| import torch | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| bert_models = None | |
| tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext-large") | |
| def get_bert_feature(text, word2ph, device, language): | |
| global bert_models | |
| if language != "zh": | |
| return torch.zeros(1024, sum(word2ph)) | |
| if bert_models == None: | |
| bert_models = AutoModelForMaskedLM.from_pretrained( | |
| "hfl/chinese-roberta-wwm-ext-large" | |
| ).to(device) | |
| print('loaded bert model at rank', device) | |
| with torch.no_grad(): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| for i in inputs: | |
| inputs[i] = inputs[i].to(device) | |
| res = bert_models(**inputs, output_hidden_states=True) | |
| res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] | |
| assert len(word2ph) == len(text) | |
| phone_level_feature = [] | |
| for i in range(len(word2ph)): | |
| repeat_feature = res[i].repeat(word2ph[i], 1) | |
| phone_level_feature.append(repeat_feature) | |
| phone_level_feature = torch.cat(phone_level_feature, dim=0) | |
| return phone_level_feature.T | |