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import torch |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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import sys |
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models = {} |
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tokenizers = {} |
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def get_bert_feature(text, word2ph, device=None, model_id='cl-tohoku/bert-base-japanese-v3'): |
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global model |
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global tokenizer |
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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 model_id not in models: |
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model = AutoModelForMaskedLM.from_pretrained(model_id).to( |
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device |
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) |
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models[model_id] = model |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizers[model_id] = tokenizer |
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else: |
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model = models[model_id] |
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tokenizer = tokenizers[model_id] |
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with torch.no_grad(): |
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inputs = tokenizer(text, return_tensors="pt") |
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tokenized = tokenizer.tokenize(text) |
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for i in inputs: |
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inputs[i] = inputs[i].to(device) |
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res = model(**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), f"{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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