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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ This code is modified from https://github.com/facebookresearch/libri-light/blob/main/data_preparation/cut_by_vad.py""" | |
| import pathlib | |
| import soundfile as sf | |
| import numpy as np | |
| import json | |
| import multiprocessing | |
| import tqdm | |
| def save(seq, fname, index, extension): | |
| """save audio sequences to file""" | |
| output = np.hstack(seq) | |
| file_name = fname.parent / (fname.stem + f"_{index:04}{extension}") | |
| fname.parent.mkdir(exist_ok=True, parents=True) | |
| sf.write(file_name, output, samplerate=16000) | |
| def cut_sequence(path, vad, path_out, target_len_sec, out_extension): | |
| """cut audio sequences based on VAD""" | |
| data, samplerate = sf.read(path) | |
| assert len(data.shape) == 1 | |
| assert samplerate == 16000 | |
| to_stitch = [] | |
| length_accumulated = 0.0 | |
| i = 0 | |
| # Iterate over VAD segments | |
| for start, end in vad: | |
| start_index = int(start * samplerate) | |
| end_index = int(end * samplerate) | |
| slice = data[start_index:end_index] | |
| # Save slices that exceed the target length or if there's already accumulated audio | |
| if ( | |
| length_accumulated + (end - start) > target_len_sec | |
| and length_accumulated > 0 | |
| ): | |
| save(to_stitch, path_out, i, out_extension) | |
| to_stitch = [] | |
| i += 1 | |
| length_accumulated = 0 | |
| # Add the current slice to the list to be stitched | |
| to_stitch.append(slice) | |
| length_accumulated += end - start | |
| # Save any remaining slices | |
| if to_stitch: | |
| save(to_stitch, path_out, i, out_extension) | |
| def cut_book(task): | |
| """process each book in the dataset""" | |
| path_book, root_out, target_len_sec, extension = task | |
| speaker = pathlib.Path(path_book.parent.name) | |
| for i, meta_file_path in enumerate(path_book.glob("*.json")): | |
| with open(meta_file_path, "r") as f: | |
| meta = json.loads(f.read()) | |
| book_id = meta["book_meta"]["id"] | |
| vad = meta["voice_activity"] | |
| sound_file = meta_file_path.parent / (meta_file_path.stem + ".flac") | |
| path_out = root_out / speaker / book_id / (meta_file_path.stem) | |
| cut_sequence(sound_file, vad, path_out, target_len_sec, extension) | |
| def cut_segments( | |
| input_dir, output_dir, target_len_sec=30, n_process=32, out_extension=".wav" | |
| ): | |
| """Main function to cut segments from audio files""" | |
| pathlib.Path(output_dir).mkdir(exist_ok=True, parents=True) | |
| list_dir = pathlib.Path(input_dir).glob("*/*") | |
| list_dir = [x for x in list_dir if x.is_dir()] | |
| print(f"{len(list_dir)} directories detected") | |
| print(f"Launching {n_process} processes") | |
| # Create tasks for multiprocessing | |
| tasks = [ | |
| (path_book, output_dir, target_len_sec, out_extension) for path_book in list_dir | |
| ] | |
| # Process tasks in parallel using multiprocessing | |
| with multiprocessing.Pool(processes=n_process) as pool: | |
| for _ in tqdm.tqdm(pool.imap_unordered(cut_book, tasks), total=len(tasks)): | |
| pass | |
| if __name__ == "__main__": | |
| input_dir = "/path/to/input_dir" | |
| output_dir = "/path/to/output_dir" | |
| target_len_sec = 10 | |
| n_process = 16 | |
| cut_segments(input_dir, output_dir, target_len_sec, n_process) | |