| 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() |
|
|
