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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertModel, BertConfig, BertTokenizer
try:
from contants import config
except:
from contants import config
class CharEmbedding(nn.Module):
def __init__(self, model_dir):
super().__init__()
self.tokenizer = BertTokenizer.from_pretrained(model_dir)
self.bert_config = BertConfig.from_pretrained(model_dir)
self.hidden_size = self.bert_config.hidden_size
self.bert = BertModel(self.bert_config)
self.proj = nn.Linear(self.hidden_size, 256)
self.linear = nn.Linear(256, 3)
def text2Token(self, text):
token = self.tokenizer.tokenize(text)
txtid = self.tokenizer.convert_tokens_to_ids(token)
return txtid
def forward(self, inputs_ids, inputs_masks, tokens_type_ids):
out_seq = self.bert(input_ids=inputs_ids,
attention_mask=inputs_masks,
token_type_ids=tokens_type_ids)[0]
out_seq = self.proj(out_seq)
return out_seq
class TTSProsody(object):
def __init__(self, path, device):
self.device = device
self.char_model = CharEmbedding(path)
self.char_model.load_state_dict(
torch.load(
os.path.join(config.abs_path, config.system.data_path, config.model_config.vits_chinese_bert,
"prosody_model.pt"),
map_location=config.system.device
),
strict=False
)
self.char_model.eval()
self.char_model.to(self.device)
def get_char_embeds(self, text):
input_ids = self.char_model.text2Token(text)
input_masks = [1] * len(input_ids)
type_ids = [0] * len(input_ids)
input_ids = torch.LongTensor([input_ids]).to(self.device)
input_masks = torch.LongTensor([input_masks]).to(self.device)
type_ids = torch.LongTensor([type_ids]).to(self.device)
with torch.no_grad():
char_embeds = self.char_model(
input_ids, input_masks, type_ids).squeeze(0).cpu()
return char_embeds
def expand_for_phone(self, char_embeds, length): # length of phones for char
assert char_embeds.size(0) == len(length)
expand_vecs = list()
for vec, leng in zip(char_embeds, length):
vec = vec.expand(leng, -1)
expand_vecs.append(vec)
expand_embeds = torch.cat(expand_vecs, 0)
assert expand_embeds.size(0) == sum(length)
return expand_embeds.numpy()
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
prosody = TTSProsody('./bert/', device)
while True:
text = input("请输入文本：")
prosody.get_char_embeds(text)
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