121 lines
3.1 KiB
Python
121 lines
3.1 KiB
Python
import sys
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import torch
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from config import config
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.text_processing import bert_models
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models = dict()
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def get_bert_feature(
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text: str,
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word2ph,
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device = config.bert_gen_config.device,
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assist_text: str | None = None,
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assist_text_weight: float = 0.7,
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):
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if (
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sys.platform == "darwin"
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and torch.backends.mps.is_available()
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and device == "cpu"
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):
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device = "mps"
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if not device:
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device = "cuda"
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if device == "cuda" and not torch.cuda.is_available():
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device = "cpu"
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if device not in models.keys():
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models[device] = bert_models.load_model(Languages.ZH).to(device)
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with torch.no_grad():
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tokenizer = bert_models.load_tokenizer(Languages.ZH)
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = models[device](**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
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if assist_text:
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style_inputs = tokenizer(assist_text, return_tensors="pt")
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for i in style_inputs:
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style_inputs[i] = style_inputs[i].to(device)
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style_res = models[device](**style_inputs, output_hidden_states=True)
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style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
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style_res_mean = style_res.mean(0)
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assert len(word2ph) == len(text) + 2
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word2phone = word2ph
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phone_level_feature = []
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for i in range(len(word2phone)):
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if assist_text:
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repeat_feature = (
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res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
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+ style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
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)
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else:
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repeat_feature = res[i].repeat(word2phone[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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if __name__ == "__main__":
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word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征
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word2phone = [
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1,
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2,
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1,
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2,
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2,
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1,
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2,
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2,
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1,
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2,
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2,
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1,
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2,
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2,
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2,
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2,
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2,
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1,
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1,
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2,
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2,
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1,
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2,
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2,
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2,
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2,
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1,
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2,
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2,
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2,
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2,
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2,
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1,
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2,
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2,
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2,
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2,
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1,
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]
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# 计算总帧数
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total_frames = sum(word2phone)
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print(word_level_feature.shape)
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print(word2phone)
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phone_level_feature = []
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for i in range(len(word2phone)):
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print(word_level_feature[i].shape)
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# 对每个词重复word2phone[i]次
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repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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print(phone_level_feature.shape) # torch.Size([36, 1024])
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