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import sys
import torch
from config import config
from transformers import MegatronBertModel, BertTokenizer
LOCAL_PATH = "./bert/Erlangshen-MegatronBert-1.3B-Chinese"
tokenizer = BertTokenizer.from_pretrained(LOCAL_PATH)
models = dict()
def get_bert_feature(
text,
word2ph,
device=config.bert_gen_config.device,
style_text=None,
style_weight=0.7,
):
if (
sys.platform == "darwin"
and torch.backends.mps.is_available()
and device == "cpu"
):
device = "mps"
if not device:
device = "cuda"
if device not in models.keys():
if config.webui_config.fp16_run:
models[device] = MegatronBertModel.from_pretrained(
LOCAL_PATH, torch_dtype=torch.float16
).to(device)
else:
models[device] = MegatronBertModel.from_pretrained(LOCAL_PATH).to(device)
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)
res = models[device](**inputs, output_hidden_states=True)
res = (
torch.nn.functional.normalize(
torch.cat(res["hidden_states"][-3:-2], -1)[0], dim=0
)
.float()
.cpu()
)
if style_text:
style_inputs = tokenizer(style_text, return_tensors="pt")
for i in style_inputs:
style_inputs[i] = style_inputs[i].to(device)
style_res = models[device](**style_inputs, output_hidden_states=True)
style_res = (
torch.nn.functional.normalize(
torch.cat(style_res["hidden_states"][-3:-2], -1)[0], dim=0
)
.float()
.cpu()
)
style_res_mean = style_res.mean(0)
assert len(word2ph) == len(text) + 2
word2phone = word2ph
phone_level_feature = []
for i in range(len(word2phone)):
if style_text:
repeat_feature = (
res[i].repeat(word2phone[i], 1) * (1 - style_weight)
+ style_res_mean.repeat(word2phone[i], 1) * style_weight
)
else:
repeat_feature = res[i].repeat(word2phone[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
return phone_level_feature.T
if __name__ == "__main__":
word_level_feature = torch.rand(38, 2048) # 12个词,每个词2048维特征
word2phone = [
1,
2,
1,
2,
2,
1,
2,
2,
1,
2,
2,
1,
2,
2,
2,
2,
2,
1,
1,
2,
2,
1,
2,
2,
2,
2,
1,
2,
2,
2,
2,
2,
1,
2,
2,
2,
2,
1,
]
# 计算总帧数
total_frames = sum(word2phone)
print(word_level_feature.shape)
print(word2phone)
phone_level_feature = []
for i in range(len(word2phone)):
print(word_level_feature[i].shape)
# 对每个词重复word2phone[i]次
repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
print(phone_level_feature.shape) # torch.Size([36, 2048])
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