import os
import random
import re
import numpy as np
import librosa
import torch
import random
from utils import repeat_expand_2d
from tqdm import tqdm
from torch.utils.data import Dataset
def traverse_dir(
root_dir,
extensions,
amount=None,
str_include=None,
str_exclude=None,
is_pure=False,
is_sort=False,
is_ext=True):
file_list = []
cnt = 0
for root, _, files in os.walk(root_dir):
for file in files:
if any([file.endswith(f".{ext}") for ext in extensions]):
# path
mix_path = os.path.join(root, file)
pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
# amount
if (amount is not None) and (cnt == amount):
if is_sort:
file_list.sort()
return file_list
# check string
if (str_include is not None) and (str_include not in pure_path):
continue
if (str_exclude is not None) and (str_exclude in pure_path):
continue
if not is_ext:
ext = pure_path.split('.')[-1]
pure_path = pure_path[:-(len(ext)+1)]
file_list.append(pure_path)
cnt += 1
if is_sort:
file_list.sort()
return file_list
def get_data_loaders(args, whole_audio=False):
data_train = AudioDataset(
filelists = args.data.training_files,
waveform_sec=args.data.duration,
hop_size=args.data.block_size,
sample_rate=args.data.sampling_rate,
load_all_data=args.train.cache_all_data,
whole_audio=whole_audio,
extensions=args.data.extensions,
n_spk=args.model.n_spk,
spk=args.spk,
device=args.train.cache_device,
fp16=args.train.cache_fp16,
unit_interpolate_mode = args.data.unit_interpolate_mode,
use_aug=True)
loader_train = torch.utils.data.DataLoader(
data_train ,
batch_size=args.train.batch_size if not whole_audio else 1,
shuffle=True,
num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
pin_memory=True if args.train.cache_device=='cpu' else False
)
data_valid = AudioDataset(
filelists = args.data.validation_files,
waveform_sec=args.data.duration,
hop_size=args.data.block_size,
sample_rate=args.data.sampling_rate,
load_all_data=args.train.cache_all_data,
whole_audio=True,
spk=args.spk,
extensions=args.data.extensions,
unit_interpolate_mode = args.data.unit_interpolate_mode,
n_spk=args.model.n_spk)
loader_valid = torch.utils.data.DataLoader(
data_valid,
batch_size=1,
shuffle=False,
num_workers=0,
pin_memory=True
)
return loader_train, loader_valid
class AudioDataset(Dataset):
def __init__(
self,
filelists,
waveform_sec,
hop_size,
sample_rate,
spk,
load_all_data=True,
whole_audio=False,
extensions=['wav'],
n_spk=1,
device='cpu',
fp16=False,
use_aug=False,
unit_interpolate_mode = 'left'
):
super().__init__()
self.waveform_sec = waveform_sec
self.sample_rate = sample_rate
self.hop_size = hop_size
self.filelists = filelists
self.whole_audio = whole_audio
self.use_aug = use_aug
self.data_buffer={}
self.pitch_aug_dict = {}
self.unit_interpolate_mode = unit_interpolate_mode
# np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
if load_all_data:
print('Load all the data filelists:', filelists)
else:
print('Load the f0, volume data filelists:', filelists)
with open(filelists,"r") as f:
self.paths = f.read().splitlines()
for name_ext in tqdm(self.paths, total=len(self.paths)):
name = os.path.splitext(name_ext)[0]
path_audio = name_ext
duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
path_f0 = name_ext + ".f0.npy"
f0,_ = np.load(path_f0,allow_pickle=True)
f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
path_volume = name_ext + ".vol.npy"
volume = np.load(path_volume)
volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
path_augvol = name_ext + ".aug_vol.npy"
aug_vol = np.load(path_augvol)
aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
if n_spk is not None and n_spk > 1:
spk_name = name_ext.split("/")[-2]
spk_id = spk[spk_name] if spk_name in spk else 0
if spk_id < 0 or spk_id >= n_spk:
raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
else:
spk_id = 0
spk_id = torch.LongTensor(np.array([spk_id])).to(device)
if load_all_data:
'''
audio, sr = librosa.load(path_audio, sr=self.sample_rate)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
audio = torch.from_numpy(audio).to(device)
'''
path_mel = name_ext + ".mel.npy"
mel = np.load(path_mel)
mel = torch.from_numpy(mel).to(device)
path_augmel = name_ext + ".aug_mel.npy"
aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
aug_mel = np.array(aug_mel,dtype=float)
aug_mel = torch.from_numpy(aug_mel).to(device)
self.pitch_aug_dict[name_ext] = keyshift
path_units = name_ext + ".soft.pt"
units = torch.load(path_units).to(device)
units = units[0]
units = repeat_expand_2d(units,f0.size(0),unit_interpolate_mode).transpose(0,1)
if fp16:
mel = mel.half()
aug_mel = aug_mel.half()
units = units.half()
self.data_buffer[name_ext] = {
'duration': duration,
'mel': mel,
'aug_mel': aug_mel,
'units': units,
'f0': f0,
'volume': volume,
'aug_vol': aug_vol,
'spk_id': spk_id
}
else:
path_augmel = name_ext + ".aug_mel.npy"
aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
self.pitch_aug_dict[name_ext] = keyshift
self.data_buffer[name_ext] = {
'duration': duration,
'f0': f0,
'volume': volume,
'aug_vol': aug_vol,
'spk_id': spk_id
}
def __getitem__(self, file_idx):
name_ext = self.paths[file_idx]
data_buffer = self.data_buffer[name_ext]
# check duration. if too short, then skip
if data_buffer['duration'] < (self.waveform_sec + 0.1):
return self.__getitem__( (file_idx + 1) % len(self.paths))
# get item
return self.get_data(name_ext, data_buffer)
def get_data(self, name_ext, data_buffer):
name = os.path.splitext(name_ext)[0]
frame_resolution = self.hop_size / self.sample_rate
duration = data_buffer['duration']
waveform_sec = duration if self.whole_audio else self.waveform_sec
# load audio
idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
start_frame = int(idx_from / frame_resolution)
units_frame_len = int(waveform_sec / frame_resolution)
aug_flag = random.choice([True, False]) and self.use_aug
'''
audio = data_buffer.get('audio')
if audio is None:
path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
audio, sr = librosa.load(
path_audio,
sr = self.sample_rate,
offset = start_frame * frame_resolution,
duration = waveform_sec)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio)
# clip audio into N seconds
audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
audio = torch.from_numpy(audio).float()
else:
audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
'''
# load mel
mel_key = 'aug_mel' if aug_flag else 'mel'
mel = data_buffer.get(mel_key)
if mel is None:
mel = name_ext + ".mel.npy"
mel = np.load(mel)
mel = mel[start_frame : start_frame + units_frame_len]
mel = torch.from_numpy(mel).float()
else:
mel = mel[start_frame : start_frame + units_frame_len]
# load f0
f0 = data_buffer.get('f0')
aug_shift = 0
if aug_flag:
aug_shift = self.pitch_aug_dict[name_ext]
f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
# load units
units = data_buffer.get('units')
if units is None:
path_units = name_ext + ".soft.pt"
units = torch.load(path_units)
units = units[0]
units = repeat_expand_2d(units,f0.size(0),self.unit_interpolate_mode).transpose(0,1)
units = units[start_frame : start_frame + units_frame_len]
# load volume
vol_key = 'aug_vol' if aug_flag else 'volume'
volume = data_buffer.get(vol_key)
volume_frames = volume[start_frame : start_frame + units_frame_len]
# load spk_id
spk_id = data_buffer.get('spk_id')
# load shift
aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
def __len__(self):
return len(self.paths)