mirror of
https://github.com/storytold/vits-finetuning.git
synced 2026-10-13 23:53:25 +00:00
ZDisket
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
import argparse
|
|
import text
|
|
from utils import load_filepaths_and_text
|
|
from tqdm import tqdm
|
|
import os
|
|
from moji.moji import TorchMoji
|
|
import torch
|
|
from bertfe import BERTFrontEnd
|
|
import commons
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--out_extension", default="cleaned")
|
|
parser.add_argument("--text_index", default=1, type=int)
|
|
parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
|
|
parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
|
|
parser.add_argument("--bert", default="huawei-noah/TinyBERT_General_4L_312D")
|
|
parser.add_argument("--test", default="")
|
|
|
|
|
|
args = parser.parse_args()
|
|
moji = TorchMoji(verbose=True)
|
|
bert_f = BERTFrontEnd(model_name=args.bert)
|
|
test_sentences = ["The quick brown fox jumps over the lazy dog",
|
|
"In a galaxy far, far away, a young hero embarks on an epic adventure",
|
|
"Ladies and gentlemen, welcome to the annual science fair!",
|
|
"The chef skillfully prepares a delicious gourmet meal with fresh ingredients and exquisite flavors",
|
|
"The crowd erupted in cheers as the team scored the winning goal in the final seconds of the match"]
|
|
|
|
if len(args.test) > 1:
|
|
with open(args.test) as f:
|
|
test_sentences = f.readlines()
|
|
|
|
|
|
if "arpa_cleaners" in args.text_cleaners:
|
|
print("ARPA cleaners detected, will run in dual mode")
|
|
|
|
for filelist in args.filelists:
|
|
print("START:", filelist)
|
|
filepaths_and_text = load_filepaths_and_text(filelist)
|
|
new_fp_text = []
|
|
|
|
for i in tqdm(range(len(filepaths_and_text))):
|
|
original_text = filepaths_and_text[i][args.text_index]
|
|
original_fname = filepaths_and_text[i][0].split(".")[0] # wavs/cat.wav -> wavs/cat
|
|
|
|
moji_filename = original_fname + ".torchmoji"
|
|
if not os.path.isfile(moji_filename):
|
|
moji_result = moji(original_text)
|
|
torch.save(moji_result, moji_filename)
|
|
|
|
|
|
bert_filename = original_fname + ".bert"
|
|
if not os.path.isfile(bert_filename):
|
|
bert_res, _ = bert_f.infer(original_text)
|
|
torch.save(bert_res,bert_filename)
|
|
|
|
|
|
cleaned_text = text._clean_text(original_text, args.text_cleaners)
|
|
text_arrs = filepaths_and_text[i]
|
|
text_arrs[args.text_index] = cleaned_text
|
|
new_fp_text.append(text_arrs)
|
|
if "arpa_cleaners" in args.text_cleaners:
|
|
# ZDisket: Python's default behavior when equalling a list is a reference, so unless we use .copy() this ends up
|
|
# overriding the previous entry and leading into 2 text entries (wtf, why like this?)
|
|
text_arrs2 = filepaths_and_text[i].copy()
|
|
text_arrs2[args.text_index] = text._clean_text(original_text, ["arpa_precleaners"])
|
|
new_fp_text.append(text_arrs2)
|
|
|
|
|
|
new_filelist = filelist + "." + args.out_extension
|
|
with open(new_filelist, "w", encoding="utf-8") as f:
|
|
for x in new_fp_text:
|
|
f.write("|".join(x) + "\n")
|
|
|
|
print("Writing test data...")
|
|
test_path = "test_preproc"
|
|
for i, test_sent in tqdm(enumerate(test_sentences)):
|
|
moji_t = moji(test_sent)
|
|
bert_t, _ = bert_f.infer(test_sent)
|
|
|
|
test_cleaned_text = text._clean_text(test_sent, args.text_cleaners)
|
|
cleaned_text_indices = text.cleaned_text_to_sequence(test_cleaned_text)
|
|
text_norm = commons.intersperse(cleaned_text_indices, 0)
|
|
text_norm = torch.LongTensor(text_norm)
|
|
|
|
if not os.path.exists(test_path):
|
|
os.makedirs(test_path)
|
|
|
|
test_rawfn = f"test{i}.pt"
|
|
test_fullfn = os.path.join(test_path, test_rawfn)
|
|
|
|
torch.save([text_norm, moji_t, bert_t], test_fullfn)
|
|
|
|
|
|
|
|
|
|
|
|
|