作，
# 无需在传入给这个loss函数之前手动使用sigmoid/softmax将之前网络的输入映射到[0,1]之间
d_loss_t = F.binary_cross_entropy_with_logits(input=out_f,target=torch.zeros_like(out_f, dtype=torch.float)) + \
F.binary_cross_entropy_with_logits(input=out_r, target=torch.ones_like(out_r, dtype=torch.float))
# 生成样本的分类结果
out_cls = self.C(x_fake)
# 交叉熵计算生成样本的域分类损失
d_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# 计算梯度惩罚的损失
alpha = torch.rand(x_real.size(0), 1, 1, 1).to(self.device)
# 计算x_hat
# requires_grad_设置积分方法，将requires_grad是否积分的属性设置为真
# 取一个随机数混合真实样本和生成样本得到一个x尖
x_hat = (alpha * x_real.data +
(1 - alpha) * x_fake.data).requires_grad_(True)
# 计算混合样本和目标标签的判别结果
out_src = self.D(x_hat, label_trg)
# 调用自定义方法得到处理导数后的数据
d_loss_gp = self.gradient_penalty(out_src, x_hat)
# 计算判别器的总体损失
d_loss = d_loss_t + self.lambda_cls * d_loss_cls + 5 * d_loss_gp
# 调用自定义方法重置梯度变化缓冲区
self.reset_grad()
# 对D的损失求导
d_loss.backward()
# 更新模型判别器D参数
self.d_optimizer.step()
# loss['D/d_loss_t'] = d_loss_t.item()
# loss['D/loss_cls'] = d_loss_cls.item()
# loss['D/D_gp'] = d_loss_gp.item()
# 获取判别器损失
loss['D/D_loss'] = d_loss.item()
# =================================================================================== #
# 3.训练生成器 #
# =================================================================================== #
# 进行模运算，判读更新时间
if (i + 1) % self.n_critic == 0:
# 源至目标域
# 利用真实样本和目标标签生成生成样本
x_fake = self.G(x_real, label_trg)
# 判别生成样本与目标标签
g_out_src = self.D(x_fake, label_trg)
# 将生成与目标标签的损失与相同大小纯1张量计算交叉熵得到生成G损失
g_loss_fake = F.binary_cross_entropy_with_logits(
input=g_out_src,
target=torch.ones_like(g_out_src, dtype=torch.float))
# 得到真实样本通过域分类器得到的类别
out_cls = self.C(x_real)
# 计算C计算类别与输入的类别的交叉熵损失即G的分类损失
g_loss_cls = CELoss(input=out_cls, target=speaker_idx_org)
# 目标至源域
# 通过G将生成样本转换为源标签
x_reconst = self.G(x_fake, label_org)
# 得到循环一致性损失，即通过G转回来的损失，按道理这两个是同样的
# l1_loss为L1损失函数，即平均绝对误差
g_loss_rec = F.l1_loss(x_reconst, x_real)
# 源到源域(身份一致性损失).
# 通过真实样本与源标签生成，按道理也是生成x_real
x_fake_iden = self.G(x_real, label_org)
# 利用L1损失函数计算
id_loss = F.l1_loss(x_fake_iden, x_real)
# 后退和优化
# 得到生成器的总体损失函数
g_loss = g_loss_fake + self.lambda_cycle * g_loss_rec +\
self.lambda_cls * g_loss_cls + self.lambda_identity * id_loss
# 重置梯度变化缓冲区
self.reset_grad()
# 对G损失求导
g_loss.backward()
# 更新生成器参数
self.g_optimizer.step()
# 记录对应的损失
loss['G/loss_fake'] = g_loss_fake.item()
loss['G/loss_rec'] = g_loss_rec.item()
loss['G/loss_cls'] = g_loss_cls.item()
loss['G/loss_id'] = id_loss.item()
loss['G/g_loss'] = g_loss.item()
# =================================================================================== #
# 4.其他 #
# =================================================================================== #
# 打印训练相关信息
if (i + 1) % self.log_step == 0:
# 得到训练时间
et = datetime.now() - start_time
# 截取后面的时间段
et = str(et)[:-7]
# 耗时与迭代次数
log = "耗时：[{}], 迭代次数：[{}/{}]".format(et, i + 1, self.num_iters)
# 打印对应损失值
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
# 如果调用tensorboard来记录训练过程
if self.use_tensorboard:
for tag, value in loss.items():
# 添加到log中
self.logger.scalar_summary(tag, value, i + 1)
# 翻译固定数据进行调试
if (i + 1) % self.sample_step == 0:
# torch.no_grad是一个上下文管理器，被该语句包括起来的部分将不会track 梯度
# 所有依赖他的tensor会全部变成True，反向传播时就不会自动求导了，反向传播就不会保存梯度，因此大大节约了显存或者说内存。
with torch.no_grad():
# 调用自定义方法，定义一个路由，并随机选取一个发音者作为测试数据
d, speaker = TestSet(self.test_dir).test_data()
# random.choice返回参数的随机项
# 随机在speakers中选择一个不是目标的发音者
target = random.choice(
[x for x in speakers if x != speaker])
# 将二值化的标签组取出第一个作为目标
# LabelBinary.transfrom方法将复杂类标签转换为二进制标签
label_t = self.spk_enc.transform([target])[0]
# np.asarray将python原生列表或元组形式的现有数据来创建numpy数组
label_t = np.asarray([label_t])
# 取出字典中的文件名与内容
for filename, content in d.items():
f0 = content['f0']
ap = content['ap']
# 调用自定义方法处理对应的数据
sp_norm_pad = self.pad_coded_sp(
content['coded_sp_norm'])
convert_result = []
for start_idx in range(
0, sp_norm_pad.shape[1] - FRAMES + 1, FRAMES):
one_seg = sp_norm_pad[:,
start_idx:start_idx + FRAMES]
one_seg = torch.FloatTensor(one_seg).to(
self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = torch.FloatTensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).data.cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:,
0:content['coded_sp_norm'].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(
f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap,
SAMPLE_RATE)
name = f'{speaker}-{target}_iter{i+1}_{filename}'
path = os.path.join(self.sample_dir, name)
print(f'[save]:{path}')
librosa.output.write_wav(path, wav, SAMPLE_RATE)
# 保存模型检查点
if (i + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(i + 1))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(i + 1))
C_path = os.path.join(self.model_save_dir,
'{}-C.ckpt'.format(i + 1))
torch.save(self.G.state_dict(), G_path)
torch.save(self.D.state_dict(), D_path)
torch.save(self.C.state_dict(), C_path)
print('Saved model checkpoints into {}...'.format(
self.model_save_dir))
# 衰减学习率
if (i + 1) % self.lr_update_step == 0 and (i + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= (self.g_lr / float(self.num_iters_decay))
d_lr -= (self.d_lr / float(self.num_iters_decay))
c_lr -= (self.c_lr / float(self.num_iters_decay))
self.update_lr(g_lr, d_lr, c_lr)
print('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(
g_lr, d_lr))
def main(args):
if os.path.isdir('test'):
rmtree('test')
os.mkdir('test')
#x, fs = sf.read('utterance/vaiueo2d.wav')
x, fs = sf.read('utterance/p226_002.wav')
# x, fs = librosa.load('utterance/vaiueo2d.wav', dtype=np.float64)
# 1. A convient way
f0, sp, ap = pw.wav2world(x, fs) # use default options
y = pw.synthesize(f0, sp, ap, fs, pw.default_frame_period)
# 2. Step by step
# 2-1 Without F0 refinement
_f0, t = pw.dio(x,
fs,
f0_floor=50.0,
f0_ceil=600.0,
channels_in_octave=2,
frame_period=args.frame_period,
speed=args.speed)
_sp = pw.cheaptrick(x, _f0, t, fs)
_ap = pw.d4c(x, _f0, t, fs)
_y = pw.synthesize(_f0, _sp, _ap, fs, args.frame_period)
# librosa.output.write_wav('test/y_without_f0_refinement.wav', _y, fs)
sf.write('test/y_without_f0_refinement.wav', _y, fs)
# 2-2 DIO with F0 refinement (using Stonemask)
f0 = pw.stonemask(x, _f0, t, fs)
sp = pw.cheaptrick(x, f0, t, fs)
ap = pw.d4c(x, f0, t, fs)
y = pw.synthesize(f0, sp, ap, fs, args.frame_period)
# librosa.output.write_wav('test/y_with_f0_refinement.wav', y, fs)
sf.write('test/y_with_f0_refinement.wav', y, fs)
# 2-3 Harvest with F0 refinement (using Stonemask)
_f0_h, t_h = pw.harvest(x, fs)
f0_h = pw.stonemask(x, _f0_h, t_h, fs)
sp_h = pw.cheaptrick(x, f0_h, t_h, fs)
ap_h = pw.d4c(x, f0_h, t_h, fs)
y_h = pw.synthesize(f0_h, sp_h, ap_h, fs, pw.default_frame_period)
# librosa.output.write_wav('test/y_harvest_with_f0_refinement.wav', y_h, fs)
sf.write('test/y_harvest_with_f0_refinement.wav', y_h, fs)
# 2-4 DIO with F0 refinement (using Stonemask). Code and restore sp, ap.
code_sp = pw.code_spectral_envelope(sp, fs, 80)
code_ap = pw.code_aperiodicity(ap, fs)
fft_size = (sp.shape[1] - 1) * 2
rest_sp = pw.decode_spectral_envelope(code_sp, fs, fft_size)
rest_ap = pw.decode_aperiodicity(code_ap, fs, fft_size)
y_r = pw.synthesize(f0, rest_sp, rest_ap, fs, args.frame_period)
sf.write('test/y_with_f0_refinement_code_and_restore.wav', y_r, fs)
print("fft size: {:d}".format(fft_size))
print("coded sp shape: ({:d}, {:d})".format(code_sp.shape[0],
code_sp.shape[1]))
print("coded ap shape: ({:d}, {:d})".format(code_ap.shape[0],
code_ap.shape[1]))
# 2-5 DIO with F0 refinement (using Stonemask). Code and restore sp, ap. frame_shift: 12.5 ms, frame_length: 50.0 ms
f0_xx, t_xx = pw.dio(x,
fs,
f0_floor=50.0,
f0_ceil=600.0,
channels_in_octave=2,
frame_period=12.5,
speed=args.speed)
f0_xx = pw.stonemask(x, f0_xx, t_xx, fs)
sp_xx = pw.cheaptrick(x, f0_xx, t_xx, fs)
ap_xx = pw.d4c(x, f0_xx, t_xx, fs)
code_sp_xx = pw.code_spectral_envelope(sp_xx, fs, 80)
code_ap_xx = pw.code_aperiodicity(ap_xx, fs)
fft_size = (sp_xx.shape[1] - 1) * 2
rest_sp_xx = pw.decode_spectral_envelope(code_sp_xx, fs, fft_size)
rest_ap_xx = pw.decode_aperiodicity(code_ap_xx, fs, fft_size)
y_r_xx = pw.synthesize(f0_xx, rest_sp_xx, rest_ap_xx, fs, 12.5)
sf.write(
'test/y_with_f0_refinement_code_and_restore_frame_period_12.5.wav',
y_r_xx, fs)
print("coded sp_xx shape: ({:d}, {:d})".format(code_sp_xx.shape[0],
code_sp_xx.shape[1]))
print("coded ap_xx shape: ({:d}, {:d})".format(code_ap_xx.shape[0],
code_ap_xx.shape[1]))
# Comparison
savefig('test/wavform.png', [x, _y, y, y_h, y_r, y_r_xx])
savefig('test/sp.png', [_sp, sp, sp_h, rest_sp, rest_sp_xx])
savefig('test/ap.png', [_ap, ap, ap_h, rest_ap, rest_ap_xx], log=False)
savefig('test/f0.png', [_f0, f0, f0_h, f0_xx])
print('Please check "test" directory for output files')
def decode_spectral_envelop(coded_spect, sampling_rate):
fftlen = pyworld.get_cheaptrick_fft_size(sampling_rate)
decoded_spect = pyworld.decode_spectral_envelope(coded_spect,
sampling_rate, fftlen)
return decoded_spect
sp = pw.cheaptrick(x, f0, t, fs) # extract smoothed spectrogram
ap = pw.d4c(x, f0, t, fs) # extract aperiodicity
end = timer()
print('Feature Extraction:', end - start, 'seconds')
# f0_new
from copy import deepcopy # to avoid call by reference!!
f0_new = deepcopy(f0) # 1-58 59-138 139-198 // 269-360 // 429-522
f0_new[1:198] = np.flip(f0_new[1:198], 0) # reverse pitch
f0_new[269:360] = f0_new[269:360] + 62 #E(330hz) -> G (392hz)
f0_new[429:522] = f0_new[429:522] + 193 #E(330hz) -> G(523hz)
#%% reduce dimension of spectral envelope and aperiodicity.
enc_sp = pw.code_spectral_envelope(sp, fs, number_of_dimensions=32)
dec_sp = pw.decode_spectral_envelope(enc_sp,
fs,
fft_size=(sp.shape[1] - 1) * 2)
enc_ap = pw.code_aperiodicity(ap, fs)
dec_ap = pw.decode_aperiodicity(enc_ap, fs, fft_size=(ap.shape[1] - 1) * 2)
#%%
y = pw.synthesize(f0, sp, ap, fs)
librosa.output.write_wav('y_EyesNose_short_resynthesis.wav', y, fs)
#%%
y = pw.synthesize(f0, dec_sp, ap, fs)
librosa.output.write_wav('y_EyesNose_short_resynthesis_sp_decode_32.wav', y,
fs)
#%% synthesis using new f0
y = pw.synthesize(f0_new, sp, ap, fs)
def train(self):
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
c_lr = self.c_lr
start_iters = 0
if self.resume_iters:
pass
norm = Normalizer()
data_iter = iter(self.data_loader)
print("Start training......")
start_time = datetime.now()
for i in range(start_iters, self.num_iters):
# Preprocess input data
# Fetch real images and labels.
try:
x_real, speaker_idx_org, label_org = next(data_iter)
except:
data_iter = iter(self.data_loader)
x_real, speaker_idx_org, label_org = next(data_iter)
# Generate target domain labels randomly.
rand_idx = flow.randperm(label_org.size(0))
label_trg = label_org[rand_idx]
speaker_idx_trg = speaker_idx_org[rand_idx]
x_real = x_real.to(self.device)
# Original domain one-hot labels.
label_org = label_org.to(self.device)
# Target domain one-hot labels.
label_trg = label_trg.to(self.device)
speaker_idx_org = speaker_idx_org.to(self.device)
speaker_idx_trg = speaker_idx_trg.to(self.device)
# Train the discriminator
# Compute loss with real audio frame.
CELoss = nn.CrossEntropyLoss()
cls_real = self.C(x_real)
cls_loss_real = CELoss(input=cls_real, target=speaker_idx_org)
self.reset_grad()
cls_loss_real.backward()
self.c_optimizer.step()
# Logging.
loss = {}
loss["C/C_loss"] = cls_loss_real.item()
out_r = self.D(x_real, label_org)
# Compute loss with fake audio frame.
x_fake = self.G(x_real, label_trg)
out_f = self.D(x_fake.detach(), label_trg)
d_loss_t = nn.BCEWithLogitsLoss()(
input=out_f, target=flow.zeros_like(
out_f).float()) + nn.BCEWithLogitsLoss()(
input=out_r, target=flow.ones_like(out_r).float())
out_cls = self.C(x_fake)
d_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# Compute loss for gradient penalty.
alpha = flow.rand(x_real.size(0), 1, 1, 1).to(self.device)
x_hat = ((alpha * x_real +
(1 - alpha) * x_fake).detach().requires_grad_(True))
out_src = self.D(x_hat, label_trg)
# TODO: Second-order derivation is not currently supported in oneflow, so gradient penalty cannot be used temporarily.
if self.use_gradient_penalty:
d_loss_gp = self.gradient_penalty(out_src, x_hat)
d_loss = d_loss_t + self.lambda_cls * d_loss_cls + 5 * d_loss_gp
else:
d_loss = d_loss_t + self.lambda_cls * d_loss_cls
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
loss["D/D_loss"] = d_loss.item()
# Train the generator
if (i + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(x_real, label_trg)
g_out_src = self.D(x_fake, label_trg)
g_loss_fake = nn.BCEWithLogitsLoss()(
input=g_out_src, target=flow.ones_like(g_out_src).float())
out_cls = self.C(x_real)
g_loss_cls = CELoss(input=out_cls, target=speaker_idx_org)
# Target-to-original domain.
x_reconst = self.G(x_fake, label_org)
g_loss_rec = nn.L1Loss()(x_reconst, x_real)
# Original-to-Original domain(identity).
x_fake_iden = self.G(x_real, label_org)
id_loss = nn.L1Loss()(x_fake_iden, x_real)
# Backward and optimize.
g_loss = (g_loss_fake + self.lambda_cycle * g_loss_rec +
self.lambda_cls * g_loss_cls +
self.lambda_identity * id_loss)
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss["G/loss_fake"] = g_loss_fake.item()
loss["G/loss_rec"] = g_loss_rec.item()
loss["G/loss_cls"] = g_loss_cls.item()
loss["G/loss_id"] = id_loss.item()
loss["G/g_loss"] = g_loss.item()
# Miscellaneous
# Print out training information.
if (i + 1) % self.log_step == 0:
et = datetime.now() - start_time
et = str(et)[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, i + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
# Translate fixed images for debugging.
if (i + 1) % self.sample_step == 0:
with flow.no_grad():
d, speaker = TestSet(self.test_dir).test_data()
target = random.choice(
[x for x in speakers if x != speaker])
label_t = self.spk_enc.transform([target])[0]
label_t = np.asarray([label_t])
for filename, content in d.items():
f0 = content["f0"]
ap = content["ap"]
sp_norm_pad = self.pad_coded_sp(
content["coded_sp_norm"])
convert_result = []
for start_idx in range(
0, sp_norm_pad.shape[1] - FRAMES + 1, FRAMES):
one_seg = sp_norm_pad[:,
start_idx:start_idx + FRAMES]
one_seg = flow.Tensor(one_seg).to(self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = flow.Tensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).detach().cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:,
0:content["coded_sp_norm"].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(
f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap,
SAMPLE_RATE)
name = f"{speaker}-{target}_iter{i+1}_{filename}"
path = os.path.join(self.sample_dir, name)
print(f"[save]:{path}")
sf.write(path, wav, SAMPLE_RATE)
# Save model checkpoints.
if (i + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
"{}-G".format(i + 1))
D_path = os.path.join(self.model_save_dir,
"{}-D".format(i + 1))
C_path = os.path.join(self.model_save_dir,
"{}-C".format(i + 1))
flow.save(self.G.state_dict(), G_path)
flow.save(self.D.state_dict(), D_path)
flow.save(self.C.state_dict(), C_path)
print("Saved model checkpoints into {}...".format(
self.model_save_dir))
# Decay learning rates.
if (i + 1) % self.lr_update_step == 0 and (i + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= self.g_lr / float(self.num_iters_decay)
d_lr -= self.d_lr / float(self.num_iters_decay)
c_lr -= self.c_lr / float(self.num_iters_decay)
self.update_lr(g_lr, d_lr, c_lr)
print("Decayed learning rates, g_lr: {}, d_lr: {}.".format(
g_lr, d_lr))
def save_states(global_step, writer, mel_outputs, linear_outputs, attn, mel, y,
input_lengths, checkpoint_dir=None):
print("Save intermediate states at step {}".format(global_step))
# idx = np.random.randint(0, len(input_lengths))
idx = min(1, len(input_lengths) - 1)
input_length = input_lengths[idx]
# Alignment
# Multi-hop attention
if attn is not None and attn.dim() == 4:
for i, alignment in enumerate(attn):
alignment = alignment[idx].cpu().data.numpy()
tag = "alignment_layer{}".format(i + 1)
writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
# save files as well for now
alignment_dir = join(checkpoint_dir, "alignment_layer{}".format(i + 1))
os.makedirs(alignment_dir, exist_ok=True)
path = join(alignment_dir, "step{:09d}_layer_{}_alignment.png".format(
global_step, i + 1))
save_alignment(path, alignment)
# Save averaged alignment
alignment_dir = join(checkpoint_dir, "alignment_ave")
os.makedirs(alignment_dir, exist_ok=True)
path = join(alignment_dir, "step{:09d}_alignment.png".format(global_step))
alignment = attn.mean(0)[idx].cpu().data.numpy()
save_alignment(path, alignment)
tag = "averaged_alignment"
writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
# Predicted mel spectrogram
if mel_outputs is not None:
mel_output = mel_outputs[idx].cpu().data.numpy()
if hparams.vocoder != "world":
mel_output = prepare_spec_image(audio._denormalize(mel_output))
writer.add_image("Predicted mel spectrogram", mel_output, global_step)
else:
mel_output_prep = mel_output
try:
writer.add_image("Predicted WORLD output", mel_output_prep, global_step)
except:
pass
mel_output = denormalize(mel_output)
nfft = pw.get_cheaptrick_fft_size(hparams.sample_rate)
f0 = mel_output[:,0].astype(np.float64)
sp = pw.decode_spectral_envelope(mel_output[:,1:(hparams.coded_env_dim+1)].astype(np.float64), hparams.sample_rate, nfft)
ap = pw.decode_aperiodicity(mel_output[:,(hparams.coded_env_dim+1):hparams.num_mels].astype(np.float64), hparams.sample_rate, nfft)
signal = pw.synthesize(f0, sp, ap, hparams.sample_rate, pw.default_frame_period)
path = join(checkpoint_dir, "step{:09d}_out.wav".format(
global_step))
audio.save_wav(signal, path)
try:
signal /= np.max(np.abs(signal))
writer.add_audio("Target audio signal", signal, global_step, sample_rate=fs)
except:
print("Unexpected error :", sys.exc_info())
mel_tgt = mel[idx].cpu().data.numpy()
mel_tgt = denormalize(mel_tgt)
f0 = mel_tgt[:,0].astype(np.float64)
sp = pw.decode_spectral_envelope(mel_tgt[:,1:(hparams.coded_env_dim+1)].astype(np.float64), hparams.sample_rate, nfft)
ap = pw.decode_aperiodicity(mel_tgt[:,(hparams.coded_env_dim+1):hparams.num_mels].astype(np.float64), hparams.sample_rate, nfft)
signal = pw.synthesize(f0, sp, ap, hparams.sample_rate, pw.default_frame_period)
try:
signal /= np.max(np.abs(signal))
writer.add_audio("Target audio signal", signal, global_step, sample_rate=hparams.sample_rate)
except:
print("Unexpected error :", sys.exc_info())
# Predicted spectrogram
if linear_outputs is not None:
linear_output = linear_outputs[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear_output))
writer.add_image("Predicted linear spectrogram", spectrogram, global_step)
# Predicted audio signal
signal = audio.inv_spectrogram(linear_output.T)
signal /= np.max(np.abs(signal))
path = join(checkpoint_dir, "step{:09d}_predicted.wav".format(
global_step))
try:
writer.add_audio("Predicted audio signal", signal, global_step, sample_rate=fs)
except Exception as e:
warn(str(e))
pass
audio.save_wav(signal, path)
# Target mel spectrogram
if mel_outputs is not None:
mel_output = mel[idx].cpu().data.numpy()
mel_output = prepare_spec_image(audio._denormalize(mel_output))
writer.add_image("Target mel spectrogram", mel_output, global_step)
# Target spectrogram
if linear_outputs is not None:
linear_output = y[idx].cpu().data.numpy()
spectrogram = prepare_spec_image(audio._denormalize(linear_output))
writer.add_image("Target linear spectrogram", spectrogram, global_step)
#ei
path = join(checkpoint_dir, "step{:09d}_mel_target.npy".format(
global_step))
mel_output = mel[idx].cpu().data.numpy()
np.save(path, denormalize(mel_output))
path = join(checkpoint_dir, "step{:09d}_mel_out.npy".format(
global_step))
mel_output = denormalize(mel_outputs[idx].cpu().data.numpy())
np.save(path, mel_output)
def train(self):
# Learning rate cache for decaying.
g_lr = self.g_lr
d_lr = self.d_lr
c_lr = self.c_lr
start_iters = 0
if self.resume_iters:
pass
norm = Normalizer()
data_iter = iter(self.data_loader)
print('Start training......')
start_time = datetime.now()
for i in range(start_iters, self.num_iters):
# =================================================================================== #
# 1. Preprocess input data #
# =================================================================================== #
# Fetch real images and labels.
try:
x_real, speaker_idx_org, label_org = next(data_iter)
except:
data_iter = iter(self.data_loader)
x_real, speaker_idx_org, label_org = next(data_iter)
# Generate target domain labels randomly.
rand_idx = torch.randperm(label_org.size(0))
label_trg = label_org[rand_idx]
speaker_idx_trg = speaker_idx_org[rand_idx]
x_real = x_real.to(self.device) # Input images.
label_org = label_org.to(
self.device) # Original domain one-hot labels.
label_trg = label_trg.to(
self.device) # Target domain one-hot labels.
speaker_idx_org = speaker_idx_org.to(
self.device) # Original domain labels
speaker_idx_trg = speaker_idx_trg.to(
self.device) #Target domain labels
# =================================================================================== #
# 2. Train the discriminator #
# =================================================================================== #
# Compute loss with real audio frame.
CELoss = nn.CrossEntropyLoss()
cls_real = self.C(x_real)
cls_loss_real = CELoss(input=cls_real, target=speaker_idx_org)
self.reset_grad()
cls_loss_real.backward()
self.c_optimizer.step()
# Logging.
loss = {}
loss['C/C_loss'] = cls_loss_real.item()
out_r = self.D(x_real, label_org)
# Compute loss with fake audio frame.
x_fake = self.G(x_real, label_trg)
out_f = self.D(x_fake.detach(), label_trg)
d_loss_t = F.binary_cross_entropy_with_logits(input=out_f,target=torch.zeros_like(out_f, dtype=torch.float)) + \
F.binary_cross_entropy_with_logits(input=out_r, target=torch.ones_like(out_r, dtype=torch.float))
out_cls = self.C(x_fake)
d_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# Compute loss for gradient penalty.
alpha = torch.rand(x_real.size(0), 1, 1, 1).to(self.device)
x_hat = (alpha * x_real.data +
(1 - alpha) * x_fake.data).requires_grad_(True)
out_src = self.D(x_hat, label_trg)
d_loss_gp = self.gradient_penalty(out_src, x_hat)
d_loss = d_loss_t + self.lambda_cls * d_loss_cls + 5 * d_loss_gp
self.reset_grad()
d_loss.backward()
self.d_optimizer.step()
# loss['D/d_loss_t'] = d_loss_t.item()
# loss['D/loss_cls'] = d_loss_cls.item()
# loss['D/D_gp'] = d_loss_gp.item()
loss['D/D_loss'] = d_loss.item()
# =================================================================================== #
# 3. Train the generator #
# =================================================================================== #
if (i + 1) % self.n_critic == 0:
# Original-to-target domain.
x_fake = self.G(x_real, label_trg)
g_out_src = self.D(x_fake, label_trg)
g_loss_fake = F.binary_cross_entropy_with_logits(
input=g_out_src,
target=torch.ones_like(g_out_src, dtype=torch.float))
out_cls = self.C(x_fake)
g_loss_cls = CELoss(input=out_cls, target=speaker_idx_trg)
# Target-to-original domain.
x_reconst = self.G(x_fake, label_org)
g_loss_rec = F.l1_loss(x_reconst, x_real)
# Original-to-Original domain(identity).
x_fake_iden = self.G(x_real, label_org)
id_loss = F.l1_loss(x_fake_iden, x_real)
# Backward and optimize.
g_loss = g_loss_fake + self.lambda_cycle * g_loss_rec +\
self.lambda_cls * g_loss_cls + self.lambda_identity * id_loss
self.reset_grad()
g_loss.backward()
self.g_optimizer.step()
# Logging.
loss['G/loss_fake'] = g_loss_fake.item()
loss['G/loss_rec'] = g_loss_rec.item()
loss['G/loss_cls'] = g_loss_cls.item()
loss['G/loss_id'] = id_loss.item()
loss['G/g_loss'] = g_loss.item()
# =================================================================================== #
# 4. Miscellaneous #
# =================================================================================== #
# Print out training information.
if (i + 1) % self.log_step == 0:
et = datetime.now() - start_time
et = str(et)[:-7]
log = "Elapsed [{}], Iteration [{}/{}]".format(
et, i + 1, self.num_iters)
for tag, value in loss.items():
log += ", {}: {:.4f}".format(tag, value)
print(log)
if self.use_tensorboard:
for tag, value in loss.items():
self.logger.scalar_summary(tag, value, i + 1)
# Translate fixed images for debugging.
if (i + 1) % self.sample_step == 0:
with torch.no_grad():
d, speaker = TestSet(self.test_dir).test_data()
target = random.choice(
[x for x in speakers if x != speaker])
label_t = self.spk_enc.transform([target])[0]
label_t = np.asarray([label_t])
for filename, content in d.items():
f0 = content['f0']
ap = content['ap']
sp_norm_pad = self.pad_coded_sp(
content['coded_sp_norm'])
convert_result = []
for start_idx in range(
0, sp_norm_pad.shape[1] - FRAMES + 1, FRAMES):
one_seg = sp_norm_pad[:,
start_idx:start_idx + FRAMES]
one_seg = torch.FloatTensor(one_seg).to(
self.device)
one_seg = one_seg.view(1, 1, one_seg.size(0),
one_seg.size(1))
l = torch.FloatTensor(label_t)
one_seg = one_seg.to(self.device)
l = l.to(self.device)
one_set_return = self.G(one_seg,
l).data.cpu().numpy()
one_set_return = np.squeeze(one_set_return)
one_set_return = norm.backward_process(
one_set_return, target)
convert_result.append(one_set_return)
convert_con = np.concatenate(convert_result, axis=1)
convert_con = convert_con[:,
0:content['coded_sp_norm'].
shape[1]]
contigu = np.ascontiguousarray(convert_con.T,
dtype=np.float64)
decoded_sp = decode_spectral_envelope(contigu,
SAMPLE_RATE,
fft_size=FFTSIZE)
f0_converted = norm.pitch_conversion(
f0, speaker, target)
wav = synthesize(f0_converted, decoded_sp, ap,
SAMPLE_RATE)
name = f'{speaker}-{target}_iter{i+1}_{filename}'
path = os.path.join(self.sample_dir, name)
print(f'[save]:{path}')
librosa.output.write_wav(path, wav, SAMPLE_RATE)
# Save model checkpoints.
if (i + 1) % self.model_save_step == 0:
G_path = os.path.join(self.model_save_dir,
'{}-G.ckpt'.format(i + 1))
D_path = os.path.join(self.model_save_dir,
'{}-D.ckpt'.format(i + 1))
C_path = os.path.join(self.model_save_dir,
'{}-C.ckpt'.format(i + 1))
torch.save(self.G.state_dict(), G_path)
torch.save(self.D.state_dict(), D_path)
torch.save(self.C.state_dict(), C_path)
print('Saved model checkpoints into {}...'.format(
self.model_save_dir))
# Decay learning rates.
if (i + 1) % self.lr_update_step == 0 and (i + 1) > (
self.num_iters - self.num_iters_decay):
g_lr -= (self.g_lr / float(self.num_iters_decay))
d_lr -= (self.d_lr / float(self.num_iters_decay))
c_lr -= (self.c_lr / float(self.num_iters_decay))
self.update_lr(g_lr, d_lr, c_lr)
print('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(
g_lr, d_lr))
# output_dir = './data/processed'
#
# parser.add_argument('--input_dir', type = str, help = 'the direcotry contains data need to be processed', default = input_dir)
# parser.add_argument('--output_dir', type = str, help = 'the directory stores the processed data', default = output_dir)
#
# argv = parser.parse_args()
# input_dir = argv.input_dir
# output_dir = argv.output_dir
#
# os.makedirs(output_dir, exist_ok=True)
#
# wav_to_mcep_file(input_dir, SAMPLE_RATE, processed_filepath=output_dir)
#
# #input_dir is train dataset. we need to calculate and save the speech\
# # statistical characteristics for each speaker.
# generator = GenerateStatistics(output_dir)
# generator.generate_stats()
# generator.normalize_dataset()
# end = datetime.now()
# print(f"[Runing Time]: {end-start}")
data_dir = '../data/audio/'
sample = data_dir + 'Ses01F_impro01_F000.wav'
wav = librosa.load(one_file, sr=sr, mono=True, dtype=np.float64)[0]
f0, ap, sp, coded_sp = call_mcep(wav)
decoded_sp = decode_spectral_envelope(coded_sp, SAMPLE_RATE, fft_size=FFTSIZE)
# f0_converted = norm.pitch_conversion(f0, speaker, target)
wav2 = synthesize(f0_converted, decoded_sp, ap, SAMPLE_RATE)
audio_utils.save(wav2, './')
out_name = key.replace("/", "_")
else:
out_name = key
out_wavfile = tspk_dir / f"{out_name}.wav"
out_wavfile.parent.mkdir(exist_ok=True, parents=True)
# test generator
mcep_T = np.asarray(mcep.T, dtype=np.float32)
mcep_T = mcep_T.reshape((1, *mcep_T.shape))
gen_mcep_var = generator(mcep_T, tspk_lab)
gen_mcep = gen_mcep_var[0].data.T
denorm_gen_mcep = denorm_mcep(gen_mcep, f0, mcep_mean[tspk], mcep_std[tspk])
denorm_gen_mcep = signal.medfilt(denorm_gen_mcep, (5, 1))
conved_f0 = conv_f0(f0, logf0_mean[tspk], logf0_std[tspk])
specenv = pw.decode_spectral_envelope(denorm_gen_mcep, args.samplerate, args.fftsize)
x = pw.synthesize(conved_f0, specenv, ap, args.samplerate, frame_period=args.frame_period*1000)
x = x / max(abs(x)) * 30000
x = x.astype(np.int16)
wavfile.write(out_wavfile, args.samplerate, x)
# test discriminator
# test real data
if fspk not in real_flags:
real_datas[key] = np.squeeze(adverserial_discriminator(mcep_T, fspk_lab, dp_ratio=0.0)[1].data)
# test fake data
fake_datas[key] = np.squeeze(adverserial_discriminator(gen_mcep_var, tspk_lab, dp_ratio=0.0)[1].data)
# save values of discriminator of fake data (fspk -> tspk)
plt.clf()
import pyworld IN_WAVE_FILE = "in.wav" # 入力音声 OUT_WAVE_FILE = "out.wav" # 分析再合成した音声 SP_DIM = 50 # スペクトル包絡の圧縮後の次元 # 音声の読み込み fs, x = wavfile.read(IN_WAVE_FILE) x = x.astype(np.float64) # 音声の分析 (基本周波数、スペクトル包絡、非周期性指標) f0, sp, ap = pyworld.wav2world(x, fs) fft_size = pyworld.get_cheaptrick_fft_size(fs) # スペクトル包絡をエンコード / デコード # https://www.isca-speech.org/archive/Interspeech_2017/abstracts/0067.html code_sp = pyworld.code_spectral_envelope(sp, fs, SP_DIM) decode_sp = pyworld.decode_spectral_envelope(code_sp, fs, fft_size) # 非周期性指標をエンコード / デコード code_ap = pyworld.code_aperiodicity(ap, fs) decode_ap = pyworld.decode_aperiodicity(code_ap, fs, fft_size) # 音声の再合成 y = pyworld.synthesize(f0, decode_sp, decode_ap, fs) y = y.astype(np.int16) # 音声の書き込み wavfile.write(OUT_WAVE_FILE, fs, y)
def inference(self, dataset, rank_size=1):
if dataset is None:
print("convert dataset error!")
return
for i, samples in enumerate(dataset):
samples = self.model.prepare_samples(samples)
src_coded_sp, src_speaker_onehot, src_f0, src_ap = samples["src_coded_sp"], \
samples["src_speaker"], samples["src_f0"], samples["src_ap"]
tar_speaker_onehot = samples["tar_speaker"]
# Map the ids to the speakers name
src_id, tar_id = samples["src_id"], samples["tar_id"]
src_id, tar_id = int(src_id), int(tar_id)
src_speaker = self.speakers_ids_dict[src_id]
tar_speaker = self.speakers_ids_dict[tar_id]
src_wav_filename = samples["src_wav_filename"]
src_filename = src_wav_filename.numpy()[0].decode().replace(
".npz", "")
gen_coded_sp = self.model.convert(src_coded_sp, tar_speaker_onehot)
gen_coded_sp = tf.transpose(tf.squeeze(gen_coded_sp), [1, 0])
coded_sp = self.feature_normalizer(gen_coded_sp,
str(tar_speaker),
reverse=True)
def apply_f0_cmvn(cmvn_dict, feat_data, src_speaker, tar_speaker):
if tar_speaker not in cmvn_dict:
print("tar_speaker not in cmvn_dict!")
return feat_data
f0 = feat_data.numpy()
src_mean = cmvn_dict[src_speaker][2]
src_var = cmvn_dict[src_speaker][3]
tar_mean = cmvn_dict[tar_speaker][2]
tar_var = cmvn_dict[tar_speaker][3]
f0_converted = np.exp((np.ma.log(f0) - src_mean) /
np.sqrt(src_var) * np.sqrt(tar_var) +
tar_mean)
return f0_converted
f0 = apply_f0_cmvn(self.feature_normalizer.cmvn_dict, src_f0,
str(src_speaker), str(tar_speaker))
# Restoration of sp characteristics
c = []
for one_slice in coded_sp:
one_slice = np.ascontiguousarray(one_slice,
dtype=np.float64).reshape(
1, -1)
decoded_sp = pyworld.decode_spectral_envelope(
one_slice, self.fs, fft_size=self.fft_size)
c.append(decoded_sp)
sp = np.concatenate((c), axis=0)
f0 = np.squeeze(f0, axis=(0, )).astype(np.float64)
src_ap = np.squeeze(src_ap.numpy(), axis=(0, )).astype(np.float64)
# Remove the extra padding at the end of the sp feature
sp = sp[:src_ap.shape[0], :]
# sp: T,fft_size//2+1 f0: T ap: T,fft_size//2+1
synwav = pyworld.synthesize(f0, sp, src_ap, self.fs)
wavname = src_speaker + "_" + tar_speaker + "_" + src_filename + ".wav"
wavfolder = os.path.join(self.hparams.output_directory)
if not os.path.exists(wavfolder):
os.makedirs(wavfolder)
wavpath = os.path.join(wavfolder, wavname)
librosa.output.write_wav(wavpath, synwav, sr=self.fs)
print("generate wav:", wavpath)
else:
sentences = args.sentences
print(f"sentences: {sentences}")
for s, snt in enumerate(sentences):
feature, gen_letter_stateseq = feat_generator.generate(snt)
mcep = mcep_generator.generate(feature, args.target_speaker)
ap = ap_generator.generate(gen_letter_stateseq)
f0 = f0_generator.generate(gen_letter_stateseq)
f0[f0 < 0] = 0
mcep = denorm_mcep(mcep, mcep_min, mcep_max)
mcep = signal.medfilt(mcep, (5, 1))
mcep = mcep.astype(float, order="C")
decoded_sp = pw.decode_spectral_envelope(mcep, args.samplerate,
args.fftsize)
synthesized = pw.synthesize(f0,
decoded_sp,
ap,
args.samplerate,
frame_period=args.frame_period * 1000)
synthesized = synthesized / max(abs(synthesized)) * 30000
args.output_prefix.parent.mkdir(parents=True, exist_ok=True)
out_file = args.output_prefix.with_name(
f"{args.output_prefix.name}_{s:02d}_({'_'.join(map(str, snt))}).wav")
wavfile.write(out_file, args.samplerate, synthesized.astype(np.int16))
