from multiprocess.pool import ThreadPool from speaker_encoder.params_data import * from speaker_encoder.config import librispeech_datasets, anglophone_nationalites from datetime import datetime from speaker_encoder import audio from pathlib import Path from tqdm import tqdm import numpy as np 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 speaker_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_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension, skip_existing, logger): print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) # Function to preprocess utterances for one speaker def preprocess_speaker(speaker_dir: Path): # 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") 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) logger.add_sample(duration=len(wav) / sampling_rate) sources_file.write("%s,%s\n" % (out_fname, in_fpath)) sources_file.close() # Process the utterances for each speaker with ThreadPool(8) as pool: list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), unit="speakers")) logger.finalize() print("Done preprocessing %s.\n" % dataset_name) # Function to preprocess utterances for one speaker def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, 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 = {} existing_fnames = {} # Gather all audio files for that speaker recursively sources_file = sources_fpath.open("a" if skip_existing else "w") 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) # logger.add_sample(duration=len(wav) / sampling_rate) sources_file.write("%s,%s\n" % (out_fname, in_fpath)) sources_file.close() return len(wav) def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension, skip_existing, logger): # from multiprocessing import Pool, cpu_count from pathos.multiprocessing import ProcessingPool as Pool # Function to preprocess utterances for one speaker def __preprocess_speaker(speaker_dir: Path): # 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") existing_fnames = {} # Gather all audio files for that speaker recursively sources_file = sources_fpath.open("a" if skip_existing else "w") wav_lens = [] 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) # logger.add_sample(duration=len(wav) / sampling_rate) sources_file.write("%s,%s\n" % (out_fname, in_fpath)) wav_lens.append(len(wav)) sources_file.close() return wav_lens print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) # Process the utterances for each speaker # with ThreadPool(8) as pool: # list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), # unit="speakers")) pool = Pool(processes=20) for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1): for wav_len in wav_lens: logger.add_sample(duration=wav_len / sampling_rate) print(f'{i}/{len(speaker_dirs)} \r') 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, "flac", 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] keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()] 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, "wav", 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_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a", skip_existing, logger)