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data_generator.py
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data_generator.py
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"""
Ke Chen knutchen@ucsd.edu
Tone-Octave Network - data_generator file
This file contains the dataset and data generator classes
"""
import os
import torch
import numpy as np
from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader
from util import index2centf
from feature_extraction import get_CenFreq
def reorganize(x, octave_res):
n_order = []
max_bin = x.shape[1]
for i in range(octave_res):
n_order += [j for j in range(i, max_bin, octave_res)]
nx = [x[:,n_order[i],:] for i in range(x.shape[1])]
nx = np.array(nx)
nx = nx.transpose((1,0,2))
return nx
class TONetTrainDataset(Dataset):
def __init__(self, data_list, config):
self.config = config
self.cfp_dir = os.path.join(config.data_path,config.cfp_dir)
self.f0_dir = os.path.join(config.data_path,"f0ref")
self.data_list = data_list
self.cent_f = np.array(get_CenFreq(config.startfreq, config.stopfreq, config.octave_res))
# init data array
self.data_cfp = []
self.data_gd = []
self.data_tcfp = []
seg_frame = config.seg_frame
shift_frame = config.shift_frame
print("Data List:", data_list)
with open(data_list, "r") as f:
data_txt = f.readlines()
data_txt = [d.split(".")[0] for d in data_txt]
# data_txt = data_txt[:100]
print("Song Size:", len(data_txt))
# process cfp
for i, filename in enumerate(tqdm(data_txt)):
# file set
cfp_file = os.path.join(self.cfp_dir, filename + ".npy")
ref_file = os.path.join(self.f0_dir, filename + ".txt")
# get raw cfp and freq
temp_cfp = np.load(cfp_file, allow_pickle = True)
# temp_cfp[0, :, :] = temp_cfp[1, :, :] * temp_cfp[2, :, :]
temp_freq = np.loadtxt(ref_file)
temp_freq = temp_freq[:,1]
# check length
if temp_freq.shape[0] > temp_cfp.shape[2]:
temp_freq = temp_freq[:temp_cfp.shape[2]]
else:
temp_cfp = temp_cfp[:,:,:temp_freq.shape[0]]
# build data
for j in range(0, temp_cfp.shape[2], shift_frame):
bgnt = j
endt = j + seg_frame
temp_x = temp_cfp[:, :, bgnt:endt]
temp_gd = index2centf(temp_freq[bgnt:endt], self.cent_f)
if temp_x.shape[2] < seg_frame:
rl = temp_x.shape[2]
pad_x = np.zeros((temp_x.shape[0], temp_x.shape[1], seg_frame))
pad_gd = np.zeros((seg_frame))
pad_gd[:rl] = temp_gd
pad_x[:,:, :rl] = temp_x
temp_x = pad_x
temp_gd = pad_gd
temp_tx = reorganize(temp_x[:], config.octave_res)
self.data_tcfp.append(temp_tx)
self.data_cfp.append(temp_x)
self.data_gd.append(temp_gd)
self.data_cfp = np.array(self.data_cfp)
self.data_tcfp = np.array(self.data_tcfp)
self.data_gd = np.array(self.data_gd)
print("Total Datasize:", self.data_cfp.shape)
def __len__(self):
return len(self.data_cfp)
def __getitem__(self,index):
temp_dict = {
"cfp": self.data_cfp[index].astype(np.float32),
"tcfp": self.data_tcfp[index].astype(np.float32),
"gd": self.data_gd[index]
}
return temp_dict
class TONetTestDataset(Dataset):
def __init__(self, data_list, config):
self.config = config
self.cfp_dir = os.path.join(config.data_path,config.cfp_dir)
self.f0_dir = os.path.join(config.data_path,"f0ref")
self.data_list = data_list
self.cent_f = np.array(get_CenFreq(config.startfreq, config.stopfreq, config.octave_res))
# init data array
self.data_cfp = []
self.data_gd = []
self.data_len = []
self.data_tcfp = []
seg_frame = config.seg_frame
shift_frame = config.shift_frame
print("Data List:", data_list)
with open(data_list, "r") as f:
data_txt = f.readlines()
data_txt = [d.split(".")[0] for d in data_txt]
print("Song Size:", len(data_txt))
# process cfp
for i, filename in enumerate(tqdm(data_txt)):
group_cfp = []
group_gd = []
group_tcfp = []
# file set
cfp_file = os.path.join(self.cfp_dir, filename + ".npy")
ref_file = os.path.join(self.f0_dir, filename + ".txt")
# get raw cfp and freq
temp_cfp = np.load(cfp_file, allow_pickle = True)
# temp_cfp[0, :, :] = temp_cfp[1, :, :] * temp_cfp[2, :, :]
temp_freq = np.loadtxt(ref_file)
temp_freq = temp_freq[:,1]
self.data_len.append(len(temp_freq))
# check length
if temp_freq.shape[0] > temp_cfp.shape[2]:
temp_freq = temp_freq[:temp_cfp.shape[2]]
else:
temp_cfp = temp_cfp[:,:,:temp_freq.shape[0]]
# build data
for j in range(0, temp_cfp.shape[2], shift_frame):
bgnt = j
endt = j + seg_frame
temp_x = temp_cfp[:, :, bgnt:endt]
temp_gd = temp_freq[bgnt:endt]
if temp_x.shape[2] < seg_frame:
rl = temp_x.shape[2]
pad_x = np.zeros((temp_x.shape[0], temp_x.shape[1], seg_frame))
pad_gd = np.zeros(seg_frame)
pad_gd[:rl] = temp_gd
pad_x[:,:, :rl] = temp_x
temp_x = pad_x
temp_gd = pad_gd
temp_tx = reorganize(temp_x[:], config.octave_res)
group_tcfp.append(temp_tx)
group_cfp.append(temp_x)
group_gd.append(temp_gd)
group_tcfp = np.array(group_tcfp)
group_cfp = np.array(group_cfp)
group_gd = np.array(group_gd)
self.data_tcfp.append(group_tcfp)
self.data_cfp.append(group_cfp)
self.data_gd.append(group_gd)
def __len__(self):
return len(self.data_cfp)
def __getitem__(self,index):
temp_dict = {
"cfp": self.data_cfp[index].astype(np.float32),
"tcfp": self.data_tcfp[index].astype(np.float32),
"gd": self.data_gd[index],
"length": self.data_len[index]
}
return temp_dict