4f2ae91ce2
* Prevent repeatedly loading the BERT model from the disk. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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import sys
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tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")
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models = dict()
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def get_bert_feature(text, word2ph, device=None):
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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(
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"./bert/bert-base-japanese-v3"
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).to(device)
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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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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