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https://github.com/RVC-Boss/GPT-SoVITS.git
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54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
import os
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import torch
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from transformers import AutoTokenizer, AutoModel
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from scipy.spatial.distance import cosine
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import math
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bert_path = os.environ.get(
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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)
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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model = AutoModel.from_pretrained(bert_path)
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def calculate_similarity(text1, text2, max_length=512):
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# 预处理文本，设置最大长度
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inputs1 = tokenizer(text1, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
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inputs2 = tokenizer(text2, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
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# 获取句子向量（这里是取CLS token的向量并展平为一维）
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with torch.no_grad():
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encoded_text1 = model(**inputs1)[0][:, 0, :].flatten()
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encoded_text2 = model(**inputs2)[0][:, 0, :].flatten()
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# 确保转换为numpy数组并且是一维的
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similarity = 1 - cosine(encoded_text1.cpu().numpy().flatten(), encoded_text2.cpu().numpy().flatten())
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return similarity
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# 对boundary到1区间的值进行放大
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def adjusted_similarity(similarity_score2, boundary=0.8):
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if similarity_score2 < boundary:
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return 0
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# 倍数
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multiple = 1 / (1 - boundary)
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adjusted_score = (similarity_score2 - boundary) * multiple
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return adjusted_score
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def calculate_result(t1, t2, boundary):
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# 计算并打印相似度
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similarity_score2 = calculate_similarity(t1, t2)
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# 调整相似度
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adjusted_similarity_score2 = adjusted_similarity(similarity_score2, boundary)
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return similarity_score2, adjusted_similarity_score2
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