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live_dataloader.py
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live_dataloader.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 28 11:46:20 2018
@author: sungkyun
- Live Data Loader for LJ-Speech Dataset.
"""
import pandas as pd
import numpy as np
import torch
import string, glob
import librosa
from torch.utils.data.dataset import Dataset
from nnmnkwii.datasets import FileDataSource, FileSourceDataset, MemoryCacheDataset
#DATA_ROOT = '/mnt/ssd2/data/LJSpeech-1.1'
N_TRAIN = 13000 # N_TEST = 100 (13000~13099)
X_SPEC_MAX = 193.99077 # Required for feature normalization
X_MELSPEC_MAX = 0.04071
OMIT_DATA_ROWS =[945, 1119, 1149, 1371, 1853, 1929, 1939, 2965, 3405, 4246, 4670,
4684, 4699, 4706, 4713, 5163, 5164, 5165, 5166, 5167, 5169, 5172,
5174, 5182, 5185, 5532, 5935, 5964, 5974, 6039] # Reduce sentences with foreign characters.
MAX_LEN_TEXT = 250+2 # Max-lengths are required for z-padding
MAX_LEN_MELSPEC = 217+2
MAX_LEN_SPEC = 868+8
#MAX_LEN_PAIRED_TEXT = 360
#MAX_LEN_PAIRED_MELSPEC = 291# Max-lengths are required for z-padding
#MAX_LEN_PAIRED_SPEC = 1164
class SpecSource(FileDataSource):
'''
USAGE:
Mel-spec-only : mode = ['melspec']
Spec, Mel-spec: mode = ['SSRN']
'''
def __init__(self, wav_data_root=None, file_sel_range=None, target_sr = 22050, output_mode=['melspec'], norm_factors=[X_SPEC_MAX,X_MELSPEC_MAX], max_num_files=-1):
file_list = sorted(glob.glob(wav_data_root + "/*.wav")) # All *.wav files into list
for i in sorted(OMIT_DATA_ROWS, reverse=True):
del file_list[i] # Omitting foreign language files
self.input_wav_files = file_list
self.target_sr = target_sr
self.output_mode = output_mode
self.norm_factors = norm_factors # [max_X_spec, max_X_melspec]
if max_num_files is not -1 :self.input_wav_files = self.input_wav_files[:max_num_files]
if file_sel_range is not None: self.input_wav_files = self.input_wav_files[file_sel_range[0]:file_sel_range[1]]
self.mel_filter = librosa.filters.mel(self.target_sr, 1024, n_mels=80)
def collect_files(self):
# This class method is required..
return self.input_wav_files
def collect_features(self, path):
x, fs = librosa.load(path, sr=self.target_sr, mono=True, dtype=np.float32)
x_spec = np.abs(librosa.core.stft(y=x, n_fft=1024, hop_length=256, window='hann')) #STFT size : [513,4T]
x_spec = np.power(x_spec / self.norm_factors[0], 0.6) # normalize by max(abs(X_spec))
x_spec_fixed_t = int(x_spec.shape[1]/4) * 4
x_spec = x_spec[:, :x_spec_fixed_t]
x_melspec = np.matmul(self.mel_filter, x_spec)
x_melspec = x_melspec[:, np.arange(0, x_spec_fixed_t, 4)]
#x_melspec = np.power(x_melspec / self.norm_factors[1], 0.6) # normalize by max(abs(X_melspec))
x_melspec = np.power((x_melspec / np.max(x_melspec)) * np.random.uniform(0.9,1.0), 0.6)
if 'SSRN' in self.output_mode:
return x_spec, x_melspec
else:
return x_melspec
#%%
class LJSpeechDataset(Dataset):
'''
Live Data Loader for LJ-Speech Dataset.
OUTPUT MODE:
mode = ['melspec'] : return index, text, melspec
mode = ['SSRN'] : return index, spec, melspec
'''
def __init__(self, data_root_dir=None, train_mode=False , output_mode='melspec', transform=None, data_sel=None):
self.wav_root_dir = data_root_dir + '/wavs/'
self.train_mode = train_mode
self.output_mode = output_mode
self.transform = transform
self.data_sel = data_sel
self.max_len_text = MAX_LEN_TEXT
self.max_len_melspec = MAX_LEN_MELSPEC
self.max_len_spec = MAX_LEN_SPEC
# self.max_len_paired_text = MAX_LEN_PAIRED_TEXT # Max-lengths are required for z-padding
# self.max_len_paired_spec = MAX_LEN_PAIRED_SPEC
# self.max_len_paired_melspec = MAX_LEN_PAIRED_MELSPEC
# Preparing Text:
self.text_csv_path = data_root_dir + '/metadata.csv'
self.reduce_punc_table = str.maketrans(string.ascii_uppercase, string.ascii_lowercase,
'0123456789!#"$%&\()*+/:;<=>?@[\\]^_`{|}~')
self.chr2int_table = dict(zip(" ',-." + string.ascii_lowercase, np.arange(0, 31)))
df = pd.read_csv(self.text_csv_path, index_col=False, sep='|', header=None, memory_map=True) # memory_map: speed-up reading.
nan_rows = df[df[2].isnull()].index.values
df.iloc[nan_rows,2] = df.iloc[nan_rows,1] # fixing dataset NaN value bugs...
df = df.drop(1, axis=1); df.columns=['file_id', 'text']
df = df.drop(OMIT_DATA_ROWS, axis=0).reset_index(drop=True) # Omitting foreign language..
if self.train_mode is True:
self.file_ids = df.iloc[0:N_TRAIN, 0] # file_ids: LJ**-**** (13,000)
self.texts = df.iloc[0:N_TRAIN, 1]
if self.data_sel is not None:
self.file_ids = self.file_ids[self.data_sel].reset_index(drop=True)
self.texts = self.texts[self.data_sel].reset_index(drop=True)
else:
self.file_ids = df.iloc[N_TRAIN:, 0].reset_index(drop=True) # (100)
self.texts = df.iloc[N_TRAIN:, 1].reset_index(drop=True)
if self.data_sel is not None:
self.file_ids = self.file_ids[self.data_sel].reset_index(drop=True)
self.texts = self.texts[self.data_sel].reset_index(drop=True)
# Prepraing Audio:
if self.train_mode is True:
self.spec_features = MemoryCacheDataset(
FileSourceDataset(
SpecSource(
wav_data_root=self.wav_root_dir,
file_sel_range=[0, N_TRAIN],
output_mode=self.output_mode)),
cache_size=len(self.file_ids))
else:
self.spec_features = MemoryCacheDataset(
FileSourceDataset(
SpecSource(
wav_data_root=self.wav_root_dir,
file_sel_range=[N_TRAIN, None],
output_mode=self.output_mode)),
cache_size=len(self.file_ids))
assert(len(self.file_ids) == len(self.spec_features))
# # Pairing: Sort and divide by feature lengths, then concat small + large
# lengths = np.load('mspec_length_train_13000.npy')
# sorted_by_len = np.argsort(lengths)
# n_org = len(sorted_by_len)
# n_pairs = int(n_org / 2)
# self.paired_items = list()
# for i in range(n_pairs):
# self.paired_items.append([sorted_by_len[i], sorted_by_len[n_org - 1 - i]])
return None
def __getitem__(self, index): # = Index of self.paired_items
# # Decouple paired index to idx1, idx2
# _choice = np.random.choice([0,1])
# idx1, idx2 = paired_items[index][_choice], paired_items[index][1-_choice]
if self.output_mode is 'melspec':
text = self.chr2int(self.texts[index])
melspec = self.spec_features[index]
text, nz_text = self.zeropad(text, self.max_len_text)
melspec, nz_melspec = self.zeropad(melspec, self.max_len_melspec)
return index, torch.LongTensor(text), melspec, np.asarray([nz_text, nz_melspec])
else: # if self.output_mode is 'SSRN':
spec, melspec = self.spec_features[index]
spec, nz_spec = self.zeropad(spec, self.max_len_spec)
melspec, nz_melspec = self.zeropad(melspec, self.max_len_melspec)
return index, spec, melspec, np.asarray([nz_spec, nz_melspec])
#np.flip(paired_items[30], axis=0)
def __len__(self):
#return len(self.paired_items)
return len(self.file_ids)
def chr2int(self, text):
# 'City$,' ==> ['c','i','t','y',','] ==> [7,13,24,29,2]
text = list(text.translate(self.reduce_punc_table))
return np.asarray([self.chr2int_table[c] for c in text])
def zeropad(self, x, target_length):
if len(x.shape) is 1:
# 1D input:
n_zeros = target_length - len(x)
#x = np.pad(x, (n_zeros,0), 'constant', constant_values=(0,0))
x = np.pad(x, (1,n_zeros-1), 'constant', constant_values=(0,0)) # letf 1 + right (all-1)-zpading!
else:
# 2D input: D x T
n_zeros = target_length - x.shape[1]
xz = np.zeros((x.shape[0], x.shape[1]+n_zeros))
#xz[:, n_zeros:] = x
xz[:, 1:(xz.shape[1] - n_zeros + 1)] = x # right-zpading!!
x = xz
return x, n_zeros