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| # Copyright (c) 2024 Alibaba Inc | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from torch.utils.data import DataLoader | |
| from inspiremusic.dataset.dataset import Dataset | |
| import numpy as np | |
| import librosa | |
| def audio_process_dataset_and_dataloader(args, configs): | |
| input_dataset = Dataset(args.input_data, data_pipeline=configs['data_pipeline'], mode='processing', shuffle=True, partition=True) | |
| # do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts | |
| input_data_loader = DataLoader(input_dataset, | |
| batch_size=None, | |
| pin_memory=args.pin_memory, | |
| num_workers=args.num_workers, | |
| prefetch_factor=args.prefetch) | |
| return input_dataset, input_data_loader | |
| def is_silent(wav_path, threshold=0.01, frame_length=2048, hop_length=512): | |
| y, sr = librosa.load(wav_path, sr=None) | |
| rms = librosa.feature.rms(y=y, frame_length=frame_length, hop_length=hop_length)[0] | |
| silent_frames = np.sum(rms < threshold) / len(rms) | |
| silence_fraction_threshold = 0.95 | |
| return silent_frames >= silence_fraction_threshold | |
| def rich_captions(text=None, tags=None, lyrics=None, chorus="verse", start_time=0.0, end_time=30.0): | |
| if text is None and tags is None and lyrics is None: | |
| return None | |
| else: | |
| if start_time is None: | |
| start_time = 0.0 | |
| if end_time is None: | |
| end_time = 30.0 | |
| if chorus is None: | |
| chorus = "verse" | |
| captions = f"<|{start_time:.1f}|><|{chorus}|>" | |
| if tags is not None: | |
| captions += f"<|{tags}|>" | |
| if text is not None: | |
| captions += f"<|{text}|>" | |
| if lyrics is not None: | |
| captions += f"<|lyrics|><|{lyrics}|>" | |
| captions += f"<|{end_time:.1f}|>" | |
| return captions | |
| def process_tags(infile, outfile, timefile = None): | |
| key_list = [] | |
| with open(infile, "r") as f: | |
| for line in f: | |
| sec = line.strip() | |
| key_list.append(sec) | |
| f.close() | |
| if timefile is None: | |
| with open(outfile, 'w') as f: | |
| for k in key_list: | |
| parts = k.rsplit('_', 1) | |
| text = parts[0].replace('_', ' ') + ', ' + parts[1] | |
| caption = rich_captions(text, None, None) | |
| if caption is not None: | |
| f.write("%s\t%s\n" %(k, caption)) | |
| f.close() | |
| else: | |
| times = {} | |
| with open(timefile, "r") as f: | |
| for line in f: | |
| sec = line.strip().split("\t") | |
| if len(sec) == 2 : | |
| times[sec[0]] = sec[1] | |
| f.close() | |
| with open(outfile, 'w') as f: | |
| for k in key_list: | |
| parts = k.rsplit('_', 1) | |
| text = parts[0].replace('_', ' ') + ', ' + parts[1] | |
| if k in times.keys(): | |
| caption = rich_captions(text, None, None, "verse", 0.0, float(times[k])) | |
| if caption is not None: | |
| f.write("%s\t%s\n" %(k, caption)) | |
| f.close() | |
| def process_trans(infile, outfile): | |
| trans = {} | |
| with open(infile, "r") as f: | |
| for line in f: | |
| sec = line.strip().split("\t") | |
| if len(sec) == 2: | |
| trans[sec[0]] = sec[1] | |
| else: | |
| print(line) | |
| f.close() | |
| with open(outfile, 'w') as f: | |
| for k, v in trans.items(): | |
| f.write("%s\t%s\n" %(k, rich_captions(v))) | |
| f.close() |