PaddleSpeech/utils/spm_encode

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in
# https://github.com/pytorch/fairseq/blob/master/LICENSE
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import argparse
import contextlib
import sys

import sentencepiece as spm


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", required=True,
                        help="sentencepiece model to use for encoding")
    parser.add_argument("--inputs", nargs="+", default=['-'],
                        help="input files to filter/encode")
    parser.add_argument("--outputs", nargs="+", default=['-'],
                        help="path to save encoded outputs")
    parser.add_argument("--output_format", choices=["piece", "id"], default="piece")
    parser.add_argument("--min-len", type=int, metavar="N",
                        help="filter sentence pairs with fewer than N tokens")
    parser.add_argument("--max-len", type=int, metavar="N",
                        help="filter sentence pairs with more than N tokens")
    args = parser.parse_args()

    assert len(args.inputs) == len(args.outputs), \
        "number of input and output paths should match"

    sp = spm.SentencePieceProcessor()
    sp.Load(args.model)

    if args.output_format == "piece":
        def encode(l):
            return sp.EncodeAsPieces(l)
    elif args.output_format == "id":
        def encode(l):
            return list(map(str, sp.EncodeAsIds(l)))
    else:
        raise NotImplementedError

    if args.min_len is not None or args.max_len is not None:
        def valid(line):
            return (
                (args.min_len is None or len(line) >= args.min_len) and
                (args.max_len is None or len(line) <= args.max_len)
            )
    else:
        def valid(lines):
            return True

    with contextlib.ExitStack() as stack:
        inputs = [
            stack.enter_context(open(input, "r", encoding="utf-8"))
            if input != "-" else sys.stdin
            for input in args.inputs
        ]
        outputs = [
            stack.enter_context(open(output, "w", encoding="utf-8"))
            if output != "-" else sys.stdout
            for output in args.outputs
        ]

        stats = {
            "num_empty": 0,
            "num_filtered": 0,
        }

        def encode_line(line):
            line = line.strip()
            if len(line) > 0:
                line = encode(line)
                if valid(line):
                    return line
                else:
                    stats["num_filtered"] += 1
            else:
                stats["num_empty"] += 1
            return None

        for i, lines in enumerate(zip(*inputs), start=1):
            enc_lines = list(map(encode_line, lines))
            if not any(enc_line is None for enc_line in enc_lines):
                for enc_line, output_h in zip(enc_lines, outputs):
                    print(" ".join(enc_line), file=output_h)
            if i % 10000 == 0:
                print("processed {} lines".format(i), file=sys.stderr)

        print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr)
        print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr)


if __name__ == "__main__":
    main()