# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/t2s_dataset.py import pdb import sys # sys.path.append("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert") import traceback, os from typing import Dict from typing import List import numpy as np import pandas as pd import torch, json from torch.utils.data import DataLoader from torch.utils.data import Dataset from transformers import AutoTokenizer from text import cleaned_text_to_sequence # from config import exp_dir def batch_sequences(sequences: List[np.array], axis: int = 0, pad_value: int = 0): seq = sequences[0] ndim = seq.ndim if axis < 0: axis += ndim dtype = seq.dtype pad_value = dtype.type(pad_value) seq_lengths = [seq.shape[axis] for seq in sequences] max_length = np.max(seq_lengths) padded_sequences = [] for seq, length in zip(sequences, seq_lengths): padding = ( [(0, 0)] * axis + [(0, max_length - length)] + [(0, 0)] * (ndim - axis - 1) ) padded_seq = np.pad(seq, padding, mode="constant", constant_values=pad_value) padded_sequences.append(padded_seq) batch = np.stack(padded_sequences) return batch class Text2SemanticDataset(Dataset): """dataset class for text tokens to semantic model training.""" def __init__( self, phoneme_path: str, semantic_path: str, max_sample: int = None, max_sec: int = 100, pad_val: int = 1024, # min value of phoneme/sec min_ps_ratio: int = 3, # max value of phoneme/sec max_ps_ratio: int = 25, ) -> None: super().__init__() self.semantic_data = pd.read_csv( semantic_path, delimiter="\t", encoding="utf-8" ) # get dict self.path2 = phoneme_path # "%s/2-name2text.txt"%exp_dir#phoneme_path self.path3 = "%s/3-bert" % ( os.path.basename(phoneme_path) ) # "%s/3-bert"%exp_dir#bert_dir self.path6 = semantic_path # "%s/6-name2semantic.tsv"%exp_dir#semantic_path assert os.path.exists(self.path2) assert os.path.exists(self.path6) self.phoneme_data = {} with open(self.path2, "r", encoding="utf8") as f: lines = f.read().strip("\n").split("\n") for line in lines: tmp = line.split("\t") if len(tmp) != 4: continue self.phoneme_data[tmp[0]] = [tmp[1], tmp[2], tmp[3]] # self.phoneme_data = np.load(phoneme_path, allow_pickle=True).item() # pad for semantic tokens self.PAD: int = pad_val # self.hz = 25 # with open("/data/docker/liujing04/gpt-vits/mq-vits-s1bert_no_bert/configs/s2.json", "r") as f:data = f.read() # data=json.loads(data)["model"]["semantic_frame_rate"]#50hz # self.hz=int(data[:-2])# self.hz = int(os.environ.get("hz", "25hz")[:-2]) # max seconds of semantic token self.max_sec = max_sec self.min_ps_ratio = min_ps_ratio self.max_ps_ratio = max_ps_ratio if max_sample is not None: self.semantic_data = self.semantic_data[:max_sample] # {idx: (semantic, phoneme)} # semantic list, phoneme list self.semantic_phoneme = [] self.item_names = [] self.inited = False if not self.inited: # 调用初始化函数 self.init_batch() self.inited = True del self.semantic_data del self.phoneme_data # self.tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large") # self.tokenizer = AutoTokenizer.from_pretrained("/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large") def init_batch(self): semantic_data_len = len(self.semantic_data) phoneme_data_len = len(self.phoneme_data.keys()) print("semantic_data_len:", semantic_data_len) print("phoneme_data_len:", phoneme_data_len) print(self.semantic_data) idx = 0 num_not_in = 0 num_deleted_bigger = 0 num_deleted_ps = 0 for i in range(semantic_data_len): # 先依次遍历 # get str item_name = self.semantic_data.iloc[i,0] # print(self.phoneme_data) try: phoneme, word2ph, text = self.phoneme_data[item_name] except Exception: traceback.print_exc() # print(f"{item_name} not in self.phoneme_data !") num_not_in += 1 continue semantic_str = self.semantic_data.iloc[i,1] # get token list semantic_ids = [int(idx) for idx in semantic_str.split(" ")] # (T), 是否需要变成 (1, T) -> 不需要，因为需要求 len # 过滤掉太长的样本 if ( len(semantic_ids) > self.max_sec * self.hz ): #########1###根据token个数推测总时长过滤时长60s（config里）#40*25=1k num_deleted_bigger += 1 continue # (T, ), 这个速度不会很慢，所以可以在一开始就处理，无需在 __getitem__ 里面单个处理#### phoneme = phoneme.split(" ") try: phoneme_ids = cleaned_text_to_sequence(phoneme) except: traceback.print_exc() # print(f"{item_name} not in self.phoneme_data !") num_not_in += 1 continue # if len(phoneme_ids) >400:###########2：改为恒定限制为semantic/2.5就行 if ( len(phoneme_ids) > self.max_sec * self.hz / 2.5 ): ###########2：改为恒定限制为semantic/2.5就行 num_deleted_ps += 1 continue # if len(semantic_ids) > 1000:###########3 # num_deleted_bigger += 1 # continue ps_ratio = len(phoneme_ids) / (len(semantic_ids) / self.hz) if ( ps_ratio > self.max_ps_ratio or ps_ratio < self.min_ps_ratio ): ##########4#3~25#每秒多少个phone num_deleted_ps += 1 # print(item_name) continue self.semantic_phoneme.append((semantic_ids, phoneme_ids)) idx += 1 self.item_names.append(item_name) min_num = 100 # 20直接不补#30补了也不存ckpt leng = len(self.semantic_phoneme) if leng < min_num: tmp1 = self.semantic_phoneme tmp2 = self.item_names self.semantic_phoneme = [] self.item_names = [] for _ in range(max(2, int(min_num / leng))): self.semantic_phoneme += tmp1 self.item_names += tmp2 if num_not_in > 0: print(f"there are {num_not_in} semantic datas not in phoneme datas") if num_deleted_bigger > 0: print( f"deleted {num_deleted_bigger} audios who's duration are bigger than {self.max_sec} seconds" ) if num_deleted_ps > 0: # 4702 for LibriTTS, LirbriTTS 是标注数据, 是否需要筛？=> 需要，有值为 100 的极端值 print( f"deleted {num_deleted_ps} audios who's phoneme/sec are bigger than {self.max_ps_ratio} or smaller than {self.min_ps_ratio}" ) """ there are 31 semantic datas not in phoneme datas deleted 34 audios who's duration are bigger than 54 seconds deleted 3190 audios who's phoneme/sec are bigger than 25 or smaller than 3 dataset.__len__(): 366463 """ # 345410 for LibriTTS print("dataset.__len__():", self.__len__()) def __get_item_names__(self) -> List[str]: return self.item_names def __len__(self) -> int: return len(self.semantic_phoneme) def __getitem__(self, idx: int) -> Dict: semantic_ids, phoneme_ids = self.semantic_phoneme[idx] item_name = self.item_names[idx] phoneme_ids_len = len(phoneme_ids) # semantic tokens target semantic_ids_len = len(semantic_ids) flag = 0 path_bert = "%s/%s.pt" % (self.path3, item_name) if os.path.exists(path_bert) == True: bert_feature = torch.load(path_bert, map_location="cpu") else: flag = 1 if flag == 1: # bert_feature=torch.zeros_like(phoneme_ids,dtype=torch.float32) bert_feature = None else: assert bert_feature.shape[-1] == len(phoneme_ids) return { "idx": idx, "phoneme_ids": phoneme_ids, "phoneme_ids_len": phoneme_ids_len, "semantic_ids": semantic_ids, "semantic_ids_len": semantic_ids_len, "bert_feature": bert_feature, } def get_sample_length(self, idx: int): semantic_ids = self.semantic_phoneme[idx][0] sec = 1.0 * len(semantic_ids) / self.hz return sec def collate(self, examples: List[Dict]) -> Dict: sample_index: List[int] = [] phoneme_ids: List[torch.Tensor] = [] phoneme_ids_lens: List[int] = [] semantic_ids: List[torch.Tensor] = [] semantic_ids_lens: List[int] = [] # return for item in examples: sample_index.append(item["idx"]) phoneme_ids.append(np.array(item["phoneme_ids"], dtype=np.int64)) semantic_ids.append(np.array(item["semantic_ids"], dtype=np.int64)) phoneme_ids_lens.append(item["phoneme_ids_len"]) semantic_ids_lens.append(item["semantic_ids_len"]) # pad 0 phoneme_ids = batch_sequences(phoneme_ids) semantic_ids = batch_sequences(semantic_ids, pad_value=self.PAD) # # convert each batch to torch.tensor phoneme_ids = torch.tensor(phoneme_ids) semantic_ids = torch.tensor(semantic_ids) phoneme_ids_lens = torch.tensor(phoneme_ids_lens) semantic_ids_lens = torch.tensor(semantic_ids_lens) bert_padded = torch.FloatTensor(len(examples), 1024, max(phoneme_ids_lens)) bert_padded.zero_() for idx, item in enumerate(examples): bert = item["bert_feature"] if bert != None: bert_padded[idx, :, : bert.shape[-1]] = bert return { # List[int] "ids": sample_index, # torch.Tensor (B, max_phoneme_length) "phoneme_ids": phoneme_ids, # torch.Tensor (B) "phoneme_ids_len": phoneme_ids_lens, # torch.Tensor (B, max_semantic_ids_length) "semantic_ids": semantic_ids, # torch.Tensor (B) "semantic_ids_len": semantic_ids_lens, # torch.Tensor (B, 1024, max_phoneme_length) "bert_feature": bert_padded, } if __name__ == "__main__": root_dir = "/data/docker/liujing04/gpt-vits/prepare/dump_mix/" dataset = Text2SemanticDataset( phoneme_path=root_dir + "phoneme_train.npy", semantic_path=root_dir + "semantic_train.tsv", ) batch_size = 12 dataloader = DataLoader( dataset, batch_size=batch_size, collate_fn=dataset.collate, shuffle=False ) for i, batch in enumerate(dataloader): if i % 1000 == 0: print(i) # if i == 0: # print('batch["ids"]:', batch["ids"]) # print('batch["phoneme_ids"]:', batch["phoneme_ids"], # batch["phoneme_ids"].shape) # print('batch["phoneme_ids_len"]:', batch["phoneme_ids_len"], # batch["phoneme_ids_len"].shape) # print('batch["semantic_ids"]:', batch["semantic_ids"], # batch["semantic_ids"].shape) # print('batch["semantic_ids_len"]:', batch["semantic_ids_len"], # batch["semantic_ids_len"].shape)