Building a Swahili Tokenizer - The Foundation of Msingi1 and Sauti Ya Kenya

Kiplangat Korir
Kiplangat Korir

Why a Swahili-Specific Tokenizer Matters

Swahili is a rich and expressive language spoken by millions across East Africa. However, existing language models often struggle with its unique morphology, leading to poor performance in NLP applications. Many models are trained on datasets that do not fully capture the structure of Swahili words, resulting in unnatural translations, misinterpretations, and incorrect pronunciations in Text-to-Speech (TTS) systems.

To address this, MsingiAI has developed a specialized Swahili tokenizer as part of our Msingi1 language model and Sauti Ya Kenya, our Text-to-Speech (TTS) initiative. This tokenizer is designed to better capture the linguistic patterns of Swahili, ensuring more accurate AI-powered communication tools.

How Our Swahili Tokenizer Works

Our tokenizer is based on Byte-Pair Encoding (BPE) and has been trained on over 1.4 million Swahili words to identify the most common linguistic patterns. Unlike generic tokenizers, which often break Swahili words in unnatural ways, our model:

  • Preserves common prefixes and suffixes (e.g., "ku-", "wa-", "ni-")
  • Maintains word boundaries using a special marker ("Ġ")
  • Optimizes for subword tokenization to handle agglutinative structures
  • Recognizes proper nouns and diacritics for accurate text representation

Real-World Tokenization Examples

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Here's a look at how our tokenizer processes Swahili sentences:

Example 1: Simple Greeting

Original text: Habari ya leo?
Tokens: ['H', 'a', 'bari', 'Ġya', 'Ġleo', '?']
Decoded text: Habari ya leo?

Preserved "bari" as a unit rather than splitting it into "H", "a", and "bari" separately.
Recognized "ya" and "leo" as distinct words rather than merging them incorrectly.

Example 2: Common Conversation Sentence

Original text: Ninafurahi kukutana nawe.
Tokens: ['N', 'ina', 'furahi', 'Ġkukutana', 'Ġnawe', '.']
Decoded text: Ninafurahi kukutana nawe.

Recognized "ina" as an important verb prefix, which is crucial for proper conjugation.
Kept "kukutana" and "nawe" intact instead of breaking them into meaningless segments.

Example 3: Proper Nouns and Punctuation

Original text: Karibu Tanzania, nchi nzuri.
Tokens: ['K', 'a', 'ribu', 'Ġ', 'T', 'a', 'nzania', ',', 'Ġnchi', 'Ġnzuri', '.']
Decoded text: Karibu Tanzania, nchi nzuri.

Recognized "Tanzania" as a proper noun while breaking it down logically.
Preserved punctuation marks and spacing correctly.

Why This Matters for Msingi1 and Sauti Ya Kenya

Having a well-optimized tokenizer is a critical step in training better Swahili language models and TTS systems. Our tokenizer ensures that:

  • Msingi1 (Swahili Language Model) can generate fluent Swahili text with accurate grammar.
  • Sauti Ya Kenya (TTS System) can produce natural, accent-free Swahili speech by understanding phonetic structures.

What's Next?

Now that our tokenizer is working correctly, we are integrating it into the training process for Msingi1 and Sauti Ya Kenya. We'll continue optimizing it for:

  • Dialect variations
  • Low-resource words
  • Real-world conversational patterns

This is just the beginning! Stay tuned for updates as we refine our Swahili NLP models and bring more inclusive AI tools to the African tech ecosystem.

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