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utils.py
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utils.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
from scipy.io import wavfile
from vocoder.hifigan_generator import Generator
import hparams as hp
import os
import text
import json
from pydub import AudioSegment
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import matplotlib.font_manager as fm
# Speaker list 불러오기 (Add)
def get_speakers(synthesize=False):
# preprocessed의 alignment로 확인
path = 'preprocessed/' + hp.dataset + '/alignment'
if not os.path.exists(path):
n_speakers = 1
else:
file_list = os.listdir(path)
file_list.sort()
n_speakers = len(file_list)
speaker_table = {}
# 학습 시 Multi-speaker training의 경우 테이블 내용 json 저장
if n_speakers > 1:
cnt = 0
for file in file_list:
speaker_table[file] = cnt
cnt+=1
pre_speakers = {}
pre_speakers['n_speakers'] = n_speakers
pre_speakers['speaker_table'] = speaker_table
with open('speaker_info.json', 'w') as f:
json.dump(pre_speakers, f)
# 참고할 table이 있는지 확인 / 있으면 읽어오고 없으면 본인만 지정
else:
# Single-speaker / fine-tuning
if os.path.exists('speaker_info.json'):
with open('speaker_info.json', 'r') as f:
pre_speakers = json.load(f)
speaker_table = pre_speakers['speaker_table']
# Single-speaker / No fine-tuning
else:
speaker_table = {}
speaker_table[file_list[0]] = 0
return n_speakers, speaker_table
# Speaker Embedding 레이어 함수 (Add)
def Embedding(num_embeddings, embedding_dim, padding_idx, std=0.01):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
m.weight.data.normal_(0, std) # weight normalize
return m
# 스피커를 하나로 통합하는 모듈 (Add)
class SpeakerIntegrator(nn.Module):
def __init__(self):
super(SpeakerIntegrator, self).__init__()
def forward(self, x, spembs):
spembs = spembs.unsqueeze(1)
spembs = spembs.repeat(1, x.shape[1], 1)
x = x + spembs
return x
def get_alignment(tier):
sil_phones = ['sil', 'sp', 'spn']
phones = []
durations = []
start_time = 0
end_time = 0
end_idx = 0
for t in tier._objects:
s, e, p = t.start_time, t.end_time, t.text
# Trimming leading silences
if phones == []:
if p in sil_phones:
continue
else:
start_time = s
if p not in sil_phones:
phones.append(p)
end_time = e
end_idx = len(phones)
else:
phones.append(p)
durations.append(int(e*hp.sampling_rate/hp.hop_length)-int(s*hp.sampling_rate/hp.hop_length))
# Trimming tailing silences
phones = phones[:end_idx]
durations = durations[:end_idx]
return phones, np.array(durations), start_time, end_time
def process_meta(meta_path):
with open(meta_path, "r", encoding="utf-8") as f:
text = []
name = []
for line in f.readlines():
n, t = line.strip('\n').split('|')
name.append(n)
text.append(t)
return name, text
def get_param_num(model):
num_param = sum(param.numel() for param in model.parameters())
return num_param
def plot_data(data, sentence_list, titles=None, filename=None):
fonts = 'data/NanumGothic.ttf'
fontprop = fm.FontProperties(fname=fonts)
# total_mel_postnet_torch[0].detach().cpu().numpy()
fig, axes = plt.subplots(1, len(data[0][0]), squeeze=False)
if titles is None:
titles = [None for i in range(len(data))]
def add_axis(fig, old_ax, offset=0):
ax = fig.add_axes(old_ax.get_position(), anchor='W')
ax.set_facecolor("None")
return ax
plt.rcParams["figure.figsize"] = (10,4)
for i in range(len(data)):
spectrograms, pitchs, energies = data[i]
for j in range(len(spectrograms)):
spectrogram = spectrograms[j][0].detach().cpu().numpy() # Spectrogram은 통째로 받아서 사용할 때 0번째 numpy로 재정의
axes[0][j].imshow(spectrogram, origin='lower')
axes[0][j].set_aspect(2.5, adjustable='box')
axes[0][j].set_ylim(0, hp.n_mel_channels)
#axes[0][j].set_title(titles[0]+'_'+str(j), fontsize='medium')
axes[0][j].set_title(sentence_list[j], fontsize='medium', fontproperties=fontprop)
axes[0][j].tick_params(labelsize='x-small', left=False, labelleft=False)
axes[0][j].set_anchor('W')
ax1 = add_axis(fig, axes[0][j])
ax1.plot(pitchs[j], color='tomato')
ax1.set_xlim(0, spectrogram.shape[1])
ax1.set_ylim(0, hp.f0_max)
ax1.set_ylabel('F0', color='tomato')
ax1.tick_params(labelsize='x-small', colors='tomato', bottom=False, labelbottom=False)
ax2 = add_axis(fig, axes[0][j], 1.2)
ax2.plot(energies[j], color='darkviolet')
ax2.set_xlim(0, spectrogram.shape[1])
ax2.set_ylim(hp.energy_min, hp.energy_max)
ax2.set_ylabel('Energy', color='darkviolet')
ax2.yaxis.set_label_position('right')
ax2.tick_params(labelsize='x-small', colors='darkviolet', bottom=False, labelbottom=False, left=False, labelleft=False, right=True, labelright=True)
#curFilename = filename[:-4] + '_' + str(i) + filename[-4:]
plt.savefig(filename, dpi=200)
plt.close()
def get_mask_from_lengths(lengths, max_len=None):
batch_size = lengths.shape[0]
if max_len is None:
max_len = torch.max(lengths).item()
ids = torch.arange(0, max_len).unsqueeze(0).expand(batch_size, -1).to(device)
mask = (ids >= lengths.unsqueeze(1).expand(-1, max_len))
return mask
def get_vocgan(ckpt_path, n_mel_channels=hp.n_mel_channels, generator_ratio = [4, 4, 2, 2, 2, 2], n_residual_layers=4, mult=256, out_channels=1):
checkpoint = torch.load(ckpt_path, map_location=device)
model = Generator(n_mel_channels, n_residual_layers,
ratios=generator_ratio, mult=mult,
out_band=out_channels)
model.load_state_dict(checkpoint['model_g'])
model.to(device).eval()
return model
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
# HiFi-GAN 불러오기 (Add)
def get_hifigan(ckpt_path):
state_dict_g = load_checkpoint(ckpt_path, device)
model = Generator().to(device)
model.load_state_dict(state_dict_g['generator'], strict=False)
return model
# 문장 단위로 잘라 합성 (Add)
def combine_wav(path, cnt):
for i in range(cnt):
curPath = path[:-4] + '_' + str(i+1) + path[-4:]
if i == 0:
combined_sounds = AudioSegment.from_wav(curPath)
else:
combined_sounds += AudioSegment.from_wav(curPath)
os.remove(curPath)
combined_sounds.export(path, format="wav")
print(path, 'done')
# Synthesize 과정에서 HiFi-GAN을 통한 Mel to Waveform (Add)
def hifigan_infer(mel_list, path, synthesize=False):
if torch.cuda.is_available():
torch.cuda.manual_seed(1234)
device = torch.device('cuda')
else:
device = torch.device('cpu')
generator = Generator().to(device)
state_dict_g = load_checkpoint(hp.vocoder_pretrained_model_path, device)
generator.load_state_dict(state_dict_g['generator'], strict=False)
generator.eval()
generator.remove_weight_norm()
cnt = 0
for mel in mel_list:
cnt += 1
with torch.no_grad():
if not synthesize:
mel = torch.unsqueeze(mel, 0)
x = mel
y_g_hat = generator(x)
audio = y_g_hat.squeeze()
audio = audio * 32768.0 # MAX_WAV_VALUE
audio = audio.cpu().numpy().astype('int16')
curPath = path[:-4] + '_' + str(cnt) + path[-4:]
wavfile.write(curPath, hp.sampling_rate, audio)
print(curPath, 'done')
combine_wav(path, cnt)
def pad_1D(inputs, PAD=0):
def pad_data(x, length, PAD):
x_padded = np.pad(x, (0, length - x.shape[0]),
mode='constant',
constant_values=PAD)
return x_padded
max_len = max((len(x) for x in inputs))
padded = np.stack([pad_data(x, max_len, PAD) for x in inputs])
return padded
def pad_2D(inputs, maxlen=None):
def pad(x, max_len):
PAD = 0
if np.shape(x)[0] > max_len:
raise ValueError("not max_len")
s = np.shape(x)[1]
x_padded = np.pad(x, (0, max_len - np.shape(x)[0]),
mode='constant',
constant_values=PAD)
return x_padded[:, :s]
if maxlen:
output = np.stack([pad(x, maxlen) for x in inputs])
else:
max_len = max(np.shape(x)[0] for x in inputs)
output = np.stack([pad(x, max_len) for x in inputs])
return output
def pad(input_ele, mel_max_length=None):
if mel_max_length:
max_len = mel_max_length
else:
max_len = max([input_ele[i].size(0)for i in range(len(input_ele))])
out_list = list()
for i, batch in enumerate(input_ele):
if len(batch.shape) == 1:
one_batch_padded = F.pad(
batch, (0, max_len-batch.size(0)), "constant", 0.0)
elif len(batch.shape) == 2:
one_batch_padded = F.pad(
batch, (0, 0, 0, max_len-batch.size(0)), "constant", 0.0)
out_list.append(one_batch_padded)
out_padded = torch.stack(out_list)
return out_padded
# from dathudeptrai's FastSpeech2 implementation
def standard_norm(x, mean, std, is_mel=False):
if not is_mel:
x = remove_outlier(x)
zero_idxs = np.where(x == 0.0)[0]
x = (x - mean) / std
x[zero_idxs] = 0.0
return x
return (x - mean) / std
def de_norm(x, mean, std):
zero_idxs = torch.where(x == 0.0)[0]
x = mean + std * x
x[zero_idxs] = 0.0
return x
def _is_outlier(x, p25, p75):
"""Check if value is an outlier."""
lower = p25 - 1.5 * (p75 - p25)
upper = p75 + 1.5 * (p75 - p25)
return np.logical_or(x <= lower, x >= upper)
# 길이가 짧은 경우 outlier로 판단하여 사용 X
def remove_outlier(x):
"""Remove outlier from x."""
p25 = np.percentile(x, 25)
p75 = np.percentile(x, 75)
indices_of_outliers = []
for ind, value in enumerate(x):
if _is_outlier(value, p25, p75):
indices_of_outliers.append(ind)
x[indices_of_outliers] = 0.0
# replace by mean f0.
x[indices_of_outliers] = np.max(x)
return x
def average_by_duration(x, durs):
mel_len = durs.sum()
durs_cum = np.cumsum(np.pad(durs, (1, 0)))
# calculate charactor f0/energy
x_char = np.zeros((durs.shape[0],), dtype=np.float32)
for idx, start, end in zip(range(mel_len), durs_cum[:-1], durs_cum[1:]):
values = x[start:end][np.where(x[start:end] != 0.0)[0]]
x_char[idx] = np.mean(values) if len(values) > 0 else 0.0 # np.mean([]) = nan.
return x_char.astype(np.float32)
## Only HiFi-GAN fuction
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 get_padding(kernel_size, dilation=1):
return int((kernel_size*dilation - dilation)/2)