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import sys
import torch
from transformers import DebertaV2Model, DebertaV2Tokenizer
from config import config
LOCAL_PATH = "./bert/deberta-v3-large"
tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH)
models = dict()
def get_bert_feature(
text,
word2ph,
device=config.bert_gen_config.device,
assist_text=None,
assist_text_weight=0.7,
):
if (
sys.platform == "darwin"
and torch.backends.mps.is_available()
and device == "cpu"
):
device = "mps"
if not device:
device = "cuda"
if device == "cuda" and not torch.cuda.is_available():
device = "cpu"
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()
if assist_text:
style_inputs = tokenizer(assist_text, return_tensors="pt")
for i in style_inputs:
style_inputs[i] = style_inputs[i].to(device)
style_res = models[device](**style_inputs, output_hidden_states=True)
style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
style_res_mean = style_res.mean(0)
assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
word2phone = word2ph
phone_level_feature = []
for i in range(len(word2phone)):
if assist_text:
repeat_feature = (
res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+ style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
)
else:
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
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