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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
from tqdm import tqdm
import os
import torchaudio
from utils import audio
import csv
import random
from utils.util import has_existed
from text import _clean_text
import librosa
import soundfile as sf
from scipy.io import wavfile
from pathlib import Path
import numpy as np
def textgird_extract(
corpus_directory,
output_directory,
mfa_path=os.path.join("mfa", "montreal-forced-aligner", "bin", "mfa_align"),
lexicon=os.path.join("mfa", "lexicon", "librispeech-lexicon.txt"),
acoustic_model_path=os.path.join(
"mfa", "montreal-forced-aligner", "pretrained_models", "english.zip"
),
jobs="8",
):
assert os.path.exists(
corpus_directory
), "Please check the directionary contains *.wav, *.lab"
assert (
os.path.exists(mfa_path)
and os.path.exists(lexicon)
and os.path.exists(acoustic_model_path)
), f"Please download the MFA tools to {mfa_path} firstly"
Path(output_directory).mkdir(parents=True, exist_ok=True)
print(f"MFA results are save in {output_directory}")
os.system(
f".{os.path.sep}{mfa_path} {corpus_directory} {lexicon} {acoustic_model_path} {output_directory} -j {jobs} --clean"
)
def get_lines(file):
lines = []
with open(file, encoding="utf-8") as f:
for line in tqdm(f):
lines.append(line.strip())
return lines
def get_uid2utt(ljspeech_path, dataset, cfg):
index_count = 0
total_duration = 0
uid2utt = []
for l in tqdm(dataset):
items = l.split("|")
uid = items[0]
text = items[2]
res = {
"Dataset": "LJSpeech",
"index": index_count,
"Singer": "LJSpeech",
"Uid": uid,
"Text": text,
}
# Duration in wav files
audio_file = os.path.join(ljspeech_path, "wavs/{}.wav".format(uid))
res["Path"] = audio_file
waveform, sample_rate = torchaudio.load(audio_file)
duration = waveform.size(-1) / sample_rate
res["Duration"] = duration
uid2utt.append(res)
index_count = index_count + 1
total_duration += duration
return uid2utt, total_duration / 3600
def split_dataset(lines, test_rate=0.05, test_size=None):
if test_size == None:
test_size = int(len(lines) * test_rate)
random.shuffle(lines)
train_set = []
test_set = []
for line in lines[:test_size]:
test_set.append(line)
for line in lines[test_size:]:
train_set.append(line)
return train_set, test_set
max_wav_value = 32768.0
def prepare_align(dataset, dataset_path, cfg, output_path):
in_dir = dataset_path
out_dir = os.path.join(output_path, dataset, cfg.raw_data)
sampling_rate = cfg.sample_rate
cleaners = cfg.text_cleaners
speaker = "LJSpeech"
with open(os.path.join(dataset_path, "metadata.csv"), encoding="utf-8") as f:
for line in tqdm(f):
parts = line.strip().split("|")
base_name = parts[0]
text = parts[2]
text = _clean_text(text, cleaners)
output_wav_path = os.path.join(out_dir, speaker, "{}.wav".format(base_name))
output_lab_path = os.path.join(out_dir, speaker, "{}.lab".format(base_name))
if os.path.exists(output_wav_path) and os.path.exists(output_lab_path):
continue
wav_path = os.path.join(in_dir, "wavs", "{}.wav".format(base_name))
if os.path.exists(wav_path):
os.makedirs(os.path.join(out_dir, speaker), exist_ok=True)
wav, _ = librosa.load(wav_path, sampling_rate)
wav = wav / max(abs(wav)) * max_wav_value
wavfile.write(
os.path.join(out_dir, speaker, "{}.wav".format(base_name)),
sampling_rate,
wav.astype(np.int16),
)
with open(
os.path.join(out_dir, speaker, "{}.lab".format(base_name)),
"w",
) as f1:
f1.write(text)
# Extract textgird with MFA
textgird_extract(
corpus_directory=out_dir,
output_directory=os.path.join(output_path, dataset, "TextGrid"),
)
def main(output_path, dataset_path, cfg):
print("-" * 10)
print("Dataset splits for {}...\n".format("LJSpeech"))
dataset = "LJSpeech"
save_dir = os.path.join(output_path, dataset)
os.makedirs(save_dir, exist_ok=True)
ljspeech_path = dataset_path
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.json")
singer_dict_file = os.path.join(save_dir, "singers.json")
speaker = "LJSpeech"
speakers = [dataset + "_" + speaker]
singer_lut = {name: i for i, name in enumerate(sorted(speakers))}
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
if has_existed(train_output_file) and has_existed(test_output_file):
return
meta_file = os.path.join(ljspeech_path, "metadata.csv")
lines = get_lines(meta_file)
train_set, test_set = split_dataset(lines)
res, hours = get_uid2utt(ljspeech_path, train_set, cfg)
# Save train
os.makedirs(save_dir, exist_ok=True)
with open(train_output_file, "w") as f:
json.dump(res, f, indent=4, ensure_ascii=False)
print("Train_hours= {}".format(hours))
res, hours = get_uid2utt(ljspeech_path, test_set, cfg)
# Save test
os.makedirs(save_dir, exist_ok=True)
with open(test_output_file, "w") as f:
json.dump(res, f, indent=4, ensure_ascii=False)
print("Test_hours= {}".format(hours))
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