Do YAML Files Belong in Transcription Workflows?

When you think about transcription output formats, JSON, SRT, and plain text files immediately come to mind. But lately I've been wondering: what about YAML? It feels like YAML has quietly taken over configuration management across the tech industry—from Docker Compose to Kubernetes manifests to CI/CD pipelines. Yet when it comes to transcription, YAML is conspicuously absent. Is there a good reason for that, or are we missing an opportunity?

What YAML Actually Does Well

YAML (YAML Ain't Markup Language) excels at one thing above all: human-readable configuration. Unlike JSON with its strict syntax and mandatory quotes, YAML feels almost like reading an outline. As Red Hat points out, "YAML is a popular programming language because it is optimized for data serialization, formatted dumping, configuration files, log files, and internet messaging."

The rise of YAML in DevOps isn't accidental. When AWS compares YAML to JSON, they emphasize that YAML's syntax is "close to natural language" and accessible even to non-technical personnel. That readability advantage matters when you're managing complex infrastructure—or, potentially, when you're managing complex transcription metadata.

The Emerging AI Connection

Something interesting is happening in the AI and LLM space that makes YAML worth reconsidering for transcription workflows. Tools like Pathway now let developers "build Large Language Model (LLM) Apps using YAML configuration files" to create production-ready RAG pipelines without writing code. Microsoft's Semantic Kernel includes a YAML schema specifically for prompts, and Azure Machine Learning uses YAML to define prompt flow integrations with DevOps.

Why does this matter for transcription? Because modern transcription increasingly isn't just about converting speech to text—it's about feeding that text into AI pipelines for analysis, summarization, and transformation. If those pipelines are already configured in YAML, having transcript metadata in the same format creates natural integration points.

Speaker Diarization: A Use Case That Almost Makes Sense

Consider speaker diarization—the process of identifying "who spoke when" in a recording. Amazon Transcribe returns speaker labels with timestamps in JSON. Google Cloud Speech-to-Text tags each word with speaker numbers. But there's also the RTTM format (Rich Transcription Time Marked), developed by NIST specifically for annotating speaker segments.

RTTM is powerful but arcane—10 space-separated fields per line, strict formatting requirements, essentially a data format that only machines love. YAML could represent the same speaker metadata far more readably:

transcript:
  speakers:
    - id: SPEAKER_01
      name: Sarah Chen
      role: Product Manager
      segments:
        - start: 0.0
          end: 15.3
          text: "Let's start by reviewing last quarter's metrics."
        - start: 45.2
          end: 67.8
          text: "The conversion rate improved by 23 percent."

This is infinitely more readable than RTTM, easier to version control than JSON, and straightforward to edit manually. But here's the problem: nobody's asking for it.

The Honest Assessment

After researching how YAML is actually being used in 2025, I've come to a conclusion that might disappoint YAML enthusiasts: transcription probably doesn't need YAML files, at least not for primary output.

JSON dominates for a reason. Every programming language parses it effortlessly, APIs consume it naturally, and it's compact enough for network transmission. As Leapcell's comparison of data formats notes, JSON remains "the de facto standard for web APIs" because of its universal compatibility.

The places where YAML shines—configuration management, infrastructure as code, CI/CD pipelines—aren't where transcript data lives. Transcript data gets processed, analyzed, stored in databases, and passed between services. Those workflows need speed and reliability more than human readability.

Where YAML Might Actually Help

That said, there's one scenario where YAML could add value: configuring transcription processing pipelines.

Imagine a YAML file that defines how a transcript should be processed:

processing:
  speakers:
    identify: true
    minimum_segments: 2
  formatting:
    remove_filler_words: true
    add_punctuation: true
  ai_analysis:
    - extract_action_items
    - generate_summary
    - identify_key_decisions
  output_formats:
    - json
    - srt
    - docx

This makes sense. You're not storing transcript content in YAML, you're storing instructions for how to handle that content. It's configuration, which is exactly what YAML was designed for.

The yamllm-core library demonstrates this approach beautifully for LLM workflows, letting developers "define various aspects of your LLM prototypes, from model selection and parameters to memory management and output formatting, all within a human-readable YAML configuration."

The Verdict

YAML files don't belong in transcription output. JSON handles that job better—it's faster to parse, universally compatible, and well-established in the transcription ecosystem. But YAML absolutely has a role in configuring how transcripts get processed, especially as transcription workflows increasingly integrate with AI analysis pipelines.

The question isn't whether YAML can store transcript data—it can. The question is whether it should. And for primary transcript storage, the answer is probably no. But for making complex processing workflows readable and maintainable? That's where YAML might finally earn its place in the transcription stack.

Sometimes the right tool isn't the most capable one—it's the one that fits naturally into how people actually work.