mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-23 08:26:06 +00:00
Add back original so-vits-svc
This commit is contained in:
Brandon Thomas
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import os
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import shutil
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class AttrDict(dict):
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def __init__(self, *args, **kwargs):
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super(AttrDict, self).__init__(*args, **kwargs)
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self.__dict__ = self
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def build_env(config, config_name, path):
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t_path = os.path.join(path, config_name)
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if config != t_path:
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os.makedirs(path, exist_ok=True)
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shutil.copyfile(config, os.path.join(path, config_name))
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@@ -0,0 +1,503 @@
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import os
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import json
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from .env import AttrDict
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from .utils import init_weights, get_padding
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LRELU_SLOPE = 0.1
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def load_model(model_path, device='cuda'):
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config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
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with open(config_file) as f:
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data = f.read()
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global h
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json_config = json.loads(data)
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h = AttrDict(json_config)
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generator = Generator(h).to(device)
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cp_dict = torch.load(model_path)
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generator.load_state_dict(cp_dict['generator'])
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generator.eval()
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generator.remove_weight_norm()
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del cp_dict
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return generator, h
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class ResBlock1(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
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super(ResBlock1, self).__init__()
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self.h = h
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self.convs1 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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])
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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])
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self.convs2.apply(init_weights)
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def forward(self, x):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class ResBlock2(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
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super(ResBlock2, self).__init__()
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self.h = h
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self.convs = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1])))
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])
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self.convs.apply(init_weights)
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def forward(self, x):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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def padDiff(x):
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return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
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class SineGen(torch.nn.Module):
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""" Definition of sine generator
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SineGen(samp_rate, harmonic_num = 0,
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sine_amp = 0.1, noise_std = 0.003,
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voiced_threshold = 0,
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flag_for_pulse=False)
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samp_rate: sampling rate in Hz
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harmonic_num: number of harmonic overtones (default 0)
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sine_amp: amplitude of sine-wavefrom (default 0.1)
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noise_std: std of Gaussian noise (default 0.003)
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voiced_thoreshold: F0 threshold for U/V classification (default 0)
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flag_for_pulse: this SinGen is used inside PulseGen (default False)
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Note: when flag_for_pulse is True, the first time step of a voiced
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segment is always sin(np.pi) or cos(0)
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"""
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def __init__(self, samp_rate, harmonic_num=0,
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sine_amp=0.1, noise_std=0.003,
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voiced_threshold=0,
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flag_for_pulse=False):
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super(SineGen, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = noise_std
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self.harmonic_num = harmonic_num
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self.dim = self.harmonic_num + 1
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self.sampling_rate = samp_rate
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self.voiced_threshold = voiced_threshold
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self.flag_for_pulse = flag_for_pulse
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def _f02uv(self, f0):
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# generate uv signal
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uv = (f0 > self.voiced_threshold).type(torch.float32)
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return uv
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def _f02sine(self, f0_values):
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""" f0_values: (batchsize, length, dim)
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where dim indicates fundamental tone and overtones
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"""
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# convert to F0 in rad. The interger part n can be ignored
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# because 2 * np.pi * n doesn't affect phase
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rad_values = (f0_values / self.sampling_rate) % 1
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# initial phase noise (no noise for fundamental component)
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rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
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device=f0_values.device)
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rand_ini[:, 0] = 0
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rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
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# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
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if not self.flag_for_pulse:
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# for normal case
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# To prevent torch.cumsum numerical overflow,
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# it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
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# Buffer tmp_over_one_idx indicates the time step to add -1.
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# This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
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tmp_over_one = torch.cumsum(rad_values, 1) % 1
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tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
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cumsum_shift = torch.zeros_like(rad_values)
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cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
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sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
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* 2 * np.pi)
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else:
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# If necessary, make sure that the first time step of every
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# voiced segments is sin(pi) or cos(0)
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# This is used for pulse-train generation
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# identify the last time step in unvoiced segments
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uv = self._f02uv(f0_values)
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uv_1 = torch.roll(uv, shifts=-1, dims=1)
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uv_1[:, -1, :] = 1
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u_loc = (uv < 1) * (uv_1 > 0)
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# get the instantanouse phase
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tmp_cumsum = torch.cumsum(rad_values, dim=1)
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# different batch needs to be processed differently
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for idx in range(f0_values.shape[0]):
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temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
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temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
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# stores the accumulation of i.phase within
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# each voiced segments
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tmp_cumsum[idx, :, :] = 0
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tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
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# rad_values - tmp_cumsum: remove the accumulation of i.phase
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# within the previous voiced segment.
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i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
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# get the sines
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sines = torch.cos(i_phase * 2 * np.pi)
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return sines
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def forward(self, f0):
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""" sine_tensor, uv = forward(f0)
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input F0: tensor(batchsize=1, length, dim=1)
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f0 for unvoiced steps should be 0
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output sine_tensor: tensor(batchsize=1, length, dim)
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output uv: tensor(batchsize=1, length, 1)
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"""
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with torch.no_grad():
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f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
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device=f0.device)
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# fundamental component
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fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
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# generate sine waveforms
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sine_waves = self._f02sine(fn) * self.sine_amp
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# generate uv signal
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# uv = torch.ones(f0.shape)
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# uv = uv * (f0 > self.voiced_threshold)
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uv = self._f02uv(f0)
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# noise: for unvoiced should be similar to sine_amp
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# std = self.sine_amp/3 -> max value ~ self.sine_amp
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# . for voiced regions is self.noise_std
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noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
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noise = noise_amp * torch.randn_like(sine_waves)
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# first: set the unvoiced part to 0 by uv
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# then: additive noise
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sine_waves = sine_waves * uv + noise
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return sine_waves, uv, noise
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class SourceModuleHnNSF(torch.nn.Module):
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""" SourceModule for hn-nsf
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SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
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add_noise_std=0.003, voiced_threshod=0)
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sampling_rate: sampling_rate in Hz
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harmonic_num: number of harmonic above F0 (default: 0)
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sine_amp: amplitude of sine source signal (default: 0.1)
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add_noise_std: std of additive Gaussian noise (default: 0.003)
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note that amplitude of noise in unvoiced is decided
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by sine_amp
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voiced_threshold: threhold to set U/V given F0 (default: 0)
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Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
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F0_sampled (batchsize, length, 1)
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Sine_source (batchsize, length, 1)
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noise_source (batchsize, length 1)
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uv (batchsize, length, 1)
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"""
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def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
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add_noise_std=0.003, voiced_threshod=0):
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super(SourceModuleHnNSF, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = add_noise_std
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# to produce sine waveforms
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self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
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sine_amp, add_noise_std, voiced_threshod)
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# to merge source harmonics into a single excitation
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self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
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self.l_tanh = torch.nn.Tanh()
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def forward(self, x):
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"""
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Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
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F0_sampled (batchsize, length, 1)
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Sine_source (batchsize, length, 1)
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noise_source (batchsize, length 1)
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"""
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# source for harmonic branch
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sine_wavs, uv, _ = self.l_sin_gen(x)
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sine_merge = self.l_tanh(self.l_linear(sine_wavs))
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# source for noise branch, in the same shape as uv
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noise = torch.randn_like(uv) * self.sine_amp / 3
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return sine_merge, noise, uv
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class Generator(torch.nn.Module):
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def __init__(self, h):
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super(Generator, self).__init__()
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self.h = h
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self.num_kernels = len(h["resblock_kernel_sizes"])
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self.num_upsamples = len(h["upsample_rates"])
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self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
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self.m_source = SourceModuleHnNSF(
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sampling_rate=h["sampling_rate"],
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harmonic_num=8)
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self.noise_convs = nn.ModuleList()
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self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
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resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
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c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
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self.ups.append(weight_norm(
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ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
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k, u, padding=(k - u) // 2)))
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if i + 1 < len(h["upsample_rates"]): #
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stride_f0 = np.prod(h["upsample_rates"][i + 1:])
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self.noise_convs.append(Conv1d(
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1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
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else:
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self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = h["upsample_initial_channel"] // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
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self.resblocks.append(resblock(h, ch, k, d))
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self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
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self.ups.apply(init_weights)
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self.conv_post.apply(init_weights)
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self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
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def forward(self, x, f0, g=None):
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# print(1,x.shape,f0.shape,f0[:, None].shape)
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f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
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# print(2,f0.shape)
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har_source, noi_source, uv = self.m_source(f0)
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har_source = har_source.transpose(1, 2)
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x = self.conv_pre(x)
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x = x + self.cond(g)
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# print(124,x.shape,har_source.shape)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, LRELU_SLOPE)
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# print(3,x.shape)
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x = self.ups[i](x)
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x_source = self.noise_convs[i](har_source)
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# print(4,x_source.shape,har_source.shape,x.shape)
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x = x + x_source
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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x = F.leaky_relu(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print('Removing weight norm...')
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for l in self.ups:
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remove_weight_norm(l)
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for l in self.resblocks:
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l.remove_weight_norm()
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remove_weight_norm(self.conv_pre)
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remove_weight_norm(self.conv_post)
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class DiscriminatorP(torch.nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
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])
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, periods=None):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
|
||||
self.discriminators = nn.ModuleList()
|
||||
for period in self.periods:
|
||||
self.discriminators.append(DiscriminatorP(period))
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
|
||||
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiScaleDiscriminator(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiScaleDiscriminator, self).__init__()
|
||||
self.discriminators = nn.ModuleList([
|
||||
DiscriminatorS(use_spectral_norm=True),
|
||||
DiscriminatorS(),
|
||||
DiscriminatorS(),
|
||||
])
|
||||
self.meanpools = nn.ModuleList([
|
||||
AvgPool1d(4, 2, padding=2),
|
||||
AvgPool1d(4, 2, padding=2)
|
||||
])
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
if i != 0:
|
||||
y = self.meanpools[i - 1](y)
|
||||
y_hat = self.meanpools[i - 1](y_hat)
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((1 - dr) ** 2)
|
||||
g_loss = torch.mean(dg ** 2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
l = torch.mean((1 - dg) ** 2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
@@ -0,0 +1,111 @@
|
||||
import math
|
||||
import os
|
||||
os.environ["LRU_CACHE_CAPACITY"] = "3"
|
||||
import random
|
||||
import torch
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
from librosa.util import normalize
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
from scipy.io.wavfile import read
|
||||
import soundfile as sf
|
||||
|
||||
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
|
||||
sampling_rate = None
|
||||
try:
|
||||
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
|
||||
except Exception as ex:
|
||||
print(f"'{full_path}' failed to load.\nException:")
|
||||
print(ex)
|
||||
if return_empty_on_exception:
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
else:
|
||||
raise Exception(ex)
|
||||
|
||||
if len(data.shape) > 1:
|
||||
data = data[:, 0]
|
||||
assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
|
||||
|
||||
if np.issubdtype(data.dtype, np.integer): # if audio data is type int
|
||||
max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
|
||||
else: # if audio data is type fp32
|
||||
max_mag = max(np.amax(data), -np.amin(data))
|
||||
max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
|
||||
|
||||
data = torch.FloatTensor(data.astype(np.float32))/max_mag
|
||||
|
||||
if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
if target_sr is not None and sampling_rate != target_sr:
|
||||
data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
|
||||
sampling_rate = target_sr
|
||||
|
||||
return data, sampling_rate
|
||||
|
||||
def dynamic_range_compression(x, C=1, clip_val=1e-5):
|
||||
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
|
||||
|
||||
def dynamic_range_decompression(x, C=1):
|
||||
return np.exp(x) / C
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
return torch.exp(x) / C
|
||||
|
||||
class STFT():
|
||||
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
|
||||
self.target_sr = sr
|
||||
|
||||
self.n_mels = n_mels
|
||||
self.n_fft = n_fft
|
||||
self.win_size = win_size
|
||||
self.hop_length = hop_length
|
||||
self.fmin = fmin
|
||||
self.fmax = fmax
|
||||
self.clip_val = clip_val
|
||||
self.mel_basis = {}
|
||||
self.hann_window = {}
|
||||
|
||||
def get_mel(self, y, center=False):
|
||||
sampling_rate = self.target_sr
|
||||
n_mels = self.n_mels
|
||||
n_fft = self.n_fft
|
||||
win_size = self.win_size
|
||||
hop_length = self.hop_length
|
||||
fmin = self.fmin
|
||||
fmax = self.fmax
|
||||
clip_val = self.clip_val
|
||||
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
if fmax not in self.mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
|
||||
self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
||||
# print(111,spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
|
||||
# print(222,spec)
|
||||
spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
|
||||
# print(333,spec)
|
||||
spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
|
||||
# print(444,spec)
|
||||
return spec
|
||||
|
||||
def __call__(self, audiopath):
|
||||
audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
|
||||
spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
|
||||
return spect
|
||||
|
||||
stft = STFT()
|
||||
@@ -0,0 +1,68 @@
|
||||
import glob
|
||||
import os
|
||||
import matplotlib
|
||||
import torch
|
||||
from torch.nn.utils import weight_norm
|
||||
# matplotlib.use("Agg")
|
||||
import matplotlib.pylab as plt
|
||||
|
||||
|
||||
def plot_spectrogram(spectrogram):
|
||||
fig, ax = plt.subplots(figsize=(10, 2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
fig.canvas.draw()
|
||||
plt.close()
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def apply_weight_norm(m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
weight_norm(m)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def load_checkpoint(filepath, device):
|
||||
assert os.path.isfile(filepath)
|
||||
print("Loading '{}'".format(filepath))
|
||||
checkpoint_dict = torch.load(filepath, map_location=device)
|
||||
print("Complete.")
|
||||
return checkpoint_dict
|
||||
|
||||
|
||||
def save_checkpoint(filepath, obj):
|
||||
print("Saving checkpoint to {}".format(filepath))
|
||||
torch.save(obj, filepath)
|
||||
print("Complete.")
|
||||
|
||||
|
||||
def del_old_checkpoints(cp_dir, prefix, n_models=2):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern) # get checkpoint paths
|
||||
cp_list = sorted(cp_list)# sort by iter
|
||||
if len(cp_list) > n_models: # if more than n_models models are found
|
||||
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
|
||||
open(cp, 'w').close()# empty file contents
|
||||
os.unlink(cp)# delete file (move to trash when using Colab)
|
||||
|
||||
|
||||
def scan_checkpoint(cp_dir, prefix):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern)
|
||||
if len(cp_list) == 0:
|
||||
return None
|
||||
return sorted(cp_list)[-1]
|
||||
|
||||
Reference in New Issue
Block a user