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import sys |
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import torch |
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from transformers import AutoModelForMaskedLM, AutoTokenizer |
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from config import config |
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from .japanese import text2sep_kata |
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LOCAL_PATH = "./bert/deberta-v2-large-japanese" |
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tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH) |
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models = dict() |
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def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): |
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sep_text, _, _ = text2sep_kata(text) |
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sep_tokens = [tokenizer.tokenize(t) for t in sep_text] |
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sep_ids = [tokenizer.convert_tokens_to_ids(t) for t in sep_tokens] |
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sep_ids = [2] + [item for sublist in sep_ids for item in sublist] + [3] |
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return get_bert_feature_with_token(sep_ids, word2ph, device) |
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def get_bert_feature_with_token(tokens, word2ph, device=config.bert_gen_config.device): |
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if ( |
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sys.platform == "darwin" |
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and torch.backends.mps.is_available() |
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and device == "cpu" |
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): |
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device = "mps" |
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if not device: |
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device = "cuda" |
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if device not in models.keys(): |
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models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device) |
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with torch.no_grad(): |
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inputs = torch.tensor(tokens).to(device).unsqueeze(0) |
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token_type_ids = torch.zeros_like(inputs).to(device) |
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attention_mask = torch.ones_like(inputs).to(device) |
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inputs = { |
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"input_ids": inputs, |
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"token_type_ids": token_type_ids, |
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"attention_mask": attention_mask, |
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} |
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res = models[device](**inputs, output_hidden_states=True) |
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() |
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assert inputs["input_ids"].shape[-1] == len(word2ph) |
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word2phone = word2ph |
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phone_level_feature = [] |
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for i in range(len(word2phone)): |
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repeat_feature = res[i].repeat(word2phone[i], 1) |
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phone_level_feature.append(repeat_feature) |
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phone_level_feature = torch.cat(phone_level_feature, dim=0) |
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return phone_level_feature.T |
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