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Testing Multilingual Tokenizer Implementation in OpenAI Whisper

This test suite validates the Whisper tokenizer functionality, focusing on multilingual support, token encoding/decoding, and Unicode handling. The tests ensure proper tokenization across different languages and character sets while maintaining text integrity.

Test Coverage Overview

The test suite provides comprehensive coverage of the Whisper tokenizer’s core functionality.

Key areas tested include:

Basic tokenizer initialization and configuration
Multilingual vs. standard tokenizer behavior
Language code and token mapping validation
Unicode character handling and token splitting

Implementation Analysis

The testing approach utilizes pytest’s parametrize feature for efficient test case variation. The implementation validates both monolingual and multilingual tokenization scenarios, with specific focus on Korean text encoding/decoding and Unicode character handling.

Notable patterns include:

Parametrized test cases for configuration variants
Direct comparison of tokenizer outputs
Validation of token sequence integrity

Technical Details

Testing tools and configuration:

pytest as the primary testing framework
Whisper tokenizer module integration
Custom token splitting utilities
Unicode character handling validation

Best Practices Demonstrated

The test suite exemplifies several testing best practices including isolated test cases, clear assertion patterns, and comprehensive edge case coverage. Each test focuses on a specific aspect of tokenizer functionality while maintaining clear separation of concerns.

Notable practices:

Systematic validation of tokenizer properties
Comparative testing of different tokenizer configurations
Explicit handling of multilingual scenarios

openai/whisper

tests/test_tokenizer.py

            
import pytest

from whisper.tokenizer import get_tokenizer


@pytest.mark.parametrize("multilingual", [True, False])
def test_tokenizer(multilingual):
    tokenizer = get_tokenizer(multilingual=False)
    assert tokenizer.sot in tokenizer.sot_sequence
    assert len(tokenizer.all_language_codes) == len(tokenizer.all_language_tokens)
    assert all(c < tokenizer.timestamp_begin for c in tokenizer.all_language_tokens)


def test_multilingual_tokenizer():
    gpt2_tokenizer = get_tokenizer(multilingual=False)
    multilingual_tokenizer = get_tokenizer(multilingual=True)

    text = "다람쥐 헌 쳇바퀴에 타고파"
    gpt2_tokens = gpt2_tokenizer.encode(text)
    multilingual_tokens = multilingual_tokenizer.encode(text)

    assert gpt2_tokenizer.decode(gpt2_tokens) == text
    assert multilingual_tokenizer.decode(multilingual_tokens) == text
    assert len(gpt2_tokens) > len(multilingual_tokens)


def test_split_on_unicode():
    multilingual_tokenizer = get_tokenizer(multilingual=True)

    tokens = [8404, 871, 287, 6, 246, 526, 3210, 20378]
    words, word_tokens = multilingual_tokenizer.split_tokens_on_unicode(tokens)

    assert words == [" elle", " est", " l", "'", "\ufffd", "é", "rit", "oire"]
    assert word_tokens == [[8404], [871], [287], [6], [246], [526], [3210], [20378]]