from datetime import datetime from functools import partial from multiprocessing import Pool from pathlib import Path 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) -> (Path, DatasetLog): 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): # 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_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False): for dataset_name in librispeech_datasets["train"]["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, skip_existing, logger) def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False): # Initialize the preprocessing dataset_name = "VoxCeleb1" dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) if not dataset_root: return # Get the contents of the meta file with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile: metadata = [line.split("\t") for line in metafile][1:] # Select the ID and the nationality, filter out non-anglophone speakers nationalities = {line[0]: line[3] for line in metadata} keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if nationality.lower() in anglophone_nationalites] print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % (len(keep_speaker_ids), len(nationalities))) # Get the speaker directories for anglophone speakers only speaker_dirs = dataset_root.joinpath("wav").glob("*") speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if speaker_dir.name in keep_speaker_ids] print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs))) # Preprocess all speakers _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger) def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False): # Initialize the preprocessing dataset_name = "VoxCeleb2" dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) if not dataset_root: return # Get the speaker directories # Preprocess all speakers speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*")) _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)