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data_generator.py
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193 lines (164 loc) · 6.89 KB
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'''
Required:
mixture: [B, K, L]
source: [B, nspk, K, L]
get_batch
'''
import numpy as np
import librosa
import os
from params import *
import pickle
class DataGenerator(object):
def __init__(self, batch_size, max_k,
save_dir=None, data_dir=None, name='data_gen'):
self.name = name
self.batch_size = batch_size
self.max_k = max_k
self.data_dir = data_dir
self.save_dir = save_dir
if save_dir is not None and not os.path.exists(save_dir):
os.mkdir(save_dir)
self.data_subdir = ['s1', 's2', 'mix']
self.data_type = ['tr', 'cv']
self.spks = []
self.init_samples()
self.epoch = 0
self.idx = 0
def init_samples(self):
self.samples = {'mix': [], 's': []}
self.sample_size = 0
def gen_data(self):
if self.data_dir and self.save_dir is None:
raise AssertionError
for dt in self.data_type:
self.init_samples()
save_cnt = 1
save_dir = os.path.join(self.save_dir, dt)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
dt_path = os.path.join(self.data_dir, dt)
dt_mix_path = os.path.join(dt_path, 'mix')
dt_s1_path = os.path.join(dt_path, 's1')
dt_s2_path = os.path.join(dt_path, 's2')
list_mix = os.listdir(dt_mix_path)
for wav_file in list_mix:
if not wav_file.endswith('.wav'):
continue
# print(wav_file)
mix_path = os.path.join(dt_mix_path, wav_file)
s1_path = os.path.join(dt_s1_path, wav_file)
s2_path = os.path.join(dt_s2_path, wav_file)
spk1 = wav_file[:3]
spk2 = wav_file.split(sep='_')[2][:3]
if spk1 not in self.spks:
self.spks.append(spk1)
if spk2 not in self.spks:
self.spks.append(spk2)
mix, _ = librosa.load(mix_path, sr=sr)
s1, _ = librosa.load(s1_path, sr=sr)
s2, _ = librosa.load(s2_path, sr=sr)
self.get_sample(mix, s1, s2, [spk1, spk2])
self.sample_size = len(self.samples['mix'])
if self.sample_size % 50 == 0:
print(self.sample_size)
if self.sample_size >= save_cnt * 50000:
pickle.dump(self.samples,
open(save_dir + '/raw_' + str(self.max_k) + '-' + str(self.sample_size) + '.pkl',
'wb'))
save_cnt += 1
pickle.dump(self.samples,
open(save_dir + '/raw_' + str(self.max_k) + '-' + str(self.sample_size) + '.pkl',
'wb'))
def get_sample(self, mix, s1, s2, spks):
spk_num = len(spks)
mix_len = len(mix)
sample_num = int(np.ceil(mix_len / L))
if sample_num < self.max_k:
sample_num = self.max_k
max_len = sample_num * L
pad_s1 = np.concatenate([s1, np.zeros([max_len - len(s1)])])
pad_s2 = np.concatenate([s2, np.zeros([max_len - len(s1)])])
pad_mix = np.concatenate([mix, np.zeros([max_len - len(mix)])])
k_ = 0
while k_ + self.max_k <= sample_num:
begin = k_ * L
end = (k_ +self.max_k) * L
sample_mix = pad_mix[begin:end]
sample_s1 = pad_s1[begin:end]
sample_s2 = pad_s2[begin:end]
sample_mix = np.reshape(sample_mix, [self.max_k, L])
sample_s1 = np.reshape(sample_s1, [self.max_k, L])
sample_s2 = np.reshape(sample_s2, [self.max_k, L])
sample_s = np.dstack((sample_s1, sample_s2))
sample_s = np.transpose(sample_s, (2, 0, 1))
self.samples['mix'].append(sample_mix)
self.samples['s'].append(sample_s)
k_ += self.max_k
def load_data(self, data_path):
self.samples = pickle.load(open(data_path, 'rb'))
self.sample_size = len(self.samples['mix'])
print('>> {0}: Loading samples from pkl: {1}...'.format(self.name, data_path))
def shuffle_dict(self):
rand_per = np.random.permutation(self.sample_size)
self.samples['mix'] = np.array(self.samples['mix'])[rand_per]
self.samples['s'] = np.array(self.samples['s'])[rand_per]
def get_a_sample(self, mix, s1, s2, spks, max_k):
spk_num = len(spks)
mix_len = len(mix)
sample_num = int(np.ceil(mix_len / L / max_k)) * max_k
max_len = sample_num * L
pad_s1 = np.concatenate([s1, np.zeros([max_len - len(s1)])])
pad_s2 = np.concatenate([s2, np.zeros([max_len - len(s1)])])
pad_mix = np.concatenate([mix, np.zeros([max_len - len(mix)])])
test_sample = {
'mix': [],
's': [],
}
k_ = 0
while k_ + self.max_k <= sample_num:
begin = k_ * L
end = (k_ + max_k) * L
sample_mix = pad_mix[begin:end]
sample_s1 = pad_s1[begin:end]
sample_s2 = pad_s2[begin:end]
sample_mix = np.reshape(sample_mix, [max_k, L])
sample_s1 = np.reshape(sample_s1, [max_k, L])
sample_s2 = np.reshape(sample_s2, [max_k, L])
sample_s = np.dstack((sample_s1, sample_s2))
sample_s = np.transpose(sample_s, (2, 0, 1))
test_sample['mix'].append(sample_mix)
test_sample['s'].append(sample_s)
k_ += max_k
return test_sample
def gen_batch(self, batch_size=None):
if batch_size is None:
batch_size = self.batch_size
n_begin = self.idx
n_end = self.idx + batch_size
if n_end >= self.sample_size:
# rewire the index
self.idx = 0
n_begin = self.idx
n_end = self.idx + batch_size
self.epoch += 1
self.shuffle_dict()
self.idx += batch_size
samples = {
'mix': self.samples['mix'][n_begin: n_end],
's': self.samples['s'][n_begin: n_end]
}
return samples
if __name__ == '__main__':
# data_gen = DataGenerator(batch_size=1, max_k=int(0.5/0.005), save_dir='/home/grz/data/SSSR/wsj0_tasnet/',
# data_dir='/home/grz/data/SSSR/wsj0/min/',
# name='gen_data')
# data_gen.gen_data()
# data_gen = DataGenerator(batch_size=1, max_k=int(4/0.005), save_dir='/home/grz/data/SSSR/wsj0_tasnet/',
# data_dir='/home/grz/data/SSSR/wsj0/min/',
# name='gen_data')
# data_gen.gen_data()
data_gen = DataGenerator(batch_size=1, max_k=int(2/0.005), save_dir='/home/grz/data/SSSR/wsj0_tasnet/',
data_dir='/home/grz/data/SSSR/wsj0/min/',
name='gen_data')
data_gen.gen_data()