StarMirror/MockingBird
1
0
Fork 0
mirror of https://github.com/babysor/MockingBird.git synced 2024-03-22 13:11:31 +08:00
Code Issues Projects Releases Wiki Activity

Refactor preprocessor of synthesizer to prepare to supprot more datasets

Browse Source
This commit is contained in:
babysor00 2021-08-11 23:33:43 +08:00
parent c21d2c11dd
commit 856793c9bd
3 changed files with 261 additions and 295 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

303
synthesizer/preprocess.py
View File

@ -1,31 +1,27 @@
from multiprocessing.pool import Pool
from synthesizer import audio
from functools import partial
from itertools import chain
from encoder import inference as encoder
from pathlib import Path
from utils import logmmse
from tqdm import tqdm
import numpy as np
import librosa
import platform
from pypinyin import Style
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
from pypinyin.converter import DefaultConverter
from pypinyin.core import Pinyin
class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
pass
pinyin = Pinyin(PinyinConverter()).pinyin
from encoder import inference as encoder
from synthesizer.preprocess_speaker import preprocess_speaker_aidatatang_200zh
data_info = {
"aidatatang_200zh": {
"subfolders": ["corpus/train"],
"speak_func": preprocess_speaker_aidatatang_200zh
}
# TODO add more
}
def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
skip_existing: bool, hparams, no_alignments: bool,
datasets_name: str, subfolders: str):
dataset: str):
# Gather the input directories
dataset_root = datasets_root.joinpath(datasets_name)
input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in subfolders.split(",")]
dataset_root = datasets_root.joinpath(dataset)
input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in data_info[dataset]["subfolders"]]
print("\n ".join(map(str, ["Using data from:"] + input_dirs)))
assert all(input_dir.exists() for input_dir in input_dirs)
@ -39,10 +35,10 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
# Preprocess the dataset
speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
func = partial(preprocess_speaker, out_dir=out_dir, skip_existing=skip_existing,
hparams=hparams, no_alignments=no_alignments)
func = partial(data_info[dataset]["speak_func"], out_dir=out_dir, skip_existing=skip_existing,
hparams=hparams, directory=dataset_root, no_alignments=no_alignments)
job = Pool(n_processes).imap(func, speaker_dirs)
for speaker_metadata in tqdm(job, datasets_name, len(speaker_dirs), unit="speakers"):
for speaker_metadata in tqdm(job, dataset, len(speaker_dirs), unit="speakers"):
for metadatum in speaker_metadata:
metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
metadata_file.close()
@ -60,183 +56,6 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
def preprocess_speaker(speaker_dir, out_dir: Path, skip_existing: bool, hparams, no_alignments: bool):
metadata = []
for book_dir in speaker_dir.glob("*"):
if no_alignments:
# Gather the utterance audios and texts
# LibriTTS uses .wav but we will include extensions for compatibility with other datasets
extensions = ["*.wav", "*.flac", "*.mp3"]
for extension in extensions:
wav_fpaths = book_dir.glob(extension)
for wav_fpath in wav_fpaths:
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Get the corresponding text
# Check for .txt (for compatibility with other datasets)
text_fpath = wav_fpath.with_suffix(".txt")
if not text_fpath.exists():
# Check for .normalized.txt (LibriTTS)
text_fpath = wav_fpath.with_suffix(".normalized.txt")
assert text_fpath.exists()
with text_fpath.open("r") as text_file:
text = "".join([line for line in text_file])
text = text.replace("\"", "")
text = text.strip()
# Process the utterance
metadata.append(process_utterance(wav, text, out_dir, str(wav_fpath.with_suffix("").name),
skip_existing, hparams))
else:
# Process alignment file (LibriSpeech support)
# Gather the utterance audios and texts
try:
alignments_fpath = next(book_dir.glob("*.alignment.txt"))
with alignments_fpath.open("r") as alignments_file:
alignments = [line.rstrip().split(" ") for line in alignments_file]
except StopIteration:
# A few alignment files will be missing
continue
# Iterate over each entry in the alignments file
for wav_fname, words, end_times in alignments:
wav_fpath = book_dir.joinpath(wav_fname + ".flac")
assert wav_fpath.exists()
words = words.replace("\"", "").split(",")
end_times = list(map(float, end_times.replace("\"", "").split(",")))
# Process each sub-utterance
wavs, texts = split_on_silences(wav_fpath, words, end_times, hparams)
for i, (wav, text) in enumerate(zip(wavs, texts)):
sub_basename = "%s_%02d" % (wav_fname, i)
metadata.append(process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def split_on_silences(wav_fpath, words, end_times, hparams):
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
words = np.array(words)
start_times = np.array([0.0] + end_times[:-1])
end_times = np.array(end_times)
assert len(words) == len(end_times) == len(start_times)
assert words[0] == "" and words[-1] == ""
# Find pauses that are too long
mask = (words == "") & (end_times - start_times >= hparams.silence_min_duration_split)
mask[0] = mask[-1] = True
breaks = np.where(mask)[0]
# Profile the noise from the silences and perform noise reduction on the waveform
silence_times = [[start_times[i], end_times[i]] for i in breaks]
silence_times = (np.array(silence_times) * hparams.sample_rate).astype(np.int)
noisy_wav = np.concatenate([wav[stime[0]:stime[1]] for stime in silence_times])
if len(noisy_wav) > hparams.sample_rate * 0.02:
profile = logmmse.profile_noise(noisy_wav, hparams.sample_rate)
wav = logmmse.denoise(wav, profile, eta=0)
# Re-attach segments that are too short
segments = list(zip(breaks[:-1], breaks[1:]))
segment_durations = [start_times[end] - end_times[start] for start, end in segments]
i = 0
while i < len(segments) and len(segments) > 1:
if segment_durations[i] < hparams.utterance_min_duration:
# See if the segment can be re-attached with the right or the left segment
left_duration = float("inf") if i == 0 else segment_durations[i - 1]
right_duration = float("inf") if i == len(segments) - 1 else segment_durations[i + 1]
joined_duration = segment_durations[i] + min(left_duration, right_duration)
# Do not re-attach if it causes the joined utterance to be too long
if joined_duration > hparams.hop_size * hparams.max_mel_frames / hparams.sample_rate:
i += 1
continue
# Re-attach the segment with the neighbour of shortest duration
j = i - 1 if left_duration <= right_duration else i
segments[j] = (segments[j][0], segments[j + 1][1])
segment_durations[j] = joined_duration
del segments[j + 1], segment_durations[j + 1]
else:
i += 1
# Split the utterance
segment_times = [[end_times[start], start_times[end]] for start, end in segments]
segment_times = (np.array(segment_times) * hparams.sample_rate).astype(np.int)
wavs = [wav[segment_time[0]:segment_time[1]] for segment_time in segment_times]
texts = [" ".join(words[start + 1:end]).replace(" ", " ") for start, end in segments]
# # DEBUG: play the audio segments (run with -n=1)
# import sounddevice as sd
# if len(wavs) > 1:
# print("This sentence was split in %d segments:" % len(wavs))
# else:
# print("There are no silences long enough for this sentence to be split:")
# for wav, text in zip(wavs, texts):
# # Pad the waveform with 1 second of silence because sounddevice tends to cut them early
# # when playing them. You shouldn't need to do that in your parsers.
# wav = np.concatenate((wav, [0] * 16000))
# print("\t%s" % text)
# sd.play(wav, 16000, blocking=True)
# print("")
return wavs, texts
def process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
skip_existing: bool, hparams):
## FOR REFERENCE:
# For you not to lose your head if you ever wish to change things here or implement your own
# synthesizer.
# - Both the audios and the mel spectrograms are saved as numpy arrays
# - There is no processing done to the audios that will be saved to disk beyond volume
# normalization (in split_on_silences)
# - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
# is why we re-apply it on the audio on the side of the vocoder.
# - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
# without extra padding. This means that you won't have an exact relation between the length
# of the wav and of the mel spectrogram. See the vocoder data loader.
# Skip existing utterances if needed
mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
if skip_existing and mel_fpath.exists() and wav_fpath.exists():
return None
# Trim silence
if hparams.trim_silence:
wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
# Skip utterances that are too short
if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
return None
# Compute the mel spectrogram
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
# Skip utterances that are too long
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
# Write the spectrogram, embed and audio to disk
np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
np.save(wav_fpath, wav, allow_pickle=False)
# Return a tuple describing this training example
return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
def embed_utterance(fpaths, encoder_model_fpath):
if not encoder.is_loaded():
encoder.load_model(encoder_model_fpath)
@ -266,93 +85,3 @@ def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_proce
func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
job = Pool(n_processes).imap(func, fpaths)
list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
# aidatatang_200zh
def preprocess_aidatatang_200zh(datasets_root: Path, out_dir: Path, n_processes: int,
skip_existing: bool, hparams, no_alignments: bool, datasets_name=None, subfolders=None):
# Gather the input directories
dataset_root = datasets_root.joinpath("aidatatang_200zh")
dict_info = {}
transcript_dirs = dataset_root.joinpath("transcript/aidatatang_200_zh_transcript.txt")
with open(transcript_dirs,"rb") as fp:
dict_transcript = [v.decode() for v in fp]
for v in dict_transcript:
if not v:
continue
v = v.strip().replace("\n","").split(" ")
dict_info[v[0]] = " ".join(v[1:])
input_dirs = [dataset_root.joinpath("corpus/train")]
print("\n ".join(map(str, ["Using data from:"] + input_dirs)))
assert all(input_dir.exists() for input_dir in input_dirs)
# Create the output directories for each output file type
out_dir.joinpath("mels").mkdir(exist_ok=True)
out_dir.joinpath("audio").mkdir(exist_ok=True)
# Create a metadata file
metadata_fpath = out_dir.joinpath("train.txt")
metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8")
# Preprocess the dataset
speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs))
func = partial(preprocess_speaker_aidatatang_200zh, out_dir=out_dir, skip_existing=skip_existing,
hparams=hparams, dict_info=dict_info, no_alignments=no_alignments)
job = Pool(n_processes).imap(func, speaker_dirs)
for speaker_metadata in tqdm(job, "aidatatang_200zh", len(speaker_dirs), unit="speakers"):
for metadatum in speaker_metadata:
metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
metadata_file.close()
# Verify the contents of the metadata file
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
metadata = [line.split("|") for line in metadata_file]
mel_frames = sum([int(m[4]) for m in metadata])
timesteps = sum([int(m[3]) for m in metadata])
sample_rate = hparams.sample_rate
hours = (timesteps / sample_rate) / 3600
print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." %
(len(metadata), mel_frames, timesteps, hours))
print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
def preprocess_speaker_aidatatang_200zh(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool):
metadata = []
if platform.system() == "Windows":
split = "\\"
else:
split = "/"
# for book_dir in speaker_dir.glob("*"):
# Gather the utterance audios and texts
for wav_fpath in speaker_dir.glob("*.wav"):
# D:\dataset\data_aishell\wav\train\S0002\BAC009S0002W0122.wav
# Process each sub-utterance
name = str(wav_fpath).split(split)[-1]
key = name.split(".")[0]
words = dict_info.get(key)
if not words:
continue
sub_basename = "%s_%02d" % (name, 0)
wav, text = split_on_silences_aidatatang_200zh(wav_fpath, words, hparams)
metadata.append(process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def split_on_silences_aidatatang_200zh(wav_fpath, words, hparams):
# Load the audio waveform
wav, _ = librosa.load(wav_fpath, hparams.sample_rate)
wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=512)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
resp = pinyin(words, style=Style.TONE3)
res = [v[0] for v in resp if v[0].strip()]
res = " ".join(res)
return wav, res

234
synthesizer/preprocess_speaker.py Normal file
View File

@ -0,0 +1,234 @@
import platform
import librosa
import numpy as np
from encoder import inference as encoder
from utils import logmmse
from synthesizer import audio
from pathlib import Path
from pypinyin import Style
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
from pypinyin.converter import DefaultConverter
from pypinyin.core import Pinyin
class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
pass
pinyin = Pinyin(PinyinConverter()).pinyin
def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
skip_existing: bool, hparams):
## FOR REFERENCE:
# For you not to lose your head if you ever wish to change things here or implement your own
# synthesizer.
# - Both the audios and the mel spectrograms are saved as numpy arrays
# - There is no processing done to the audios that will be saved to disk beyond volume
# normalization (in split_on_silences)
# - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
# is why we re-apply it on the audio on the side of the vocoder.
# - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
# without extra padding. This means that you won't have an exact relation between the length
# of the wav and of the mel spectrogram. See the vocoder data loader.
# Skip existing utterances if needed
mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
if skip_existing and mel_fpath.exists() and wav_fpath.exists():
return None
# Trim silence
if hparams.trim_silence:
wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
# Skip utterances that are too short
if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
return None
# Compute the mel spectrogram
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
# Skip utterances that are too long
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
# Write the spectrogram, embed and audio to disk
np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
np.save(wav_fpath, wav, allow_pickle=False)
# Return a tuple describing this training example
return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
def _split_on_silences_aidatatang_200zh(wav_fpath, words, hparams):
# Load the audio waveform
wav, _ = librosa.load(wav_fpath, hparams.sample_rate)
wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=512)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
resp = pinyin(words, style=Style.TONE3)
res = [v[0] for v in resp if v[0].strip()]
res = " ".join(res)
return wav, res
def preprocess_speaker_aidatatang_200zh(speaker_dir, out_dir: Path, skip_existing: bool, hparams, directory, no_alignments: bool):
dict_info = {}
transcript_dirs = directory.joinpath("transcript/aidatatang_200_zh_transcript.txt")
with open(transcript_dirs,"rb") as fp:
dict_transcript = [v.decode() for v in fp]
for v in dict_transcript:
if not v:
continue
v = v.strip().replace("\n","").split(" ")
dict_info[v[0]] = " ".join(v[1:])
metadata = []
if platform.system() == "Windows":
split = "\\"
else:
split = "/"
for wav_fpath in speaker_dir.glob("*.wav"):
name = str(wav_fpath).split(split)[-1]
key = name.split(".")[0]
words = dict_info.get(key)
if not words:
continue
sub_basename = "%s_%02d" % (name, 0)
wav, text = _split_on_silences_aidatatang_200zh(wav_fpath, words, hparams)
metadata.append(_process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def preprocess_speaker(speaker_dir, out_dir: Path, skip_existing: bool, hparams, no_alignments: bool):
metadata = []
for book_dir in speaker_dir.glob("*"):
if no_alignments:
# Gather the utterance audios and texts
# LibriTTS uses .wav but we will include extensions for compatibility with other datasets
extensions = ["*.wav", "*.flac", "*.mp3"]
for extension in extensions:
wav_fpaths = book_dir.glob(extension)
for wav_fpath in wav_fpaths:
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Get the corresponding text
# Check for .txt (for compatibility with other datasets)
text_fpath = wav_fpath.with_suffix(".txt")
if not text_fpath.exists():
# Check for .normalized.txt (LibriTTS)
text_fpath = wav_fpath.with_suffix(".normalized.txt")
assert text_fpath.exists()
with text_fpath.open("r") as text_file:
text = "".join([line for line in text_file])
text = text.replace("\"", "")
text = text.strip()
# Process the utterance
metadata.append(_process_utterance(wav, text, out_dir, str(wav_fpath.with_suffix("").name),
skip_existing, hparams))
else:
# Process alignment file (LibriSpeech support)
# Gather the utterance audios and texts
try:
alignments_fpath = next(book_dir.glob("*.alignment.txt"))
with alignments_fpath.open("r") as alignments_file:
alignments = [line.rstrip().split(" ") for line in alignments_file]
except StopIteration:
# A few alignment files will be missing
continue
# Iterate over each entry in the alignments file
for wav_fname, words, end_times in alignments:
wav_fpath = book_dir.joinpath(wav_fname + ".flac")
assert wav_fpath.exists()
words = words.replace("\"", "").split(",")
end_times = list(map(float, end_times.replace("\"", "").split(",")))
# Process each sub-utterance
wavs, texts = _split_on_silences(wav_fpath, words, end_times, hparams)
for i, (wav, text) in enumerate(zip(wavs, texts)):
sub_basename = "%s_%02d" % (wav_fname, i)
metadata.append(_process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
# TODO: use original split func
def _split_on_silences(wav_fpath, words, end_times, hparams):
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
words = np.array(words)
start_times = np.array([0.0] + end_times[:-1])
end_times = np.array(end_times)
assert len(words) == len(end_times) == len(start_times)
assert words[0] == "" and words[-1] == ""
# Find pauses that are too long
mask = (words == "") & (end_times - start_times >= hparams.silence_min_duration_split)
mask[0] = mask[-1] = True
breaks = np.where(mask)[0]
# Profile the noise from the silences and perform noise reduction on the waveform
silence_times = [[start_times[i], end_times[i]] for i in breaks]
silence_times = (np.array(silence_times) * hparams.sample_rate).astype(np.int)
noisy_wav = np.concatenate([wav[stime[0]:stime[1]] for stime in silence_times])
if len(noisy_wav) > hparams.sample_rate * 0.02:
profile = logmmse.profile_noise(noisy_wav, hparams.sample_rate)
wav = logmmse.denoise(wav, profile, eta=0)
# Re-attach segments that are too short
segments = list(zip(breaks[:-1], breaks[1:]))
segment_durations = [start_times[end] - end_times[start] for start, end in segments]
i = 0
while i < len(segments) and len(segments) > 1:
if segment_durations[i] < hparams.utterance_min_duration:
# See if the segment can be re-attached with the right or the left segment
left_duration = float("inf") if i == 0 else segment_durations[i - 1]
right_duration = float("inf") if i == len(segments) - 1 else segment_durations[i + 1]
joined_duration = segment_durations[i] + min(left_duration, right_duration)
# Do not re-attach if it causes the joined utterance to be too long
if joined_duration > hparams.hop_size * hparams.max_mel_frames / hparams.sample_rate:
i += 1
continue
# Re-attach the segment with the neighbour of shortest duration
j = i - 1 if left_duration <= right_duration else i
segments[j] = (segments[j][0], segments[j + 1][1])
segment_durations[j] = joined_duration
del segments[j + 1], segment_durations[j + 1]
else:
i += 1
# Split the utterance
segment_times = [[end_times[start], start_times[end]] for start, end in segments]
segment_times = (np.array(segment_times) * hparams.sample_rate).astype(np.int)
wavs = [wav[segment_time[0]:segment_time[1]] for segment_time in segment_times]
texts = [" ".join(words[start + 1:end]).replace(" ", " ") for start, end in segments]
# # DEBUG: play the audio segments (run with -n=1)
# import sounddevice as sd
# if len(wavs) > 1:
# print("This sentence was split in %d segments:" % len(wavs))
# else:
# print("There are no silences long enough for this sentence to be split:")
# for wav, text in zip(wavs, texts):
# # Pad the waveform with 1 second of silence because sounddevice tends to cut them early
# # when playing them. You shouldn't need to do that in your parsers.
# wav = np.concatenate((wav, [0] * 16000))
# print("\t%s" % text)
# sd.play(wav, 16000, blocking=True)
# print("")
return wavs, texts

19
synthesizer_preprocess_audio.py
View File

@ -1,10 +1,15 @@
from synthesizer.preprocess import preprocess_aidatatang_200zh
from synthesizer.preprocess import preprocess_dataset
from synthesizer.hparams import hparams
from utils.argutils import print_args
from pathlib import Path
import argparse
recognized_datasets = [
"aidatatang_200zh",
"SLR68",
]
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
@ -29,16 +34,14 @@ if __name__ == "__main__":
parser.add_argument("--no_alignments", action="store_true", help=\
"Use this option when dataset does not include alignments\
(these are used to split long audio files into sub-utterances.)")
parser.add_argument("--datasets_name", type=str, default="LibriSpeech", help=\
"Name of the dataset directory to process.")
parser.add_argument("--subfolders", type=str, default="train-clean-100, train-clean-360", help=\
"Comma-separated list of subfolders to process inside your dataset directory")
parser.add_argument("--dataset", type=str, default="aidatatang_200zh", help=\
"Name of the dataset to process.")
args = parser.parse_args()
# Process the arguments
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
assert args.dataset in recognized_datasets, 'not surpport such dataset'
# Create directories
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
@ -56,5 +59,5 @@ if __name__ == "__main__":
# Preprocess the dataset
print_args(args, parser)
args.hparams = hparams.parse(args.hparams)
# preprocess_dataset(**vars(args))
preprocess_aidatatang_200zh(**vars(args))
preprocess_dataset(**vars(args))