Spaces:
Runtime error
Runtime error
| import sys | |
| import torch | |
| from transformers import DebertaV2Model, DebertaV2Tokenizer | |
| from config import config | |
| LOCAL_PATH = "./bert/deberta-v3-large" | |
| #LOCAL_PATH = 'microsoft/deberta-v2-xlarge' | |
| tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH) | |
| models = dict() | |
| def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): | |
| if ( | |
| sys.platform == "darwin" | |
| and torch.backends.mps.is_available() | |
| and device == "cpu" | |
| ): | |
| device = "mps" | |
| if not device: | |
| device = "cuda" | |
| if device not in models.keys(): | |
| models[device] = DebertaV2Model.from_pretrained(LOCAL_PATH).to(device) | |
| with torch.no_grad(): | |
| inputs = tokenizer(text, return_tensors="pt") | |
| for i in inputs: | |
| inputs[i] = inputs[i].to(device) | |
| res = models[device](**inputs, output_hidden_states=True) | |
| res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() | |
| assert len(word2ph) == res.shape[0], (text, res.shape[0], 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 | |