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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/data/dataset.py
# reference: https://github.com/lifeiteng/vall-e
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)