| import torch | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| import sys | |
| model_id = 'dbmdz/bert-base-french-europeana-cased' | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = None | |
| def get_bert_feature(text, word2ph, device=None): | |
| global model | |
| if ( | |
| sys.platform == "darwin" | |
| and torch.backends.mps.is_available() | |
| and device == "cpu" | |
| ): | |
| device = "mps" | |
| if not device: | |
| device = "cuda" | |
| if model is None: | |
| model = AutoModelForMaskedLM.from_pretrained(model_id).to( | |
| device | |
| ) | |
| 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 inputs["input_ids"].shape[-1] == len(word2ph) | |
| 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 | |