voice-cloning-collab/encoder_test_preprocess.py

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 <datasets_root>/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)