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import os
import json
import torch
from torch.utils.data import Dataset
from text import cleaned_text_to_sequence
def intersperse(lst, item):
result = [item] * (len(lst) * 2 + 1)
result[1::2] = lst
return result
class StableDataset(Dataset):
def __init__(self, filelist_path, hop_length):
self.filelist_path = filelist_path
self.hop_length = hop_length
self._load_filelist(filelist_path)
def _load_filelist(self, filelist_path):
filelist, lengths = [], []
with open(filelist_path, 'r', encoding='utf-8') as f:
for line in f:
line = json.loads(line.strip())
filelist.append((line['mel_path'], line['phone']))
lengths.append(os.path.getsize(line['audio_path']) // (2 * self.hop_length))
self.filelist = filelist
self.lengths = lengths
def __len__(self):
return len(self.filelist)
def __getitem__(self, idx):
mel_path, phone = self.filelist[idx]
mel = torch.load(mel_path, map_location='cpu')
phone = torch.tensor(intersperse(cleaned_text_to_sequence(phone), 0), dtype=torch.long)
return mel, phone
def collate_fn(batch):
texts = [item[1] for item in batch]
mels = [item[0] for item in batch]
text_lengths = torch.tensor([text.size(-1) for text in texts], dtype=torch.long)
mel_lengths = torch.tensor([mel.size(-1) for mel in mels], dtype=torch.long)
# pad to the same length
texts_padded = torch.nested.to_padded_tensor(torch.nested.nested_tensor(texts), padding=0)
mels_padded = torch.nested.to_padded_tensor(torch.nested.nested_tensor(mels), padding=0)
return texts_padded, text_lengths, mels_padded, mel_lengths |