dataset/dataloader_infe.py

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