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181 lines
5.9 KiB
181 lines
5.9 KiB
#!/usr/bin/env python3 |
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# -*- coding: utf-8 -*- |
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# |
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# Copyright 2016-2099 Ailemon.net |
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# |
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# This file is part of ASRT Speech Recognition Tool. |
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# |
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# ASRT is free software: you can redistribute it and/or modify |
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# it under the terms of the GNU General Public License as published by |
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# the Free Software Foundation, either version 3 of the License, or |
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# (at your option) any later version. |
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# ASRT is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU General Public License for more details. |
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# |
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# You should have received a copy of the GNU General Public License |
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# along with ASRT. If not, see <https://www.gnu.org/licenses/>. |
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# ============================================================================ |
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""" |
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@author: nl8590687 |
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一些常用操作函数的定义 |
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""" |
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import wave |
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import difflib |
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import matplotlib.pyplot as plt |
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import numpy as np |
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def read_wav_data(filename: str) -> tuple: |
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""" |
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读取一个wav文件，返回声音信号的时域谱矩阵和播放时间 |
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""" |
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wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流 |
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num_frame = wav.getnframes() # 获取帧数 |
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num_channel=wav.getnchannels() # 获取声道数 |
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framerate=wav.getframerate() # 获取帧速率 |
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num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数 |
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str_data = wav.readframes(num_frame) # 读取全部的帧 |
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wav.close() # 关闭流 |
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wave_data = np.fromstring(str_data, dtype = np.short) # 将声音文件数据转换为数组矩阵形式 |
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wave_data.shape = -1, num_channel # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵 |
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wave_data = wave_data.T # 将矩阵转置 |
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return wave_data, framerate, num_channel, num_sample_width |
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def read_wav_bytes(filename: str) -> tuple: |
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""" |
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读取一个wav文件，返回声音信号的时域谱矩阵和播放时间 |
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""" |
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wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流 |
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num_frame = wav.getnframes() # 获取帧数 |
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num_channel=wav.getnchannels() # 获取声道数 |
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framerate=wav.getframerate() # 获取帧速率 |
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num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数 |
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str_data = wav.readframes(num_frame) # 读取全部的帧 |
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wav.close() # 关闭流 |
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return str_data, framerate, num_channel, num_sample_width |
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def get_edit_distance(str1, str2) -> int: |
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""" |
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计算两个串的编辑距离，支持str和list类型 |
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""" |
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leven_cost = 0 |
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sequence_match = difflib.SequenceMatcher(None, str1, str2) |
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for tag, index_1, index_2, index_j1, index_j2 in sequence_match.get_opcodes(): |
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if tag == 'replace': |
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leven_cost += max(index_2-index_1, index_j2-index_j1) |
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elif tag == 'insert': |
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leven_cost += (index_j2-index_j1) |
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elif tag == 'delete': |
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leven_cost += (index_2-index_1) |
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return leven_cost |
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def ctc_decode_delete_tail_blank(ctc_decode_list): |
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""" |
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处理CTC解码后序列末尾余留的空白元素，删除掉 |
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""" |
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p = 0 |
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while p < len(ctc_decode_list) and ctc_decode_list[p] != -1: |
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p += 1 |
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return ctc_decode_list[0:p] |
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def visual_1D(points_list, frequency=1): |
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""" |
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可视化1D数据 |
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""" |
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# 首先创建绘图网格，1个子图 |
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fig, ax = plt.subplots(1) |
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x = np.linspace(0, len(points_list)-1, len(points_list)) / frequency |
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# 在对应对象上调用 plot() 方法 |
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ax.plot(x, points_list) |
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fig.show() |
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def visual_2D(img): |
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""" |
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可视化2D数据 |
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""" |
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plt.subplot(111) |
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plt.imshow(img) |
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plt.colorbar(cax=None, ax=None, shrink=0.5) |
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plt.show() |
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def decode_wav_bytes(samples_data: bytes, channels: int = 1, byte_width: int = 2) -> list: |
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""" |
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解码wav格式样本点字节流，得到numpy数组 |
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""" |
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numpy_type = np.short |
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if byte_width == 4: |
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numpy_type = np.int |
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elif byte_width != 2: |
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raise Exception('error: unsurpport byte width `' + str(byte_width) + '`') |
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wave_data = np.fromstring(samples_data, dtype=numpy_type) # 将声音文件数据转换为数组矩阵形式 |
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wave_data.shape = -1, channels # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵 |
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wave_data = wave_data.T # 将矩阵转置 |
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return wave_data |
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def get_symbol_dict(dict_filename): |
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""" |
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读取拼音汉字的字典文件 |
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返回读取后的字典 |
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""" |
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txt_obj = open(dict_filename, 'r', encoding='UTF-8') # 打开文件并读入 |
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txt_text = txt_obj.read() |
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txt_obj.close() |
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txt_lines = txt_text.split('\n') # 文本分割 |
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dic_symbol = {} # 初始化符号字典 |
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for i in txt_lines: |
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list_symbol = [] # 初始化符号列表 |
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if i != '': |
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txt_l=i.split('\t') |
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pinyin = txt_l[0] |
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for word in txt_l[1]: |
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list_symbol.append(word) |
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dic_symbol[pinyin] = list_symbol |
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return dic_symbol |
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def get_language_model(model_language_filename): |
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""" |
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读取语言模型的文件 |
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返回读取后的模型 |
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""" |
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txt_obj = open(model_language_filename, 'r', encoding='UTF-8') # 打开文件并读入 |
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txt_text = txt_obj.read() |
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txt_obj.close() |
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txt_lines = txt_text.split('\n') # 文本分割 |
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dic_model = {} # 初始化符号字典 |
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for i in txt_lines: |
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if i != '': |
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txt_l = i.split('\t') |
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if len(txt_l) == 1: |
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continue |
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dic_model[txt_l[0]] = txt_l[1] |
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return dic_model |
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def ctc_decode_stream(tokens): |
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i = 0 |
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while i < len(tokens): |
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while i+1 < len(tokens) and tokens[i] == tokens[i+1]: |
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i += 1 |
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if i+1 == len(tokens) and tokens[i] != -1: |
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return tokens[0], [] |
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if tokens[i] != -1: |
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return tokens[i], tokens[i+1:] |
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i += 1 |
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return -1, []
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