Spaces:
Runtime error
Runtime error
File size: 9,152 Bytes
2045faa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
import math, os, re, sys
from pathlib import Path
import numpy as np
import pandas as pd
from multiprocessing import Pool
from scipy.io import wavfile
import tensorflow as tf
from tensorflow.keras.utils import Sequence, OrderedEnqueuer
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.sequence import pad_sequences
sys.path.append(os.path.dirname(__file__))
from g2p.g2p_en.g2p import G2p
import warnings
warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)
np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)
class GoogleCommandsDataloader(Sequence):
def __init__(self,
batch_size,
fs = 16000,
wav_dir='/home/DB/google_speech_commands',
target_list=['yes', 'no', 'up', 'down', 'left', 'right', 'on', 'off', 'stop', 'go'],
features='g2p_embed', # phoneme, g2p_embed, both ...
shuffle=True,
testset_only=False,
pkl=None,
):
phonemes = ["<pad>", ] + ['AA0', 'AA1', 'AA2', 'AE0', 'AE1', 'AE2', 'AH0', 'AH1', 'AH2', 'AO0',
'AO1', 'AO2', 'AW0', 'AW1', 'AW2', 'AY0', 'AY1', 'AY2', 'B', 'CH',
'D', 'DH', 'EH0', 'EH1', 'EH2', 'ER0', 'ER1', 'ER2', 'EY0', 'EY1',
'EY2', 'F', 'G', 'HH', 'IH0', 'IH1', 'IH2', 'IY0', 'IY1', 'IY2',
'JH', 'K', 'L', 'M', 'N', 'NG', 'OW0', 'OW1', 'OW2', 'OY0',
'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH0', 'UH1',
'UH2', 'UW', 'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH',
' ']
self.p2idx = {p: idx for idx, p in enumerate(phonemes)}
self.idx2p = {idx: p for idx, p in enumerate(phonemes)}
self.batch_size = batch_size
self.fs = fs
self.wav_dir = wav_dir
self.target_list = [x.lower() for x in target_list]
self.testset_only = testset_only
self.features = features
self.shuffle = shuffle
self.pkl = pkl
self.nPhoneme = len(phonemes)
self.g2p = G2p()
self.__prep__()
self.on_epoch_end()
def __prep__(self):
self.data = pd.DataFrame(columns=['wav', 'text', 'duration', 'label'])
if (self.pkl is not None) and (os.path.isfile(self.pkl)):
print(">> Load dataset from {}".format(self.pkl))
self.data = pd.read_pickle(self.pkl)
else:
print(">> Make dataset from {}".format(self.wav_dir))
target_dict = {}
idx = 0
for target in self.target_list:
print(">> Extract from {}".format(target))
if self.testset_only:
test_list = os.path.join(self.wav_dir, 'testing_list.txt')
with open(test_list, "r") as f:
wav_list = f.readlines()
wav_list = [os.path.join(self.wav_dir, x.strip()) for x in wav_list]
wav_list = [x for x in wav_list if target == x.split('/')[-2]]
else:
wav_list = [str(x) for x in Path(os.path.join(self.wav_dir, target)).rglob('*.wav')]
for wav in wav_list:
anchor_text = wav.split('/')[-2].lower()
duration = float(wavfile.read(wav)[1].shape[-1])/self.fs
for comparison_text in self.target_list:
label = 1 if anchor_text == comparison_text else 0
target_dict[idx] = {
'wav': wav,
'text': comparison_text,
'duration': duration,
'label': label
}
idx += 1
self.data = self.data.append(pd.DataFrame.from_dict(target_dict, 'index'), ignore_index=True)
# g2p & p2idx by g2p_en package
print(">> Convert word to phoneme")
self.data['phoneme'] = self.data['text'].apply(lambda x: self.g2p(re.sub(r"[^a-zA-Z0-9]+", ' ', x)))
print(">> Convert phoneme to index")
self.data['pIndex'] = self.data['phoneme'].apply(lambda x: [self.p2idx[t] for t in x])
print(">> Compute phoneme embedding")
self.data['g2p_embed'] = self.data['text'].apply(lambda x: self.g2p.embedding(x))
if (self.pkl is not None) and (not os.path.isfile(self.pkl)):
self.data.to_pickle(self.pkl)
# Get longest data
self.data = self.data.sort_values(by='duration').reset_index(drop=True)
self.wav_list = self.data['wav'].values
self.idx_list = self.data['pIndex'].values
self.emb_list = self.data['g2p_embed'].values
self.lab_list = self.data['label'].values
# Set dataloader params.
self.len = len(self.data)
self.maxlen_t = int((int(self.data['text'].apply(lambda x: len(x)).max() / 10) + 1) * 10)
self.maxlen_a = int((int(self.data['duration'].values[-1] / 0.5) + 1 ) * self.fs / 2)
def __len__(self):
# return total batch-wise length
return math.ceil(self.len / self.batch_size)
def _load_wav(self, wav):
return np.array(wavfile.read(wav)[1]).astype(np.float32) / 32768.0
def __getitem__(self, idx):
# chunking
indices = self.indices[idx * self.batch_size : (idx + 1) * self.batch_size]
# load inputs
batch_x = [np.array(wavfile.read(self.wav_list[i])[1]).astype(np.float32) / 32768.0 for i in indices]
if self.features == 'both':
batch_p = [np.array(self.idx_list[i]).astype(np.int32) for i in indices]
batch_e = [np.array(self.emb_list[i]).astype(np.float32) for i in indices]
else:
if self.features == 'phoneme':
batch_y = [np.array(self.idx_list[i]).astype(np.int32) for i in indices]
elif self.features == 'g2p_embed':
batch_y = [np.array(self.emb_list[i]).astype(np.float32) for i in indices]
# load outputs
batch_z = [np.array([self.lab_list[i]]).astype(np.float32) for i in indices]
# padding and masking
pad_batch_x = pad_sequences(np.array(batch_x), maxlen=self.maxlen_a, value=0.0, padding='post', dtype=batch_x[0].dtype)
if self.features == 'both':
pad_batch_p = pad_sequences(np.array(batch_p), maxlen=self.maxlen_t, value=0.0, padding='post', dtype=batch_p[0].dtype)
pad_batch_e = pad_sequences(np.array(batch_e), maxlen=self.maxlen_t, value=0.0, padding='post', dtype=batch_e[0].dtype)
else:
pad_batch_y = pad_sequences(np.array(batch_y), maxlen=self.maxlen_t, value=0.0, padding='post', dtype=batch_y[0].dtype)
pad_batch_z = pad_sequences(np.array(batch_z), value=0.0, padding='post', dtype=batch_z[0].dtype)
if self.features == 'both':
return pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z
else:
return pad_batch_x, pad_batch_y, pad_batch_z
def on_epoch_end(self):
self.indices = np.arange(self.len)
if self.shuffle == True:
np.random.shuffle(self.indices)
def convert_sequence_to_dataset(dataloader):
def data_generator():
for i in range(dataloader.__len__()):
if dataloader.features == 'both':
pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z = dataloader[i]
yield pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z
else:
pad_batch_x, pad_batch_y, pad_batch_z = dataloader[i]
yield pad_batch_x, pad_batch_y, pad_batch_z
if dataloader.features == 'both':
data_dataset = tf.data.Dataset.from_generator(data_generator, output_signature=(
tf.TensorSpec(shape=(None, dataloader.maxlen_a), dtype=tf.float32),
tf.TensorSpec(shape=(None, dataloader.maxlen_t), dtype=tf.int32),
tf.TensorSpec(shape=(None, dataloader.maxlen_t, 256), dtype=tf.float32),
tf.TensorSpec(shape=(None, 1), dtype=tf.float32),)
)
else:
data_dataset = tf.data.Dataset.from_generator(data_generator, output_signature=(
tf.TensorSpec(shape=(None, dataloader.maxlen_a), dtype=tf.float32),
tf.TensorSpec(shape=(None, dataloader.maxlen_t) if dataloader.features == 'phoneme' else (None, dataloader.maxlen_t, 256),
dtype=tf.int32 if dataloader.features == 'phoneme' else tf.float32),
tf.TensorSpec(shape=(None, 1), dtype=tf.float32),)
)
# data_dataset = data_dataset.cache()
data_dataset = data_dataset.prefetch(1)
return data_dataset
if __name__ == '__main__':
dataloader = GoogleCommandsDataloader(2048, testset_only=True, pkl='/home/DB/google_speech_commands/google_testset.pkl', features='g2p_embed') |