314 lines
11 KiB
Python
314 lines
11 KiB
Python
import re
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import sys
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import json
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from torch.utils.data import Dataset
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import torchaudio
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from torchaudio.functional import resample
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import torch
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import numpy as np
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from torch.nn.utils.rnn import pad_sequence
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def check_lryics(lyric):
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_FILTER_STRING = [
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'作词', '作曲', '编曲', '【', '策划',
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'录音', '混音', '母带', '：', '制作',
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'版权', '校对', '演奏', '制作', '伴奏'
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]
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for item in _FILTER_STRING:
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if item in lyric:
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return True
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return False
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def process_lyrics(lines):
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lyric_part = []
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timestamp_part = []
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timestamp_pattern = re.compile(r'\[\d+:\d+(\.\d+)?\]')
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for i, line in enumerate(lines):
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# 删除前几行的特定信息
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if i<10 and check_lryics(line):
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continue
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# 检查是否包含有效的时间戳和歌词内容
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if timestamp_pattern.match(line):
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timestamp_end = line.rfind(']')
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lyrics = line[timestamp_end + 1:].strip()
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timestamps = line[:timestamp_end + 1]
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if '：' in lyrics:
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if len(lyrics.split("：")[0]) <=5:
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lyrics = "".join(lyrics.split("：")[1:])
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# if lyrics: # 确保歌词部分不是空的
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# lyric_part.append(lyrics)
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# timestamp_part.append(timestamps)
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# print(processed_lyrics)
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return timestamp_part, lyric_part
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def get_timestamps(timestamp_part):
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# 转换为秒
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timestamps = []
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for line in timestamp_part:
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match = re.match(r'\[(\d+):(\d+)(\.\d+)?\]', line)
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if match:
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minutes = int(match.group(1))
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seconds = float(match.group(2))
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millis = float(match.group(3)) if match.group(3) else 0
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total_seconds = minutes * 60 + seconds + millis
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timestamps.append(total_seconds)
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return timestamps
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def process_lyrics_lrc(lyrics):
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timestamp_part, lyric_part = process_lyrics(lyrics)
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# print(timestamp_part)
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# print(lyric_part)
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timestamps = get_timestamps(timestamp_part)
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# print(timestamps)
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if len(timestamps) == 0:
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# print(f'{lyric_path}')
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return []
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slice_start = timestamps[0]
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slice_start_idx = 0
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output_list = []
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for i in range(1, len(timestamps)):
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# 如果累积时间超过30秒，则进行切分, 如果整体小于30s, 整句会被丢掉
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if timestamps[i] - slice_start > 30:
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output_list.append(f'[{str(slice_start)}:{str(timestamps[i])}]' + ", ".join(lyric_part[slice_start_idx:i]))
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slice_start = timestamps[i]
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slice_start_idx = i
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return output_list
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def process_lyrics_yrc(lyrics):
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timestamps, lyric_part = extract_lrc(lyrics)
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# timestamp_part, lyric_part = process_lyrics(lyrics)
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# import pdb; pdb.set_trace()
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# print(timestamp_part)
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# print(lyric_part)
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# timestamps = get_timestamps(timestamp_part)
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# print(timestamps)
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if len(timestamps) == 0:
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# print(f'{lyric_path}')
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return []
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slice_start = timestamps[0]
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slice_start_idx = 0
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output_list = []
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for i in range(1, len(timestamps)):
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# 如果累积时间超过30秒，则进行切分
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if timestamps[i] - slice_start > 30:
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output_list.append(f'[{str(slice_start)}:{str(timestamps[i])}]' + ", ".join(lyric_part[slice_start_idx:i]))
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slice_start = timestamps[i]
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slice_start_idx = i
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# import pdb; pdb.set_trace()
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return output_list
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def extract_lrc(lyrics):
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timestamp_part, lyric_part = [], []
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for i, text in enumerate(lyrics):
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# 提取中括号内的内容
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bracket_content = re.search(r'\[(.*?)\]', text).group(1)
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bracket_content = bracket_content.split(',')
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# 提取小括号内的内容
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parentheses_content = re.findall(r'\((.*?)\)', text)
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# 提取其他内容
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other_content = re.sub(r'\[(.*?)\]|\((.*?)\)', '', text).strip()
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# 数据怎么处理？
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# import pdb; pdb.set_trace()
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if i<10 and check_lryics(other_content):
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continue
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# import pdb; pdb.set_trace()
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timestamp_part.append(float(bracket_content[0])/1000)
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lyric_part.append(other_content)
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# import pdb; pdb.set_trace()
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return timestamp_part, lyric_part
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class WYYSongDataset(Dataset):
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def __init__(self,
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metadata_path:str,
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sr:int = 0,
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use_lang = ['en', 'zh-cn'],
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num_examples = -1,
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):
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self.sr = sr
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self.use_lang = use_lang
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self._load_metadata(metadata_path)
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# buffer
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self.lyric_buffer = {}
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if(num_examples<=0):
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self.dataset_len = len(self.data)
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self.random_slc = False
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else:
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self.dataset_len = num_examples
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self.random_slc = True
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# 读取jsonl文件
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def _load_metadata(self, metadata_path):
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with open(metadata_path) as fp:
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lines = fp.readlines()
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self.data = []
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for line in lines:
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item = json.loads(line)
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# if item['lrc-lyric'] is not None and item['yrc-lyric'] is not None:
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if 'lyrics' in item and 'lang_info' in item:
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if len(item['lyrics']) > 0:
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for lang in self.use_lang:
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if lang in item['lang_info'] and item['lang_info'][lang]['proportion'] > 0.8 and item['lang_info'][lang]['probability'] > 0.9:
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# if '伴奏' not in item['path'] and "cloud" in item['path']:
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if '伴奏' not in item['path']:
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self.data.append(item)
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def __len__(self):
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return self.dataset_len
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def __getitem__(self, idx):
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try_cnt = 0
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while True:
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if(self.random_slc):
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idx = np.random.randint(0, len(self.data))
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yrc_lyrics = []
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lrc_lyrics = []
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try:
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info = self.data[idx]
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# audio path
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path:str = info["path"]
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# 读取歌词段落
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if 'lyrics' not in info:
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if idx not in self.lyric_buffer:
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# 字级别align的歌词
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if info['yrc-lyric'] is not None:
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with open(info['yrc-lyric']) as f_in:
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yrc_lyric = json.load(f_in)
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yrc_lyrics = process_lyrics_yrc(yrc_lyric['lyrics'][:-1])
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# 句子级align的歌词
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if info['lrc-lyric'] is not None:
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with open(info['lrc-lyric']) as f_in:
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lrc_lyric = json.load(f_in)
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lrc_lyrics = process_lyrics_lrc(lrc_lyric['lyrics'][:-1])
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# 优先使用字级别align的歌词
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if len(yrc_lyrics) > 0:
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lyrics = yrc_lyrics
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else:
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lyrics = lrc_lyrics
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self.lyric_buffer[idx] = lyrics
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# TODO 每段歌词进行长度筛选，过滤掉太长和太短的歌曲
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else:
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lyrics = self.lyric_buffer[idx]
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else:
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lyrics = info['lyrics']
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# 随机选取一个lyric段落
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ly_id = torch.randint(low=1, high=len(lyrics), size=(1,))[0].item()
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# ly_id = 0
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lyric = lyrics[ly_id]
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st, et, lyric = self.parse_lyric(lyric)
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assert et - st < 40
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# 文本过滤
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lyric = re.sub(r'【.*?】', '', lyric)
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if 'zh-cn' in info['lang_info'] and info['lang_info']['zh-cn']['proportion'] > 0.8:
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assert 200 > len(lyric.replace(" ", "")) > 30
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if '：' in lyrics:
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if len(lyrics.split("：")[0]) <=5:
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lyrics = "".join(lyrics.split("：")[1:])
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if ':' in lyrics:
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if len(lyrics.split("：")[0]) <=5:
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lyrics = "".join(lyrics.split(":")[1:])
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if 'en' in info['lang_info'] and info['lang_info']['en']['proportion'] > 0.8:
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assert 200 > len(lyric.split()) > 20
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if '：' in lyrics:
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if len(lyrics.split("：")[0].split()) <=3:
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lyrics = "".join(lyrics.split("：")[1:])
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if ':' in lyrics:
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if len(lyrics.split("：")[0].split()) <=3:
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lyrics = "".join(lyrics.split(":")[1:])
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# 读取音频文件
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cur_sample_rate = torchaudio.info(path).sample_rate
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offset = int(cur_sample_rate*st)
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num_frames = int(cur_sample_rate * (et -st))
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chunk, _ = torchaudio.load(path, frame_offset=offset, num_frames=num_frames)
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# 随机选取一个channel
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if(chunk.shape[0]>1):
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chunk = chunk[torch.randint(chunk.shape[0], size=(1,)),:].float()
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else:
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chunk = chunk[[0],:].float()
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if(cur_sample_rate!=self.sr):
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# print('a:',cur_sample_rate,chunk.shape)
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chunk = torchaudio.functional.resample(chunk, cur_sample_rate, self.sr)
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return chunk, lyric, [st, et], path
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except:
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print("Error loadding ", info["path"])
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try_cnt += 1
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idx = np.random.randint(0, len(self.data))
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if(try_cnt>10):
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raise FileNotFoundError()
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def parse_lyric(self, lyric):
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pattern = r'\[(\d+\.\d+):(\d+\.\d+)\](.*)'
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match = re.search(pattern, lyric)
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start_time = float(match.group(1))
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end_time = float(match.group(2))
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content = match.group(3)
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return start_time, end_time, content
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def collect_song(data_list):
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audios = pad_sequence([data[0].t() for data in data_list], batch_first=True, padding_value=0).transpose(1,2)
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lyrics = [data[1] for data in data_list]
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st_et = [data[2] for data in data_list]
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paths = [data[3] for data in data_list]
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return audios, lyrics, st_et
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