| import torch |
| from transformers import AutoTokenizer, AutoModelForMaskedLM |
| import sys |
|
|
| model_id = 'bert-base-uncased' |
| 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 |
|
|
