AI Transcription with Sapat Inside a Daytona Workspace

authors/notdev_ng.md

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+Author: Notdev Ng
+Title: Software Engineer
+Description: Notdev Ng is a software engineer focused on developer tooling,
+reproducible development environments, and applied AI workflows. He writes
+practical guides that help teams turn messy local setups into containers anyone
+can run, and he contributes to open-source projects around developer
+experience.
+Author Image: ![notdev-ng](./assets/notdev-ng.jpg)
+Author LinkedIn: [LinkedIn](https://www.linkedin.com/in/notdevng)
+Author Twitter: [Twitter](https://twitter.com/notdevng)
+Company Name: Codiev
+Company Description: Codiev builds AI-assisted development tools that help
+engineers turn ideas into working software faster.
+Company Logo Dark: ![company-logo dark](./assets/notdev-ng-company-dark.png)
+Company Logo White: ![company-logo white](./assets/notdev-ng-company-white.png)

definitions/20260920_definition_automatic_speech_recognition.md

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+---
+title: 'Automatic Speech Recognition (ASR)'
+description:
+  'Automatic Speech Recognition is the technology that converts spoken audio
+  into written text using machine-learning models trained on speech data.'
+date: 2026-09-20
+author: 'Notdev Ng'
+---
+
+# Automatic Speech Recognition (ASR)
+
+## Definition
+
+Automatic Speech Recognition (ASR) is the technology that converts spoken
+language in an audio signal into written text. Modern ASR systems are built on
+neural networks — most commonly encoder-decoder Transformers — that map an
+audio representation, such as a log-Mel spectrogram, to a sequence of text
+tokens. Well-known ASR models and services include OpenAI's Whisper family,
+ElevenLabs Scribe, Speechmatics, and Google Gemini's audio transcription.
+
+## Context and Usage
+
+ASR sits at the front of almost every voice-driven workflow: meeting and
+interview transcription, video captions and subtitles, voice assistants,
+call-center analytics, and making audio archives searchable. A typical pipeline
+first normalizes the audio (resampling to a fixed sample rate, usually mono),
+then runs the recognition model, and optionally post-processes the output for
+punctuation, casing, or speaker labels.
+
+For developers, the practical concerns are accuracy (often measured as word
+error rate), language and accent coverage, latency, file-size limits on hosted
+APIs, and whether audio can leave the machine at all. Local engines such as
+Vosk or whisper.cpp trade some accuracy for complete privacy and zero marginal
+cost, while hosted APIs offer state-of-the-art accuracy without local model
+management. Tools like Sapat wrap many ASR providers behind one CLI so the
+choice becomes configuration rather than integration work.