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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)
def dataloader(fs = 16000,keyword='',wav_path_or_object=None,g2p=None,
features='both' # phoneme, g2p_embed, both ...
):
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',
' ']
p2idx = {p: idx for idx, p in enumerate(phonemes)}
idx2p = {idx: p for idx, p in enumerate(phonemes)}
fs = fs
wav_path_or_object = wav_path_or_object
keyword = keyword
features = features
# g2p = G2p()
data = pd.DataFrame(columns=['wav','wav_label', 'text', 'duration', 'label'])
target_dict = {}
idx = 0
wav = wav_path_or_object
keyword = keyword
if isinstance(wav_path_or_object, str):
duration = float(wavfile.read(wav)[1].shape[-1])/fs
else:
duration = float(wav_path_or_object.shape[-1])/fs
label = 1
anchor_text = wav.split('/')[-2].lower()
target_dict[idx] = {
'wav': wav,
'wav_label': anchor_text,
'text': keyword,
'duration': duration,
'label': label
}
data = data.append(pd.DataFrame.from_dict(target_dict, 'index'), ignore_index=True)
# g2p & p2idx by g2p_en package
# print(">> Convert word to phoneme")
data['phoneme'] = data['text'].apply(lambda x: g2p(re.sub(r"[^a-zA-Z0-9]+", ' ', x)))
# print(">> Convert phoneme to index")
data['pIndex'] = data['phoneme'].apply(lambda x: [p2idx[t] for t in x])
# print(">> Compute phoneme embedding")
data['g2p_embed'] = data['text'].apply(lambda x: g2p.embedding(x))
data['wav_phoneme'] = data['wav_label'].apply(lambda x: g2p(re.sub(r"[^a-zA-Z0-9]+", ' ', x)))
data['wav_pIndex'] = data['wav_phoneme'].apply(lambda x: [p2idx[t] for t in x])
# print(data['phoneme'])
# Get longest data
data = data.sort_values(by='duration').reset_index(drop=True)
wav_list = data['wav'].values
idx_list = data['pIndex'].values
emb_list = data['g2p_embed'].values
lab_list = data['label'].values
sIdx_list = data['wav_pIndex'].values
# Set dataloader params.
# len = len(data)
maxlen_t = int((int(data['text'].apply(lambda x: len(x)).max() / 10) + 1) * 10)
maxlen_a = int((int(data['duration'].values[-1] / 0.5) + 1 ) * fs / 2)
maxlen_l = int((int(data['wav_label'].apply(lambda x: len(x)).max() / 10) + 1) * 10)
indices = [0]
# load inputs
if isinstance(wav_path_or_object, str):
batch_x = [np.array(wavfile.read(wav_list[i])[1]).astype(np.float32) / 32768.0 for i in indices]
else:
batch_x = [wav_list[i] / 32768.0 for i in indices]
if features == 'both':
batch_p = [np.array(idx_list[i]).astype(np.int32) for i in indices]
batch_e = [np.array(emb_list[i]).astype(np.float32) for i in indices]
else:
if features == 'phoneme':
batch_y = [np.array(idx_list[i]).astype(np.int32) for i in indices]
elif features == 'g2p_embed':
batch_y = [np.array(emb_list[i]).astype(np.float32) for i in indices]
# load outputs
batch_z = [np.array([lab_list[i]]).astype(np.float32) for i in indices]
batch_l = [np.array(sIdx_list[i]).astype(np.int32) for i in indices]
# padding and masking
pad_batch_x = pad_sequences(np.array(batch_x), maxlen=maxlen_a, value=0.0, padding='post', dtype=batch_x[0].dtype)
if features == 'both':
pad_batch_p = pad_sequences(np.array(batch_p), maxlen=maxlen_t, value=0.0, padding='post', dtype=batch_p[0].dtype)
pad_batch_e = pad_sequences(np.array(batch_e), maxlen=maxlen_t, value=0.0, padding='post', dtype=batch_e[0].dtype)
else:
pad_batch_y = pad_sequences(np.array(batch_y), maxlen=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)
pad_batch_l = pad_sequences(np.array(batch_l), maxlen=maxlen_l, value=0.0, padding='post', dtype=batch_l[0].dtype)
if features == 'both':
return pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z,batch_l
else:
return pad_batch_x, pad_batch_y, pad_batch_z,batch_l
# def _load_wav(self, wav):
# return np.array(wavfile.read(wav)[1]).astype(np.float32) / 32768.0
def convert_sequence_to_dataset(dataloader, wav, text, features):
fs = 16000
features=features
duration = float(wavfile.read(wav)[1].shape[-1])/fs
maxlen_t = int((int(len(text) / 10) + 1) * 10)
maxlen_a = int((int(duration / 0.5) + 1 ) * fs / 2)
wav_label = wav.split('/')[-2].lower()
def data_generator():
if features == 'both':
pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z, pad_batch_l = dataloader
yield pad_batch_x, pad_batch_p, pad_batch_e, pad_batch_z, pad_batch_l
else:
pad_batch_x, pad_batch_y, pad_batch_z, pad_batch_l = dataloader
yield pad_batch_x, pad_batch_y, pad_batch_z, pad_batch_l
if features == 'both':
data_dataset = tf.data.Dataset.from_generator(data_generator, output_signature=(
tf.TensorSpec(shape=(None, maxlen_a), dtype=tf.float32),
tf.TensorSpec(shape=(None, maxlen_t), dtype=tf.int32),
tf.TensorSpec(shape=(None, maxlen_t, 256), dtype=tf.float32),
tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
tf.TensorSpec(shape=(None, None), dtype=tf.int32),)
)
else:
data_dataset = tf.data.Dataset.from_generator(data_generator, output_signature=(
tf.TensorSpec(shape=(None, maxlen_a), dtype=tf.float32),
tf.TensorSpec(shape=(None, maxlen_t) if features == 'phoneme' else (None, maxlen_t, 256),
dtype=tf.int32 if features == 'phoneme' else tf.float32),
tf.TensorSpec(shape=(None, 1), dtype=tf.float32),
tf.TensorSpec(shape=(None, None), dtype=tf.int32),)
)
# data_dataset = data_dataset.cache()
data_dataset = data_dataset.prefetch(1)
return data_dataset
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