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| import torch | |
| import sys | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| device = torch.device( | |
| "cuda" | |
| if torch.cuda.is_available() | |
| else ( | |
| "mps" | |
| if sys.platform == "darwin" and torch.backends.mps.is_available() | |
| else "cpu" | |
| ) | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large") | |
| model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device) | |
| def get_bert_feature(text, word2ph): | |
| with torch.no_grad(): | |
| inputs = tokenizer(text, return_tensors='pt') | |
| for i in inputs: | |
| inputs[i] = inputs[i].to(device) | |
| res = model(**inputs, output_hidden_states=True) | |
| res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() | |
| assert len(word2ph) == len(text)+2 | |
| word2phone = word2ph | |
| phone_level_feature = [] | |
| for i in range(len(word2phone)): | |
| 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__': | |
| # feature = get_bert_feature('你好,我是说的道理。') | |
| import torch | |
| word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024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, 1024]) | |