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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)
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