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import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from librosa.filters import mel as librosa_mel_fn
from audio_processing import dynamic_range_compression
from audio_processing import dynamic_range_decompression
from stft import STFT
def intersperse(lst, item):
result = [item] * (len(lst) * 2 + 1)
result[1::2] = lst
return result
def mle_loss(z, m, logs, logdet, mask):
l = torch.sum(logs) + 0.5 * torch.sum(
torch.exp(-2 * logs) * ((z - m) ** 2)
) # neg normal likelihood w/o the constant term
l = l - torch.sum(logdet) # log jacobian determinant
l = l / torch.sum(
torch.ones_like(z) * mask
) # averaging across batch, channel and time axes
l = l + 0.5 * math.log(2 * math.pi) # add the remaining constant term
return l
def duration_loss(logw, logw_, lengths):
l = torch.sum((logw - logw_) ** 2) / torch.sum(lengths)
return l
@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pad_shape = [item for sublist in l for item in sublist]
return pad_shape
def shift_1d(x):
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
return x
def sequence_mask(length, max_length=None):
if max_length is None:
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1)
def maximum_path(value, mask, max_neg_val=-np.inf):
"""Numpy-friendly version. It's about 4 times faster than torch version.
value: [b, t_x, t_y]
mask: [b, t_x, t_y]
"""
value = value * mask
device = value.device
dtype = value.dtype
value = value.cpu().detach().numpy()
mask = mask.cpu().detach().numpy().astype(np.bool)
b, t_x, t_y = value.shape
direction = np.zeros(value.shape, dtype=np.int64)
v = np.zeros((b, t_x), dtype=np.float32)
x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)
for j in range(t_y):
v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[
:, :-1
]
v1 = v
max_mask = v1 >= v0
v_max = np.where(max_mask, v1, v0)
direction[:, :, j] = max_mask
index_mask = x_range <= j
v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
direction = np.where(mask, direction, 1)
path = np.zeros(value.shape, dtype=np.float32)
index = mask[:, :, 0].sum(1).astype(np.int64) - 1
index_range = np.arange(b)
for j in reversed(range(t_y)):
path[index_range, index, j] = 1
index = index + direction[index_range, index, j] - 1
path = path * mask.astype(np.float32)
path = torch.from_numpy(path).to(device=device, dtype=dtype)
return path
def generate_path(duration, mask):
"""
duration: [b, t_x]
mask: [b, t_x, t_y]
"""
device = duration.device
b, t_x, t_y = mask.shape
cum_duration = torch.cumsum(duration, 1)
path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device)
cum_duration_flat = cum_duration.view(b * t_x)
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
path = path.view(b, t_x, t_y)
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
path = path * mask
return path
class Adam:
def __init__(
self,
params,
scheduler,
dim_model,
warmup_steps=4000,
lr=1e0,
betas=(0.9, 0.98),
eps=1e-9,
):
self.params = params
self.scheduler = scheduler
self.dim_model = dim_model
self.warmup_steps = warmup_steps
self.lr = lr
self.betas = betas
self.eps = eps
self.step_num = 1
self.cur_lr = lr * self._get_lr_scale()
self._optim = torch.optim.Adam(params, lr=self.cur_lr, betas=betas, eps=eps)
def _get_lr_scale(self):
if self.scheduler == "noam":
return np.power(self.dim_model, -0.5) * np.min(
[
np.power(self.step_num, -0.5),
self.step_num * np.power(self.warmup_steps, -1.5),
]
)
else:
return 1
def _update_learning_rate(self):
self.step_num += 1
if self.scheduler == "noam":
self.cur_lr = self.lr * self._get_lr_scale()
for param_group in self._optim.param_groups:
param_group["lr"] = self.cur_lr
def get_lr(self):
return self.cur_lr
def step(self):
self._optim.step()
self._update_learning_rate()
def zero_grad(self):
self._optim.zero_grad()
def load_state_dict(self, d):
self._optim.load_state_dict(d)
def state_dict(self):
return self._optim.state_dict()
class TacotronSTFT(nn.Module):
def __init__(
self,
filter_length=1024,
hop_length=256,
win_length=1024,
n_mel_channels=80,
sampling_rate=22050,
mel_fmin=0.0,
mel_fmax=8000.0,
):
super(TacotronSTFT, self).__init__()
self.n_mel_channels = n_mel_channels
self.sampling_rate = sampling_rate
self.stft_fn = STFT(filter_length, hop_length, win_length)
mel_basis = librosa_mel_fn(
sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax
)
mel_basis = torch.from_numpy(mel_basis).float()
self.register_buffer("mel_basis", mel_basis)
def spectral_normalize(self, magnitudes):
output = dynamic_range_compression(magnitudes)
return output
def spectral_de_normalize(self, magnitudes):
output = dynamic_range_decompression(magnitudes)
return output
def mel_spectrogram(self, y):
"""Computes mel-spectrograms from a batch of waves
PARAMS
------
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
RETURNS
-------
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
"""
assert torch.min(y.data) >= -1
assert torch.max(y.data) <= 1
magnitudes, phases = self.stft_fn.transform(y)
magnitudes = magnitudes.data
mel_output = torch.matmul(self.mel_basis, magnitudes)
mel_output = self.spectral_normalize(mel_output)
return mel_output
def clip_grad_value_(parameters, clip_value, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
norm_type = float(norm_type)
clip_value = float(clip_value)
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
p.grad.data.clamp_(min=-clip_value, max=clip_value)
total_norm = total_norm ** (1.0 / norm_type)
return total_norm
def squeeze(x, x_mask=None, n_sqz=2):
b, c, t = x.size()
t = (t // n_sqz) * n_sqz
x = x[:, :, :t]
x_sqz = x.view(b, c, t // n_sqz, n_sqz)
x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
if x_mask is not None:
x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz]
else:
x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
return x_sqz * x_mask, x_mask
def unsqueeze(x, x_mask=None, n_sqz=2):
b, c, t = x.size()
x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
if x_mask is not None:
x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
else:
x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
return x_unsqz * x_mask, x_mask
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