from datetime import datetime from functools import partial from multiprocessing import Pool from pathlib import Path import argparse import numpy as np from tqdm import tqdm from encoder import audio from encoder.config import librispeech_datasets, anglophone_nationalites from encoder.params_data import * _AUDIO_EXTENSIONS = ("wav", "flac", "m4a", "mp3") class DatasetLog: """ Registers metadata about the dataset in a text file. """ def __init__(self, root, name): self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w") self.sample_data = dict() start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) self.write_line("Creating dataset %s on %s" % (name, start_time)) self.write_line("-----") self._log_params() def _log_params(self): from encoder import params_data self.write_line("Parameter values:") for param_name in (p for p in dir(params_data) if not p.startswith("__")): value = getattr(params_data, param_name) self.write_line("\t%s: %s" % (param_name, value)) self.write_line("-----") def write_line(self, line): self.text_file.write("%s\n" % line) def add_sample(self, **kwargs): for param_name, value in kwargs.items(): if not param_name in self.sample_data: self.sample_data[param_name] = [] self.sample_data[param_name].append(value) def finalize(self): self.write_line("Statistics:") for param_name, values in self.sample_data.items(): self.write_line("\t%s:" % param_name) self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values))) self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values))) self.write_line("-----") end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) self.write_line("Finished on %s" % end_time) self.text_file.close() def _init_preprocess_dataset(dataset_name, datasets_root, out_dir): dataset_root = datasets_root.joinpath(dataset_name) if not dataset_root.exists(): print("Couldn\'t find %s, skipping this dataset." % dataset_root) return None, None return dataset_root, DatasetLog(out_dir, dataset_name) def _preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, skip_existing: bool): out_dir.mkdir(exist_ok=True) # Give a name to the speaker that includes its dataset speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) # Create an output directory with that name, as well as a txt file containing a # reference to each source file. speaker_out_dir = out_dir.joinpath(speaker_name) speaker_out_dir.mkdir(exist_ok=True) sources_fpath = speaker_out_dir.joinpath("_sources.txt") # There's a possibility that the preprocessing was interrupted earlier, check if # there already is a sources file. if sources_fpath.exists(): try: with sources_fpath.open("r") as sources_file: existing_fnames = {line.split(",")[0] for line in sources_file} except: existing_fnames = {} else: existing_fnames = {} # Gather all audio files for that speaker recursively sources_file = sources_fpath.open("a" if skip_existing else "w") audio_durs = [] for extension in _AUDIO_EXTENSIONS: for in_fpath in speaker_dir.glob("**/*.%s" % extension): # Check if the target output file already exists out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) out_fname = out_fname.replace(".%s" % extension, ".npy") if skip_existing and out_fname in existing_fnames: continue # Load and preprocess the waveform wav = audio.preprocess_wav(in_fpath) if len(wav) == 0: continue # Create the mel spectrogram, discard those that are too short frames = audio.wav_to_mel_spectrogram(wav) if len(frames) < partials_n_frames: continue out_fpath = speaker_out_dir.joinpath(out_fname) np.save(out_fpath, frames) sources_file.write("%s,%s\n" % (out_fname, in_fpath)) audio_durs.append(len(wav) / sampling_rate) sources_file.close() return audio_durs def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger): print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) # Process the utterances for each speaker work_fn = partial(_preprocess_speaker, datasets_root=datasets_root, out_dir=out_dir, skip_existing=skip_existing) with Pool(4) as pool: tasks = pool.imap(work_fn, speaker_dirs) for sample_durs in tqdm(tasks, dataset_name, len(speaker_dirs), unit="speakers"): for sample_dur in sample_durs: logger.add_sample(duration=sample_dur) logger.finalize() print("Done preprocessing %s.\n" % dataset_name) def preprocess_librispeechtest(datasets_root: Path, out_dir: Path, skip_existing=False): # preprocess dev dataset for dataset_name in librispeech_datasets["test"]["other"]: # Initialize the preprocessing dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) if not dataset_root: return # Preprocess all speakers speaker_dirs = list(dataset_root.glob("*")) _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir.joinpath("test"), skip_existing, logger) if __name__ == "__main__": parser = argparse.ArgumentParser( description="Preprocesses audio files from librispeech test other dataset, encodes them as mel spectrograms and " "writes them to the disk.", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("datasets_root", type=Path, help=\ "Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.") parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\ "Path to the output directory that will contain the mel spectrograms. If left out, " "defaults to /SV2TTS/encoder/") parser.add_argument("-s", "--skip_existing", action="store_true", help=\ "Whether to skip existing output files with the same name. Useful if this script was " "interrupted.") args = parser.parse_args() if not hasattr(args, "out_dir"): args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder") assert args.datasets_root.exists() args.out_dir.mkdir(exist_ok=True, parents=True) args = vars(args) preprocess_librispeechtest(**args)