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run_preprocess.py
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run_preprocess.py
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#!/usr/bin/env python3
"""
File [ run_preprocess.py ]
Author [ Heng-Jui Chang (MIT CSAIL) ]
Synopsis [ Preprocesses corpus. ]
"""
import argparse
import json
import logging
import os
from os.path import getsize, join
from joblib import Parallel, delayed
from miniasr.preprocess.generate_vocab import (
generate_subword_vocab,
generate_word_char_vocab,
)
from miniasr.utils import base_args, logging_args
def parse_arguments():
"""Parses arguments from command line."""
parser = argparse.ArgumentParser("Corpus preprocessing.")
# General arguments
parser.add_argument("--corpus", "-c", type=str, help="Corpus name.")
parser.add_argument("--path", "-p", type=str, help="Path to corpus.")
parser.add_argument(
"--set", "-s", type=str, nargs="+", help="Which subsets to be processed."
)
parser.add_argument(
"--out", "-o", type=str, default="data/", help="Output directory."
)
# Vocabulary
parser.add_argument(
"--gen-vocab",
action="store_true",
help="Specify whether to generate vocabulary files.",
)
parser.add_argument(
"--source",
type=str,
default="text",
help="Source of text data in data_dict (key)",
)
parser.add_argument(
"--char-vocab-size", type=int, default=-1, help="Character vocabulary size."
)
parser.add_argument(
"--word-vocab-size", type=int, default=-1, help="Word vocabulary size."
)
parser.add_argument(
"--subword-vocab-size", type=int, default=-1, help="Subword vocabulary size."
)
# Subword units
parser.add_argument(
"--gen-subword",
action="store_true",
help="Specify whether to generate subword vocabulary.",
)
parser.add_argument(
"--subword-mode",
type=str,
default="unigram",
choices=["unigram", "bpe"],
help="Subword training mode.",
)
parser.add_argument(
"--char-coverage", type=float, default=1.0, help="Character coverage."
)
# Add basic arguments
parser = base_args(parser)
# Parse arguments
args = parser.parse_args()
logging_args(args)
return args
def main():
"""Main function of preprocessing"""
args = parse_arguments()
if args.corpus == "LibriSpeech":
from miniasr.preprocess.librispeech import find_data
if args.corpus == "TIMIT":
from miniasr.preprocess.timit import find_data
else:
# You may add new methods here.
raise NotImplementedError(f"Unknown corpus {args.corpus}.")
logging.info(f"Preprocessing {args.corpus} corpus.")
logging.info(f"Subsets = {args.set}")
os.makedirs(args.out, exist_ok=True)
logging.info(f"Results will be saved to {args.out}")
# Find all data
logging.info(f"Reading data from {args.path}")
data_dict_list = [find_data(args.path, s) for s in args.set]
data_dict, data_list = {}, []
for d in data_dict_list:
data_dict = {**data_dict, **d}
data_list += [v for k, v in d.items()]
logging.info(f"Found {len(data_list)} audio files.")
# Save dictionary of data
json_path = join(args.out, "data_dict.json")
logging.info(f"Saving unsorted data dict to {json_path}")
with open(json_path, "w") as fp:
json.dump(data_dict, fp, indent=4, ensure_ascii=False)
# Sort data by audio file length
logging.info("Sorting data by audio file length.")
file_len = Parallel(n_jobs=args.njobs)(
delayed(getsize)(d["file"]) for d in data_list
)
logging.info(f"Total audio size = {sum(file_len)} Bytes")
data_list = [
d
for d, l in sorted(zip(data_list, file_len), reverse=True, key=lambda x: x[1])
if l > 0 and len(d.get(args.source, "dummy")) > 0
]
# Save sorted data list
json_path = join(args.out, "data_list_sorted.json")
logging.info(f"Saving sorted data list to {json_path}")
with open(json_path, "w") as fp:
json.dump(data_list, fp, indent=4, ensure_ascii=False)
# Generate pure text file for LM training
if data_list[0].get(args.source, None):
logging.info("Generating LM file.")
text_list = [d[args.source] for d in data_list]
text_path = join(args.out, "text.txt")
with open(text_path, "w") as fp:
fp.write("\n".join(text_list))
# Generate vocabulary files.
if args.gen_vocab:
logging.info("Generating vocabularies.")
if args.char_vocab_size > 0:
logging.info("Generating characters.")
generate_word_char_vocab(
text_list,
join(args.out, "vocab_char.txt"),
args.char_vocab_size,
"char",
)
if args.word_vocab_size > 0:
logging.info("Generating words.")
generate_word_char_vocab(
text_list,
join(args.out, "vocab_word.txt"),
args.word_vocab_size,
"word",
)
if args.gen_subword and args.subword_vocab_size > 0:
logging.info("Generating subwords.")
generate_subword_vocab(
text_path,
join(args.out, f"{args.subword_mode}_{args.subword_vocab_size}"),
vocab_size=args.subword_vocab_size,
model_type=args.subword_mode,
character_coverage=args.char_coverage,
)
if __name__ == "__main__":
main()