Guide: Transcribing Audio with Whisper via OpenAI and Groq (SAPAT)

authors/benjamin_eikrem.md

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+Author: Benjamin Eikrem
+Title: Software Developer
+Description: Benjamin Eikrem is a developer who writes technical guides and tooling documentation, with an interest in AI engineering workflows and developer tooling. This guide was researched and drafted with AI assistance (Claude/Fable), grounded in a direct read of the source repository it documents.
+Company Name:
+Company Description:
+Author Image: <https://avatars.githubusercontent.com/u/51953066?v=4>
+Company Logo Dark:
+Company Logo White:

definitions/20260905_definition_speech-to-text.md

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+---
+title: 'Speech-to-Text (STT)'
+description:
+  'The process of converting spoken audio into written text, typically using
+  a trained acoustic/language model such as OpenAI Whisper.'
+date: 2026-09-05
+author: 'Benjamin Eikrem'
+---
+
+# Speech-to-Text (STT)
+
+## Definition
+
+Speech-to-text (STT) is the process of automatically converting spoken audio
+into written text. Modern STT systems are neural networks trained on large
+paired datasets of audio and matching transcripts. Rather than using
+hand-built acoustic rules, they learn to map raw or preprocessed audio (most
+commonly a log-mel spectrogram — a time/frequency representation of the
+sound) directly to sequences of text tokens.
+
+Not all STT models share the same architecture. Encoder-decoder transformer
+models such as OpenAI's Whisper generate text autoregressively, one token at
+a time, conditioned on the audio. CTC-based models (Connectionist Temporal
+Classification), such as NVIDIA's Parakeet, instead predict a character or
+token for each audio frame directly and collapse repeated/blank predictions
+into the final transcript — a different, generally faster but historically
+less flexible approach for handling variable-length alignment between audio
+and text.
+
+## Context and Usage
+
+STT underpins most transcription tooling: meeting-recording transcripts,
+podcast show notes, voice assistants, subtitle generation, and call-center
+analytics all depend on it as a first step before any text-based processing
+(search, summarization, sentiment analysis) can happen.
+
+In practice, "STT" and "the model behind it" are often conflated. A CLI or
+SDK might expose one uniform `transcribe()`-style interface across many
+providers, but the acoustic model actually doing the work can differ
+significantly between them — some are literally OpenAI's open Whisper
+weights hosted on different infrastructure (OpenAI's own API, Azure OpenAI,
+Groq's LPU hardware, or run entirely offline via `whisper.cpp`), while
+others are unrelated proprietary models that simply expose a
+Whisper-compatible request/response shape for drop-in compatibility. When
+comparing STT providers on accuracy or latency, it's worth checking which
+underlying model each one actually runs rather than assuming "Whisper API"
+always means the same acoustic model.