feat: add Google Gemini AI transcription guide for Sapat (#13)

authors/zeroknowledge0x.md

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+Author: zeroknowledge0x Title: AI Engineer Description: Open-source contributor focused on AI tooling, developer experience, and reproducible workflows. Interested in speech-to-text pipelines, LLM orchestration, and containerized development environments. Author Image: Author LinkedIn: Author Twitter: Company Name: Independent Company Description: Independent open-source contributor. Company Logo Dark: Company Logo White:

definitions/20260618_definition_multimodal_transcription.md

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+---
+title: 'Multimodal Transcription'
+description: 'Using multimodal AI models to transcribe audio by combining speech recognition with natural language understanding.'
+date: 2026-06-18
+author: 'zeroknowledge0x'
+---
+
+# Multimodal Transcription
+
+## Definition
+
+Multimodal transcription is the process of converting audio to text using
+general-purpose multimodal AI models rather than dedicated speech-to-text
+systems. Instead of relying solely on acoustic models trained on speech data,
+multimodal transcription sends audio alongside text instructions to a large
+language model capable of processing both modalities. This approach enables
+combined workflows where transcription, summarization, translation, or
+structured extraction happen in a single API call.
+
+## Context and Usage
+
+Traditional speech-to-text services like Whisper or Deepgram use encoder-decoder
+architectures specifically trained on audio data. They excel at accurate
+word-level transcription but operate as single-purpose tools: audio in, text
+out.
+
+Multimodal models like Google Gemini, GPT-4o, and Claude accept audio as part
+of a broader conversation context. An AI engineer can attach an audio file and
+a prompt such as "Transcribe this meeting and extract action items," receiving
+both the transcript and structured output in one response.
+
+This paradigm is gaining adoption in AI engineering pipelines because it
+reduces the number of API calls, simplifies orchestration, and allows
+contextual instructions to improve transcription quality for domain-specific
+content. Tools like [Sapat](https://github.com/nibzard/sapat) support
+multimodal providers (e.g., `--provider gemini`) alongside traditional ones,
+letting teams choose the right approach for each use case.
+
+Trade-offs include higher per-token costs, larger payload sizes (audio must be
+base64-encoded inline), and less fine-grained control over acoustic model
+parameters compared to dedicated speech-to-text APIs.