simple-sentencepiece 0.12

A simple sentencepiece encoder and decoder without any dependency.

simple-sentencepiece

A simple sentencepiece encoder and decoder.

Note: This is not a new sentencepiece toolkit, it just uses google's sentencepiece model as input and encode the string to ids/pieces or decode the ids to string. The advantage of this tool is that it doesn't have any dependency (no protobuf), so it will be easier to integrate it into a C++ project.

Installation

pip install simple-sentencepiece

Usage

The usage is very similar to sentencepiece, it also has encode and decode interface.

from ssentencepiece import Ssentencepiece

# you can get bpe.vocab from a trained bpe model, see google's sentencepiece for details
ssp = Ssentencepiece("/path/to/bpe.vocab")

# you can also use the default models provided by this package, see below for details
ssp = Ssentencepiece("gigaspeech-500")
ssp = Ssentencepiece("zh-en-10381")

# output ids (support both str and list of strs)
# if it is list of strs, the strs are encoded in parallel
ids = ssp.encode("HELLO WORLD")
ids = ssp.encode(["HELLO WORLD", "LOVE AND PIECE"])

# output string pieces
# if it is list of strs, the strs are encoded in parallel
pieces = ssp.encode("HELLO WORLD", out_type=str)
pieces = ssp.encode(["HELLO WORLD", "LOVE AND PIECE"], out_type=str)

# decode (support list of ids or list of list of ids)
# if it is list of list of ids, the ids are decoded in parallel
res = ssp.decode([1,2,3,4,5])
res = ssp.decode([[1,2,3,4], [4,5,6,7]])

# get vocab size
res = ssp.vocab_size()

# piece to id (support both str of list of strs)
id = ssp.piece_to_id("<sos>")
ids = ssp.piece_to_id(["<sos>", "<blk>", "H", "L"])

# id to piece (support both int of list of ints)
piece = ssp.id_to_piece(5)
pieces = ssp.id_to_piece([5, 10, 15])

Default models

Model Name	Description	Link
alphabet-33	<blk>,<unk>, <sos>, <eos>, <pad>, ', ▁ and 26 alphabets.	alphabet-33
librispeech-500	500 unigram pieces trained on Librispeech.	librispeech-500
librispeech-5000	5000 unigram pieces trained on Librispeech.	librispeech-5000
gigaspeech-500	500 unigram pieces trained on Gigaspeech.	gigaspeech-500
gigaspeech-2000	2000 unigram pieces trained on Gigaspeech.	gigaspeech-2000
gigaspeech-5000	5000 unigram pieces trained on Gigaspeech.	gigaspeech-5000
zh-en-3876	3500 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 100 English unigram pieces.	zh-en-3876
zh-en-6876	6500 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 100 English unigram pieces.	zh-en-6876
zh-en-8481	8105 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 100 English unigram pieces.	zh-en-8481
zh-en-5776	3500 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 2000 English unigram pieces.	zh-en-5776
zh-en-8776	6500 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 2000 English unigram pieces.	zh-en-8776
zh-en-10381	8105 Chinese characters, 256 fallback bytes, 10 numbers, 10 punctuations, 2000 English unigram pieces.	zh-en-10381
zh-en-yue-9761	8105 + 1280 Chinese characters(Cantonese included), 256 fallback bytes, 10 numbers, 10 punctuations, 100 English unigram pieces.	zh-en-yue-9761
zh-en-yue-11661	8105 + 1280 Chinese characters(Cantonese included), 256 fallback bytes, 10 numbers, 10 punctuations, 2000 English unigram pieces.	zh-en-yue-11661
chn_jpn_yue_eng_ko_spectok.bpe	bpe tokens used in sensevoice ASR, support Chinese, Japanese, Cantonese, English, Korean	chn_jpn_yue_eng_ko_spectok.bpe

Note: The number of 3500, 6500 and 8105 is from 通用规范汉字表.

C++ Integration

The C++ core has no protobuf dependency — only a C++14 compiler and pthread are required. This makes it easy to embed simple-sentencepiece in any existing C++ system without conflicting with other protobuf versions.

A complete integration demo with CMake build scripts is provided in example/.

• English guide: example/README.md
• 中文指南: example/README_zh.md