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https://github.com/RVC-Boss/GPT-SoVITS.git
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145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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通用字符映射工具
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用于在文本标准化后，建立原始文本到标准化文本的字符级映射
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"""
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def build_char_mapping(original_text, normalized_text):
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"""
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通过字符对齐算法，构建原始文本到标准化文本的映射
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Args:
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original_text: 原始文本
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normalized_text: 标准化后的文本
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Returns:
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dict: {
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"orig_to_norm": list[int], 原始文本每个字符对应标准化文本的位置
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"norm_to_orig": list[int], 标准化文本每个字符对应原始文本的位置
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}
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"""
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# 使用动态规划找到最优对齐
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m, n = len(original_text), len(normalized_text)
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# dp[i][j] 表示 original_text[:i] 和 normalized_text[:j] 的对齐代价
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# 0 = 匹配, 1 = 替换, 插入, 删除
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dp = [[float('inf')] * (n + 1) for _ in range(m + 1)]
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# 记录路径
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path = [[None] * (n + 1) for _ in range(m + 1)]
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# 初始化
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for i in range(m + 1):
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dp[i][0] = i
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if i > 0:
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path[i][0] = ('del', i-1, -1)
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for j in range(n + 1):
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dp[0][j] = j
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if j > 0:
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path[0][j] = ('ins', -1, j-1)
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# 动态规划
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for i in range(1, m + 1):
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for j in range(1, n + 1):
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orig_char = original_text[i-1]
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norm_char = normalized_text[j-1]
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# 匹配代价（相同字符代价为0，不同字符代价为1）
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match_cost = 0 if orig_char == norm_char else 1
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# 三种操作的代价
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costs = [
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(dp[i-1][j-1] + match_cost, 'match' if match_cost == 0 else 'replace', i-1, j-1),
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(dp[i-1][j] + 1, 'del', i-1, j),
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(dp[i][j-1] + 1, 'ins', i, j-1),
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]
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min_cost, op, pi, pj = min(costs, key=lambda x: x[0])
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dp[i][j] = min_cost
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path[i][j] = (op, pi, pj)
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# 回溯路径，构建映射
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orig_to_norm = [-1] * len(original_text)
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norm_to_orig = [-1] * len(normalized_text)
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i, j = m, n
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alignments = []
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while i > 0 or j > 0:
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if path[i][j] is None:
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break
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op, pi, pj = path[i][j]
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if op in ['match', 'replace']:
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# 原始字符i-1 对应 标准化字符j-1
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alignments.append((i-1, j-1))
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i, j = pi, pj
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elif op == 'del':
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# 原始字符i-1 被删除，映射到当前标准化位置（如果存在）
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if j > 0:
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alignments.append((i-1, j-1))
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i = pi
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elif op == 'ins':
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# 标准化字符j-1 是插入的，没有对应的原始字符
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j = pj
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# 根据对齐结果建立映射
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for orig_idx, norm_idx in alignments:
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if orig_idx >= 0 and orig_idx < len(original_text):
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if orig_to_norm[orig_idx] == -1:
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orig_to_norm[orig_idx] = norm_idx
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if norm_idx >= 0 and norm_idx < len(normalized_text):
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if norm_to_orig[norm_idx] == -1:
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norm_to_orig[norm_idx] = orig_idx
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return {
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"orig_to_norm": orig_to_norm,
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"norm_to_orig": norm_to_orig
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}
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def test_char_mapping():
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"""测试字符映射功能"""
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test_cases = [
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("50元", "五十元"),
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("3.5度", "三点五度"),
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("GPT-4", "GPT minus four"), # 也可以测试英文
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]
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for orig, norm in test_cases:
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print(f"原始: '{orig}'")
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print(f"标准化: '{norm}'")
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mappings = build_char_mapping(orig, norm)
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orig_to_norm = mappings["orig_to_norm"]
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norm_to_orig = mappings["norm_to_orig"]
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print(f"原始→标准化映射:")
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for i, c in enumerate(orig):
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norm_idx = orig_to_norm[i]
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if norm_idx >= 0 and norm_idx < len(norm):
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print(f" [{i}]'{c}' → [{norm_idx}]'{norm[norm_idx]}'")
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else:
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print(f" [{i}]'{c}' → 无映射")
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print(f"标准化→原始映射:")
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for i, c in enumerate(norm):
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orig_idx = norm_to_orig[i]
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if orig_idx >= 0 and orig_idx < len(orig):
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print(f" [{i}]'{c}' ← [{orig_idx}]'{orig[orig_idx]}'")
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else:
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print(f" [{i}]'{c}' ← 无对应")
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print()
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if __name__ == "__main__":
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test_char_mapping()
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