File size: 4,497 Bytes
901e06a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | import os
import tqdm
import argparse
import pandas as pd
import sentencepiece as spm
from pathlib import Path
from tempfile import NamedTemporaryFile
import sys
import os
import re
dir_path = os.path.dirname(os.path.realpath(__file__))
parent_dir_path = os.path.abspath(os.path.join(dir_path, os.pardir))
sys.path.insert(0, parent_dir_path)
from examples.speech_to_text.data_utils import (
load_df_from_tsv,
save_df_to_tsv,
gen_vocab,
)
from examples.speech_synthesis.data_utils import ipa_phonemize
MANIFEST_COLUMNS = ["id", "tgt_text"]
SPLITS = ["train", "dev", "test"]
def learn_spm_vocab(args, train_text):
with NamedTemporaryFile(mode="w") as f:
for t in train_text:
f.write(t + "\n")
gen_vocab(
Path(f.name),
Path(args.output_dir).absolute() / f"spm_{args.vocab_type}_{args.lang}",
args.vocab_type,
args.vocab_size,
)
def process(args):
output_dir = Path(args.output_dir).absolute()
output_dir.mkdir(exist_ok=True)
if args.vocab_type in ["char", "unigram"]:
train_text = []
df = load_df_from_tsv(Path(args.data_dir).absolute() / "train.tsv")
data = list(df.T.to_dict().values())
for item in data:
if args.is_src_text:
item["src_text"] = re.sub(r"[^\w\s]", "", item["src_text"].lower())
train_text.append(item["src_text"])
else:
item["tgt_text"] = re.sub(r"[^\w\s]", "", item["tgt_text"].lower())
train_text.append(item["tgt_text"])
learn_spm_vocab(args, train_text)
sp = spm.SentencePieceProcessor(
model_file=os.path.join(
output_dir, f"spm_{args.vocab_type}_{args.lang}.model"
)
)
for split in SPLITS:
df = load_df_from_tsv(Path(args.data_dir).absolute() / f"{split}.tsv")
data = list(df.T.to_dict().values())
manifest = {c: [] for c in MANIFEST_COLUMNS}
for item in data:
manifest["id"].append(item["id"])
if args.is_src_text:
item["src_text"] = re.sub(r"[^\w\s]", "", item["src_text"].lower())
manifest["tgt_text"].append(
" ".join(sp.encode(item["src_text"], out_type=str))
)
else:
item["tgt_text"] = re.sub(r"[^\w\s]", "", item["tgt_text"].lower())
manifest["tgt_text"].append(
" ".join(sp.encode(item["tgt_text"], out_type=str))
)
df = pd.DataFrame.from_dict(manifest)
save_df_to_tsv(df, output_dir / f"{split}.tsv")
else:
for split in SPLITS:
df = load_df_from_tsv(Path(args.data_dir).absolute() / f"{split}.tsv")
data = list(df.T.to_dict().values())
manifest = {c: [] for c in MANIFEST_COLUMNS}
for item in tqdm.tqdm(data):
manifest["id"].append(item["id"])
if args.is_src_text:
item["src_text"] = re.sub(r"[^\w\s]", "", item["src_text"].lower())
manifest["tgt_text"].append(
ipa_phonemize(
item["src_text"], lang=args.lang, use_g2p=args.use_g2p
)
)
else:
item["tgt_text"] = re.sub(r"[^\w\s]", "", item["tgt_text"].lower())
manifest["tgt_text"].append(
ipa_phonemize(
item["tgt_text"], lang=args.lang, use_g2p=args.use_g2p
)
)
df = pd.DataFrame.from_dict(manifest)
save_df_to_tsv(df, output_dir / f"{split}.tsv")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", type=str, required=True)
parser.add_argument("--output-dir", type=str, required=True)
parser.add_argument("--lang", type=str, required=True)
parser.add_argument(
"--is-src-text",
action="store_true",
)
parser.add_argument(
"--vocab-type",
choices=["char", "phoneme", "unigram"],
required=True,
)
parser.add_argument("--vocab-size", default=6000, type=int)
parser.add_argument("--use-g2p", action="store_true")
args = parser.parse_args()
process(args)
if __name__ == "__main__":
main()
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