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- #coding: utf-8
- import os
- import os.path as osp
- import time
- import random
- import numpy as np
- import random
- import soundfile as sf
- import librosa
- import torch
- from torch import nn
- import torch.nn.functional as F
- import torchaudio
- from torch.utils.data import DataLoader
- import logging
- logger = logging.getLogger(__name__)
- logger.setLevel(logging.DEBUG)
- import pandas as pd
- _pad = "$"
- _punctuation = ';:,.!?¡¿—…"«»“” '
- _letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
- _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
- # Export all symbols:
- symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
- dicts = {}
- for i in range(len((symbols))):
- dicts[symbols[i]] = i
- class TextCleaner:
- def __init__(self, dummy=None):
- self.word_index_dictionary = dicts
- def __call__(self, text):
- indexes = []
- for char in text:
- try:
- indexes.append(self.word_index_dictionary[char])
- except KeyError:
- print(text)
- return indexes
- np.random.seed(1)
- random.seed(1)
- SPECT_PARAMS = {
- "n_fft": 2048,
- "win_length": 1200,
- "hop_length": 300
- }
- MEL_PARAMS = {
- "n_mels": 80,
- }
- to_mel = torchaudio.transforms.MelSpectrogram(
- n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
- mean, std = -4, 4
- def preprocess(wave):
- wave_tensor = torch.from_numpy(wave).float()
- mel_tensor = to_mel(wave_tensor)
- mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
- return mel_tensor
- class FilePathDataset(torch.utils.data.Dataset):
- def __init__(self,
- data_list,
- root_path,
- sr=24000,
- data_augmentation=False,
- validation=False,
- OOD_data="Data/OOD_texts.txt",
- min_length=50,
- ):
- spect_params = SPECT_PARAMS
- mel_params = MEL_PARAMS
- self.root_path = root_path
- _data_list = [l.strip().split('|') for l in data_list]
- _final_data_list = []
- for data in _data_list:
- wave_path = data[0]
- seconds = librosa.get_duration(path=osp.join(self.root_path, wave_path), sr=sr)
- if seconds > 1.5:
- _final_data_list.append(data)
- self.data_list = [data if len(data) == 3 else (*data, 0) for data in _final_data_list]
- self.text_cleaner = TextCleaner()
- self.sr = sr
- self.df = pd.DataFrame(self.data_list)
- self.to_melspec = torchaudio.transforms.MelSpectrogram(**MEL_PARAMS)
- self.mean, self.std = -4, 4
- self.data_augmentation = data_augmentation and (not validation)
- self.max_mel_length = 192
-
- self.min_length = min_length
- with open(OOD_data, 'r', encoding='utf-8') as f:
- tl = f.readlines()
- idx = 1 if '.wav' in tl[0].split('|')[0] else 0
- self.ptexts = [t.split('|')[idx] for t in tl]
- def __len__(self):
- return len(self.data_list)
- def __getitem__(self, idx):
- data = self.data_list[idx]
- path = data[0]
-
- wave, text_tensor, speaker_id = self._load_tensor(data)
-
- mel_tensor = preprocess(wave).squeeze()
-
- acoustic_feature = mel_tensor.squeeze()
- length_feature = acoustic_feature.size(1)
- acoustic_feature = acoustic_feature[:, :(length_feature - length_feature % 2)]
-
- # get reference sample
- ref_data = (self.df[self.df[2] == str(speaker_id)]).sample(n=1).iloc[0].tolist()
- ref_mel_tensor, ref_label = self._load_data(ref_data[:3])
-
- # get OOD text
-
- ps = ""
-
- while len(ps) < self.min_length:
- rand_idx = np.random.randint(0, len(self.ptexts) - 1)
- ps = self.ptexts[rand_idx]
-
- text = self.text_cleaner(ps)
- text.insert(0, 0)
- text.append(0)
- ref_text = torch.LongTensor(text)
-
- return speaker_id, acoustic_feature, text_tensor, ref_text, ref_mel_tensor, ref_label, path, wave
- def _load_tensor(self, data):
- wave_path, text, speaker_id = data
- speaker_id = int(speaker_id)
- wave, sr = sf.read(osp.join(self.root_path, wave_path))
- if wave.shape[-1] == 2:
- wave = wave[:, 0].squeeze()
- if sr != 24000:
- wave = librosa.resample(wave, orig_sr=sr, target_sr=24000)
-
- wave = np.concatenate([np.zeros([5000]), wave, np.zeros([5000])], axis=0)
-
- text = self.text_cleaner(text)
-
- text.insert(0, 0)
- text.append(0)
-
- text = torch.LongTensor(text)
- return wave, text, speaker_id
- def _load_data(self, data):
- wave, text_tensor, speaker_id = self._load_tensor(data)
- mel_tensor = preprocess(wave).squeeze()
- mel_length = mel_tensor.size(1)
- if mel_length > self.max_mel_length:
- random_start = np.random.randint(0, mel_length - self.max_mel_length)
- mel_tensor = mel_tensor[:, random_start:random_start + self.max_mel_length]
- return mel_tensor, speaker_id
- class Collater(object):
- """
- Args:
- adaptive_batch_size (bool): if true, decrease batch size when long data comes.
- """
- def __init__(self, return_wave=False):
- self.text_pad_index = 0
- self.min_mel_length = 192
- self.max_mel_length = 192
- self.return_wave = return_wave
-
- def __call__(self, batch):
- # batch[0] = wave, mel, text, f0, speakerid
- batch_size = len(batch)
- # sort by mel length
- lengths = [b[1].shape[1] for b in batch]
- batch_indexes = np.argsort(lengths)[::-1]
- batch = [batch[bid] for bid in batch_indexes]
- nmels = batch[0][1].size(0)
- max_mel_length = max([b[1].shape[1] for b in batch])
- max_text_length = max([b[2].shape[0] for b in batch])
- max_rtext_length = max([b[3].shape[0] for b in batch])
- labels = torch.zeros((batch_size)).long()
- mels = torch.zeros((batch_size, nmels, max_mel_length)).float()
- texts = torch.zeros((batch_size, max_text_length)).long()
- ref_texts = torch.zeros((batch_size, max_rtext_length)).long()
- input_lengths = torch.zeros(batch_size).long()
- ref_lengths = torch.zeros(batch_size).long()
- output_lengths = torch.zeros(batch_size).long()
- ref_mels = torch.zeros((batch_size, nmels, self.max_mel_length)).float()
- ref_labels = torch.zeros((batch_size)).long()
- paths = ['' for _ in range(batch_size)]
- waves = [None for _ in range(batch_size)]
-
- for bid, (label, mel, text, ref_text, ref_mel, ref_label, path, wave) in enumerate(batch):
- mel_size = mel.size(1)
- text_size = text.size(0)
- rtext_size = ref_text.size(0)
- labels[bid] = label
- mels[bid, :, :mel_size] = mel
- texts[bid, :text_size] = text
- ref_texts[bid, :rtext_size] = ref_text
- input_lengths[bid] = text_size
- ref_lengths[bid] = rtext_size
- output_lengths[bid] = mel_size
- paths[bid] = path
- ref_mel_size = ref_mel.size(1)
- ref_mels[bid, :, :ref_mel_size] = ref_mel
-
- ref_labels[bid] = ref_label
- waves[bid] = wave
- return waves, texts, input_lengths, ref_texts, ref_lengths, mels, output_lengths, ref_mels
- def build_dataloader(path_list,
- root_path,
- validation=False,
- OOD_data="Data/OOD_texts.txt",
- min_length=50,
- batch_size=4,
- num_workers=1,
- device='cpu',
- collate_config={},
- dataset_config={}):
-
- dataset = FilePathDataset(path_list, root_path, OOD_data=OOD_data, min_length=min_length, validation=validation, **dataset_config)
- collate_fn = Collater(**collate_config)
- data_loader = DataLoader(dataset,
- batch_size=batch_size,
- shuffle=(not validation),
- num_workers=num_workers,
- drop_last=(not validation),
- collate_fn=collate_fn,
- pin_memory=(device != 'cpu'))
- return data_loader
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