An alternative song lyrics generator works by leveraging various computational techniques, often based on machine learning models, natural language processing (NLP), or rule-based algorithms. The process can vary in complexity depending on the underlying system. Here's an overview of how it typically works:

1. Text Data Input

• 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.

2. Model Architecture

• 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.

3. Lyric Structure and Form

• 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.

4. Contextual Generation

• 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.

5. Training Data

• 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.

6. Post-Processing

• 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.

Example Flow:

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.

In essence, an alternative song lyrics generator synthesizes new lyrics based on patterns it has learned from a broad set of lyrical data, often personalized by user input.