8609449b63
* auto download missing model * support openi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix wrong delete * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * pass pre-commit * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix repeat login --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
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 text.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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# for i in inputs:
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# inputs[i] = inputs[i].to(device)
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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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