text/cantonese_bert.py

import sys

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

LOCAL_PATH = "./bert/bert-large-cantonese"

tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH)

models = dict()


def get_bert_feature(
    text,
    word2ph,
    device="cpu",
    style_text=None,
    style_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 not in models.keys():
        models[device] = AutoModelForMaskedLM.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 style_text:
            style_inputs = tokenizer(style_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) == len(text) + \
        2, f"{len(word2ph)} != {len(text) + 2}, {word2ph}, {text}"
    word2phone = word2ph
    phone_level_feature = []
    for i in range(len(word2phone)):
        if style_text:
            repeat_feature = (
                res[i].repeat(word2phone[i], 1) * (1 - style_weight)
                + style_res_mean.repeat(word2phone[i], 1) * style_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


if __name__ == "__main__":
    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
    word2phone = [
        1,
        2,
        1,
        2,
        2,
        1,
        2,
        2,
        1,
        2,
        2,
        1,
        2,
        2,
        2,
        2,
        2,
        1,
        1,
        2,
        2,
        1,
        2,
        2,
        2,
        2,
        1,
        2,
        2,
        2,
        2,
        2,
        1,
        2,
        2,
        2,
        2,
        1,
    ]

    # 计算总帧数
    total_frames = sum(word2phone)
    print(word_level_feature.shape)
    print(word2phone)
    phone_level_feature = []
    for i in range(len(word2phone)):
        print(word_level_feature[i].shape)

        # 对每个词重复word2phone[i]次
        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
        phone_level_feature.append(repeat_feature)

    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    print(phone_level_feature.shape)  # torch.Size([36, 1024])