import torch from transformers import AutoTokenizer, AutoModelForMaskedLM import sys models = {} tokenizers = {} def get_bert_feature(text, word2ph, device=None, model_id='tohoku-nlp/bert-base-japanese-v3'): global model global tokenizer if ( sys.platform == "darwin" and torch.backends.mps.is_available() and device == "cpu" ): device = "mps" if not device: device = "cuda" if model_id not in models: model = AutoModelForMaskedLM.from_pretrained(model_id).to( device ) models[model_id] = model tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizers[model_id] = tokenizer else: model = models[model_id] tokenizer = tokenizers[model_id] with torch.no_grad(): inputs = tokenizer(text, return_tensors="pt") tokenized = tokenizer.tokenize(text) 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), f"{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