mirror of
https://github.com/storytold/torchscript-models.git
synced 2026-10-19 07:42:59 +00:00
Brandon Thomas
236 lines
6.8 KiB
Python
236 lines
6.8 KiB
Python
from typing import List
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import math
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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from librosa.filters import mel as librosa_mel_fn
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from audio_processing import dynamic_range_compression
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from audio_processing import dynamic_range_decompression
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from stft import STFT
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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# NB(bt): type hint is because of torchjit
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def convert_pad_shape(pad_shape: List[List[int]]):
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l = pad_shape[::-1]
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# NB(bt): torch jit
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#pad_shape = [item for sublist in l for item in sublist]
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pad_shape = l[0].copy()
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for sublist in l[1:]:
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pad_shape.extend(sublist)
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#pad_shape = l.flatten()
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return pad_shape
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def shift_1d(x):
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x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
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return x
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def sequence_mask(length, max_length=None):
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if max_length is None:
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max_length = length.max()
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x = torch.arange(max_length, dtype=length.dtype, device=length.device)
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return x.unsqueeze(0) < length.unsqueeze(1)
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def maximum_path(value, mask, max_neg_val=-np.inf):
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""" Numpy-friendly version. It's about 4 times faster than torch version.
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value: [b, t_x, t_y]
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mask: [b, t_x, t_y]
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"""
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value = value * mask
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device = value.device
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dtype = value.dtype
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value = value.cpu().detach().numpy()
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mask = mask.cpu().detach().numpy().astype(np.bool)
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b, t_x, t_y = value.shape
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direction = np.zeros(value.shape, dtype=np.int64)
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v = np.zeros((b, t_x), dtype=np.float32)
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x_range = np.arange(t_x, dtype=np.float32).reshape(1,-1)
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for j in range(t_y):
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v0 = np.pad(v, [[0,0],[1,0]], mode="constant", constant_values=max_neg_val)[:, :-1]
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v1 = v
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max_mask = (v1 >= v0)
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v_max = np.where(max_mask, v1, v0)
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direction[:, :, j] = max_mask
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index_mask = (x_range <= j)
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v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
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direction = np.where(mask, direction, 1)
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path = np.zeros(value.shape, dtype=np.float32)
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index = mask[:, :, 0].sum(1).astype(np.int64) - 1
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index_range = np.arange(b)
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for j in reversed(range(t_y)):
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path[index_range, index, j] = 1
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index = index + direction[index_range, index, j] - 1
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path = path * mask.astype(np.float32)
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path = torch.from_numpy(path).to(device=device, dtype=dtype)
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return path
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def generate_path(duration, mask):
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"""
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duration: [b, t_x]
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mask: [b, t_x, t_y]
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"""
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device = duration.device
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b, t_x, t_y = mask.shape
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cum_duration = torch.cumsum(duration, 1)
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path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device)
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cum_duration_flat = cum_duration.view(b * t_x)
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path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
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path = path.view(b, t_x, t_y)
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path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:,:-1]
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path = path * mask
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return path
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class Adam():
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def __init__(self, params, scheduler, dim_model, warmup_steps=4000, lr=1e0, betas=(0.9, 0.98), eps=1e-9):
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self.params = params
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self.scheduler = scheduler
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self.dim_model = dim_model
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self.warmup_steps = warmup_steps
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self.lr = lr
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self.betas = betas
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self.eps = eps
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self.step_num = 1
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self.cur_lr = lr * self._get_lr_scale()
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self._optim = torch.optim.Adam(params, lr=self.cur_lr, betas=betas, eps=eps)
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def _get_lr_scale(self):
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if self.scheduler == "noam":
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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)])
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else:
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return 1
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def _update_learning_rate(self):
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self.step_num += 1
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if self.scheduler == "noam":
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self.cur_lr = self.lr * self._get_lr_scale()
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for param_group in self._optim.param_groups:
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param_group['lr'] = self.cur_lr
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def get_lr(self):
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return self.cur_lr
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def step(self):
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self._optim.step()
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self._update_learning_rate()
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def zero_grad(self):
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self._optim.zero_grad()
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def load_state_dict(self, d):
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self._optim.load_state_dict(d)
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def state_dict(self):
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return self._optim.state_dict()
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class TacotronSTFT(nn.Module):
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def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
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n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
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mel_fmax=8000.0):
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super(TacotronSTFT, self).__init__()
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self.n_mel_channels = n_mel_channels
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self.sampling_rate = sampling_rate
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self.stft_fn = STFT(filter_length, hop_length, win_length)
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mel_basis = librosa_mel_fn(
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sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
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mel_basis = torch.from_numpy(mel_basis).float()
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self.register_buffer('mel_basis', mel_basis)
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def spectral_normalize(self, magnitudes):
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output = dynamic_range_compression(magnitudes)
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return output
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def spectral_de_normalize(self, magnitudes):
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output = dynamic_range_decompression(magnitudes)
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return output
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def mel_spectrogram(self, y):
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"""Computes mel-spectrograms from a batch of waves
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PARAMS
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------
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y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
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RETURNS
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-------
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mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
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"""
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assert(torch.min(y.data) >= -1)
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assert(torch.max(y.data) <= 1)
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magnitudes, phases = self.stft_fn.transform(y)
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magnitudes = magnitudes.data
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mel_output = torch.matmul(self.mel_basis, magnitudes)
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mel_output = self.spectral_normalize(mel_output)
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return mel_output
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def clip_grad_value_(parameters, clip_value, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = list(filter(lambda p: p.grad is not None, parameters))
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norm_type = float(norm_type)
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clip_value = float(clip_value)
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total_norm = 0
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for p in parameters:
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param_norm = p.grad.data.norm(norm_type)
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total_norm += param_norm.item() ** norm_type
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p.grad.data.clamp_(min=-clip_value, max=clip_value)
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total_norm = total_norm ** (1. / norm_type)
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return total_norm
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def squeeze(x, x_mask=None, n_sqz=2):
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b, c, t = x.size()
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t = (t // n_sqz) * n_sqz
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x = x[:,:,:t]
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x_sqz = x.view(b, c, t//n_sqz, n_sqz)
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x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c*n_sqz, t//n_sqz)
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if x_mask is not None:
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x_mask = x_mask[:,:,n_sqz-1::n_sqz]
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else:
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x_mask = torch.ones(b, 1, t//n_sqz).to(device=x.device, dtype=x.dtype)
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return x_sqz * x_mask, x_mask
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def unsqueeze(x, x_mask=None, n_sqz=2):
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b, c, t = x.size()
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x_unsqz = x.view(b, n_sqz, c//n_sqz, t)
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x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c//n_sqz, t*n_sqz)
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if x_mask is not None:
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x_mask = x_mask.unsqueeze(-1).repeat(1,1,1,n_sqz).view(b, 1, t*n_sqz)
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else:
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x_mask = torch.ones(b, 1, t*n_sqz).to(device=x.device, dtype=x.dtype)
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return x_unsqz * x_mask, x_mask
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