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• Tokenization: Breaking down the lyrics into smaller units like words or phrases.
• Normalization: Converting words to their base forms, dealing with different forms of the same word (e.g., "run" and "running").
• Language Handling: Since K-pop songs mix languages, the system needs to handle both Korean (Hangul script) and English.

• Language Understanding: The model learns how sentences are constructed, how themes like love, youth, or self-expression are discussed, and how emotions are conveyed.
• Pattern Recognition: K-pop lyrics often follow particular structures, such as verse-chorus-bridge. The model picks up on these structures to create coherent lyrics.

• Theme-based Input: The user might specify a theme like "love" or "empowerment." The generator would then use this theme to craft relevant lyrics.
• Seed Words or Phrases: Users can input specific words or phrases (e.g., "heartbreak," "shine bright"), and the model will build the song around those cues.
• Style Adaptation: If the generator has been trained on specific K-pop artists or subgenres (like ballads, hip-hop, or bubblegum pop), it can adapt the style and tone accordingly.

• Grammar and Syntax Correction: Since models can sometimes produce imperfect sentences, post-processing ensures the lyrics flow naturally.
• Rhyming: If necessary, the generator can tweak words to maintain rhyme schemes or meter typical of K-pop songs.
• Language Polishing: Ensuring that mixed language usage (Korean and English) is fluid and appropriate.

• Length of the song: Whether they want a short chorus or a full verse.
• Mood or emotion: Whether they want something upbeat or melancholic.
• Artists' Style: Some advanced models let users specify a style similar to a particular K-pop group or artist.

1. Recognise the theme (self-confidence) and the mood (bright).
2. Generate a structure (verses and chorus) that matches typical K-pop formats.
3. Create lyrics that convey self-confidence using upbeat language, integrating English where it fits.
4. Post-process the lyrics to ensure coherence, appropriate rhymes, and smooth language transitions.

• Melody suggestions: Provide rough melody ideas based on the lyrics.
• Integration with music production: Allowing musicians to turn generated lyrics into fully produced songs with music generation tools.

• Seed Text or Themes: Many generators allow users to input a seed word, phrase, or a set of themes (e.g., love, heartbreak, nature, rebellion). This helps guide the style and topic of the lyrics.
• Style or Genre Selection: Some generators allow users to select a specific music genre (like alternative rock, indie, folk, etc.), which influences the tone, vocabulary, and structure of the output lyrics.

• Pre-trained Language Models: Many modern generators use large pre-trained language models like GPT (Generative Pre-trained Transformer), which have been trained on massive datasets of text. These models are fine-tuned on specific lyric data, such as alternative rock songs, to better mimic that genre.
• Markov Chains: Simpler generators might use a Markov chain approach, where the likelihood of a word or phrase following another is calculated based on training data, creating coherent sequences based on probabilities.

• Verse, Chorus, Bridge: Some advanced generators try to emulate typical song structures. The model can be programmed to generate a sequence of verses, choruses, and bridges by recognizing patterns in existing lyrics.
• Rhyming and Meter: Lyric generation models can be optimized to maintain rhyming patterns (e.g., ABAB or AABB), rhythm, or syllable count to ensure the lyrics are singable or have the poetic flow found in music.

• Word Embeddings: NLP models represent words as vectors in a high-dimensional space, where words with similar meanings are close to each other. This allows the generator to select contextually appropriate words, keeping the theme or emotional tone intact.
• Attention Mechanism: More advanced generators use attention mechanisms to focus on specific parts of the input seed text, ensuring coherent and contextually relevant lyrics throughout the song.

• Lyrics Databases: The model is trained on a large dataset of existing song lyrics, particularly in the alternative genre. By analyzing these examples, the model learns common themes, structures, and word choices used in alternative music.
• Fine-Tuning: For genre-specific lyrics, the generator is often fine-tuned on a curated dataset of lyrics from a specific genre, like alternative or indie rock, to make the generated text sound authentic to that style.

• Filtering for Coherence: After generating a raw draft of lyrics, some systems apply filters to ensure the output is coherent, grammatically correct, and stylistically consistent. This step helps eliminate nonsensical or repetitive lines.
• User Refinement: Some generators let users refine the output by tweaking the style or theme, regenerating specific sections, or selecting multiple output options.

1. The user inputs a seed phrase like "lost in the city."
2. The model recognizes the genre is alternative and the theme is urban isolation.
3. The generator uses a pre-trained model (like GPT-3) to generate verses, ensuring words like "streets," "lights," and "alone" stay contextually relevant.
4. The generator ensures a rhyme scheme (e.g., ABAB) is followed for lyrical flow.
5. Output: Verses and chorus about wandering through empty streets, feeling disconnected from the world.