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Running
on
L40S
Running
on
L40S
| import re | |
| import sys | |
| import json | |
| from torch.utils.data import Dataset | |
| import torchaudio | |
| from torchaudio.functional import resample | |
| import torch | |
| import numpy as np | |
| from torch.nn.utils.rnn import pad_sequence | |
| def check_lryics(lyric): | |
| _FILTER_STRING = [ | |
| '作词', '作曲', '编曲', '【', '策划', | |
| '录音', '混音', '母带', '：', '制作', | |
| '版权', '校对', '演奏', '制作', '伴奏' | |
| ] | |
| for item in _FILTER_STRING: | |
| if item in lyric: | |
| return True | |
| return False | |
| def process_lyrics(lines): | |
| lyric_part = [] | |
| timestamp_part = [] | |
| timestamp_pattern = re.compile(r'\[\d+:\d+(\.\d+)?\]') | |
| for i, line in enumerate(lines): | |
| # 删除前几行的特定信息 | |
| if i<10 and check_lryics(line): | |
| continue | |
| # 检查是否包含有效的时间戳和歌词内容 | |
| if timestamp_pattern.match(line): | |
| timestamp_end = line.rfind(']') | |
| lyrics = line[timestamp_end + 1:].strip() | |
| timestamps = line[:timestamp_end + 1] | |
| if '：' in lyrics: | |
| if len(lyrics.split("：")[0]) <=5: | |
| lyrics = "".join(lyrics.split("：")[1:]) | |
| # if lyrics: # 确保歌词部分不是空的 | |
| # lyric_part.append(lyrics) | |
| # timestamp_part.append(timestamps) | |
| # print(processed_lyrics) | |
| return timestamp_part, lyric_part | |
| def get_timestamps(timestamp_part): | |
| # 转换为秒 | |
| timestamps = [] | |
| for line in timestamp_part: | |
| match = re.match(r'\[(\d+):(\d+)(\.\d+)?\]', line) | |
| if match: | |
| minutes = int(match.group(1)) | |
| seconds = float(match.group(2)) | |
| millis = float(match.group(3)) if match.group(3) else 0 | |
| total_seconds = minutes * 60 + seconds + millis | |
| timestamps.append(total_seconds) | |
| return timestamps | |
| def process_lyrics_lrc(lyrics): | |
| timestamp_part, lyric_part = process_lyrics(lyrics) | |
| # print(timestamp_part) | |
| # print(lyric_part) | |
| timestamps = get_timestamps(timestamp_part) | |
| # print(timestamps) | |
| if len(timestamps) == 0: | |
| # print(f'{lyric_path}') | |
| return [] | |
| slice_start = timestamps[0] | |
| slice_start_idx = 0 | |
| output_list = [] | |
| for i in range(1, len(timestamps)): | |
| # 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 | |
| if timestamps[i] - slice_start > 30: | |
| output_list.append(f'[{str(slice_start)}:{str(timestamps[i])}]' + ", ".join(lyric_part[slice_start_idx:i])) | |
| slice_start = timestamps[i] | |
| slice_start_idx = i | |
| return output_list | |
| def process_lyrics_yrc(lyrics): | |
| timestamps, lyric_part = extract_lrc(lyrics) | |
| # timestamp_part, lyric_part = process_lyrics(lyrics) | |
| # import pdb; pdb.set_trace() | |
| # print(timestamp_part) | |
| # print(lyric_part) | |
| # timestamps = get_timestamps(timestamp_part) | |
| # print(timestamps) | |
| if len(timestamps) == 0: | |
| # print(f'{lyric_path}') | |
| return [] | |
| slice_start = timestamps[0] | |
| slice_start_idx = 0 | |
| output_list = [] | |
| for i in range(1, len(timestamps)): | |
| # 如果累积时间超过30秒，则进行切分 | |
| if timestamps[i] - slice_start > 30: | |
| output_list.append(f'[{str(slice_start)}:{str(timestamps[i])}]' + ", ".join(lyric_part[slice_start_idx:i])) | |
| slice_start = timestamps[i] | |
| slice_start_idx = i | |
| # import pdb; pdb.set_trace() | |
| return output_list | |
| def extract_lrc(lyrics): | |
| timestamp_part, lyric_part = [], [] | |
| for i, text in enumerate(lyrics): | |
| # 提取中括号内的内容 | |
| bracket_content = re.search(r'\[(.*?)\]', text).group(1) | |
| bracket_content = bracket_content.split(',') | |
| # 提取小括号内的内容 | |
| parentheses_content = re.findall(r'\((.*?)\)', text) | |
| # 提取其他内容 | |
| other_content = re.sub(r'\[(.*?)\]|\((.*?)\)', '', text).strip() | |
| # 数据怎么处理？ | |
| if i<10 and check_lryics(other_content): | |
| continue | |
| timestamp_part.append(float(bracket_content[0])/1000) | |
| lyric_part.append(other_content) | |
| return timestamp_part, lyric_part | |
| class WYYSongDataset(Dataset): | |
| def __init__(self, | |
| metadata_path:str, | |
| sr:int = 0, | |
| use_lang = ['en', 'zh-cn'], | |
| num_examples = -1, | |
| max_dur = 20, | |
| pad_to_max= True, | |
| ): | |
| self.sr = sr | |
| self.use_lang = use_lang | |
| self._load_metadata(metadata_path) | |
| self.max_dur = max_dur | |
| self.pad_to_max = pad_to_max | |
| # buffer | |
| self.lyric_buffer = {} | |
| if(num_examples<=0): | |
| self.dataset_len = len(self.data) | |
| self.random_slc = False | |
| else: | |
| self.dataset_len = num_examples | |
| self.random_slc = True | |
| # 读取jsonl文件 | |
| def _load_metadata(self, metadata_path): | |
| with open(metadata_path) as fp: | |
| lines = fp.readlines() | |
| self.data = [] | |
| for line in lines: | |
| item = json.loads(line) | |
| if '伴奏' not in item['path']: | |
| # if "lang_type" in item and item['lang_type'] == 'en': | |
| if "lang_type" in item: | |
| self.data.append(item) | |
| def __len__(self): | |
| return self.dataset_len | |
| def __getitem__(self, idx): | |
| try_cnt = 0 | |
| while True: | |
| if(self.random_slc): | |
| idx = np.random.randint(0, len(self.data)) | |
| yrc_lyrics = [] | |
| lrc_lyrics = [] | |
| try: | |
| info = self.data[idx] | |
| # audio path | |
| path = info["path"] | |
| lang_type = info["lang_type"] | |
| if info["lang_type"] == 'en': | |
| lyrics = info['lyrics'] | |
| else: | |
| lyrics = info['lyrics_phone'] | |
| # 随机选取一个lyric段落 | |
| ly_id = torch.randint(low=1, high=len(lyrics), size=(1,))[0].item() | |
| lyric = lyrics[ly_id].strip() | |
| st, et, lyric = self.parse_lyric(lyric) | |
| lyric = lyric.replace("\xa0", " ") | |
| lyric = " ".join(lyric.split()) | |
| assert et - st < self.max_dur | |
| if info["lang_type"] == 'en': | |
| # print(len(lyric.split())/(et-st)) | |
| assert 6 > len(lyric.split())/(et-st) > 1 | |
| else: | |
| # print(len(lyric.split())/(et-st)) | |
| lyric = lyric.replace("-", "") | |
| assert 6 > len(lyric.split())/(et-st) > 1 | |
| # 读取音频文件 | |
| cur_sample_rate = torchaudio.info(path).sample_rate | |
| offset = int(cur_sample_rate*st) | |
| num_frames = int(cur_sample_rate * (et -st)) | |
| chunk, _ = torchaudio.load(path, frame_offset=offset, num_frames=num_frames) | |
| # chunk = torch.zeros(1, 48000*15) | |
| # 随机选取一个channel | |
| if(chunk.shape[0]>1): | |
| chunk = chunk[torch.randint(chunk.shape[0], size=(1,)),:].float() | |
| else: | |
| chunk = chunk[[0],:].float() | |
| if(cur_sample_rate!=self.sr): | |
| # print('a:',cur_sample_rate,chunk.shape) | |
| chunk = torchaudio.functional.resample(chunk, cur_sample_rate, self.sr) | |
| if self.pad_to_max: | |
| chunk = self.pad_2d_tensor(chunk, int(self.max_dur * self.sr), 0) | |
| return chunk, lyric, et-st, path, lang_type | |
| except: | |
| # print("Error loadding ", info["path"]) | |
| try_cnt += 1 | |
| idx = np.random.randint(0, len(self.data)) | |
| if(try_cnt>20): | |
| raise FileNotFoundError() | |
| def parse_lyric(self, lyric): | |
| pattern = r'\[(\d+\.\d+):(\d+\.\d+)\](.*)' | |
| match = re.search(pattern, lyric) | |
| start_time = float(match.group(1)) | |
| end_time = float(match.group(2)) | |
| content = match.group(3) | |
| return start_time, end_time, content | |
| def pad_2d_tensor(self, x, max_len, pad_id): | |
| # 获取输入 tensor 的形状 | |
| batch_size, seq_len = x.size() | |
| max_len = max(max_len, seq_len) | |
| # 计算需要填充的长度 | |
| pad_len = max_len - seq_len | |
| # 如果需要填充 | |
| if pad_len > 0: | |
| # 创建填充 tensor | |
| pad_tensor = torch.full((batch_size, pad_len), pad_id, dtype=x.dtype, device=x.device) | |
| # 沿第二个维度（列）连接输入 tensor 和填充 tensor | |
| padded_tensor = torch.cat([x, pad_tensor], dim=1) | |
| else: | |
| # 如果不需要填充，直接返回输入 tensor | |
| padded_tensor = x | |
| return padded_tensor | |
| def collect_data(data_list): | |
| audios = pad_sequence([data[0].t() for data in data_list], batch_first=True, padding_value=0).transpose(1,2) | |
| lyrics = [data[1] for data in data_list] | |
| st_et = [data[2] for data in data_list] | |
| paths = [data[3] for data in data_list] | |
| lang_types = [data[4] for data in data_list] | |
| return audios, lyrics, st_et, lang_types | |
| # return audios, lyrics, st_et | |
| def build_dataset(): | |
| train_dataset = WYYSongDataset( | |
| metadata_path = "train.jsonl", | |
| sr = 48000, | |
| use_lang = ['zh-cn', 'en'], | |
| num_examples = 10*10000 | |
| ) | |
| valid_dataset = WYYSongDataset( | |
| metadata_path = "valid.jsonl", | |
| sr = 48000, | |
| use_lang = ['zh-cn', 'en'], | |
| num_examples = 500 | |
| ) | |
| return train_dataset, valid_dataset | |