56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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import os
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os.environ['ALL_PROXY']='socks5://127.0.0.1:7890'
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os.environ['HTTPS_PROXY']='http://127.0.0.1:7890'
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os.environ['HTTP_PROXY']='http://127.0.0.1:7890'
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large")
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model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-roberta-wwm-ext-large").to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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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 = model(**inputs, output_hidden_states=True)
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res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
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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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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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# feature = get_bert_feature('你好,我是说的道理。')
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import torch
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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 = [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]
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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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