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import logging
import os
import tempfile
import pytest
from dotenv import dotenv_values
from s3prl.dataio.corpus.librispeech import LibriSpeech
from s3prl.dataio.encoder.tokenizer import load_tokenizer
from s3prl.dataio.encoder.vocabulary import generate_vocab
SAMPLE = "GOOD MORNING MY FRIEND"
def is_same_vocab(vocabs_1, vocabs_2):
if len(vocabs_1) != len(vocabs_2):
return False
for v1, v2 in zip(vocabs_1, vocabs_2):
if v1 != v2:
return False
return True
@pytest.mark.corpus
def test_vocabulary():
config = dotenv_values()
corpus = LibriSpeech(config["LibriSpeech"])
text_list = corpus.data_dict["train-clean-100"]["text_list"]
with tempfile.TemporaryDirectory() as directory:
logging.info(directory)
text_file = os.path.join(directory, "text.txt")
with open(text_file, "w") as fp:
for text in text_list:
fp.write(text + "\n")
# Character
char_vocabs_1 = generate_vocab("character", text_list=text_list)
char_vocabs_2 = generate_vocab("character", text_file=text_file)
assert isinstance(char_vocabs_1, list)
assert isinstance(char_vocabs_2, list)
assert is_same_vocab(char_vocabs_1, char_vocabs_2)
char_tokenizer = load_tokenizer("character", vocab_list=char_vocabs_1)
assert char_tokenizer.decode(char_tokenizer.encode(SAMPLE)) == SAMPLE
# Word
word_vocabs_1 = generate_vocab("word", text_list=text_list, vocab_size=5000)
word_vocabs_2 = generate_vocab("word", text_file=text_file, vocab_size=5000)
assert isinstance(word_vocabs_1, list)
assert isinstance(word_vocabs_2, list)
assert is_same_vocab(word_vocabs_1, word_vocabs_2)
word_tokenizer = load_tokenizer("word", vocab_list=word_vocabs_1)
assert word_tokenizer.decode(word_tokenizer.encode(SAMPLE)) == SAMPLE
# Subword
vocab_file_1 = os.path.join(directory, "subword_1")
vocab_file_2 = os.path.join(directory, "subword_2")
subword_vocabs_1 = generate_vocab(
"subword", text_list=text_list, vocab_size=500, output_file=vocab_file_1
)
subword_vocabs_2 = generate_vocab(
"subword", text_file=text_file, vocab_size=500, output_file=vocab_file_2
)
subword_tokenizer_1 = load_tokenizer(
"subword", vocab_file=vocab_file_1 + ".model"
)
subword_tokenizer_2 = load_tokenizer(
"subword", vocab_file=vocab_file_2 + ".model"
)
assert subword_tokenizer_1.decode(subword_tokenizer_1.encode(SAMPLE)) == SAMPLE
assert subword_tokenizer_2.decode(subword_tokenizer_2.encode(SAMPLE)) == SAMPLE
assert subword_tokenizer_1.encode(SAMPLE) == subword_tokenizer_2.encode(SAMPLE)
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