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import os
import torch
import torch.nn.functional as F
import torch.utils.data
from torch import LongTensor
from tqdm import tqdm
import torchaudio
from pypinyin import Style, lazy_pinyin
from ttts.gpt.voice_tokenizer import VoiceBpeTokenizer
import json
import os
def read_jsonl(path):
with open(path, 'r') as f:
json_str = f.read()
data_list = []
for line in json_str.splitlines():
data = json.loads(line)
data_list.append(data)
return data_list
def write_jsonl(path, all_paths):
with open(path,'w', encoding='utf-8') as file:
for item in all_paths:
json.dump(item, file, ensure_ascii=False)
file.write('\n')
class CLVPDataset(torch.utils.data.Dataset):
def __init__(self, opt):
self.tok = VoiceBpeTokenizer('gpt/gpt_tts_tokenizer.json')
self.jsonl_path = opt['dataset']['path']
self.audiopaths_and_text = read_jsonl(self.jsonl_path)
def __getitem__(self, index):
# Fetch text and add start/stop tokens.
audiopath_and_text = self.audiopaths_and_text[index]
audiopath, text = audiopath_and_text['path'], audiopath_and_text['text']
text = ' '.join(lazy_pinyin(text, style=Style.TONE3, neutral_tone_with_five=True))
text = self.tok.encode(text)
text = LongTensor(text)
# Fetch quantized MELs
quant_path = audiopath + '.melvq.pth'
qmel = LongTensor(torch.load(quant_path)[0])
return text, qmel
def __len__(self):
return len(self.audiopaths_and_text)
class CLVPCollater():
def __init__(self):
pass
def __call__(self, batch):
batch = [x for x in batch if x is not None]
if len(batch)==0:
return None
text_lens = [len(x[0]) for x in batch]
max_text_len = max(text_lens)
qmel_lens = [len(x[1]) for x in batch]
max_qmel_len = max(qmel_lens)
texts = []
qmels = []
# This is the sequential "background" tokens that are used as padding for text tokens, as specified in the DALLE paper.
for b in batch:
text, qmel = b
text = F.pad(text, (0, max_text_len-len(text)), value=0)
texts.append(text)
qmels.append(F.pad(qmel, (0, max_qmel_len-len(qmel)), value=0))
padded_qmel = torch.stack(qmels)
padded_texts = torch.stack(texts)
return {
'padded_text': padded_texts,
'text_lengths': LongTensor(text_lens),
'padded_qmel': padded_qmel,
'qmel_lengths': LongTensor(qmel_lens),
}
if __name__ == '__main__':
params = {
'mode': 'gpt_tts',
'path': 'E:\\audio\\LJSpeech-1.1\\ljs_audio_text_train_filelist.txt',
'phase': 'train',
'n_workers': 0,
'batch_size': 16,
'mel_vocab_size': 512,
}
cfg = json.load(open('gpt/config.json'))
ds = CLVPDataset(cfg)
dl = torch.utils.data.DataLoader(ds, **cfg['dataloader'], collate_fn=CLVPCollater())
i = 0
m = []
max_text = 0
max_mel = 0
for b in tqdm(dl):
break
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