Voice transcription for specification capture and terminal control
Dictate tasks while thinking aloud → clean structured requirements. Dictate terminal commands while keeping eyes on code. Voice transcription respects per-project settings and integrates with text_improvement + task_refinement prompts.
Why Voice Accelerates Specification Capture
Capture ideas before they fade
Stakeholders think faster than they type. Requirements and context get lost while fingers catch up. Voice lets you capture the complete specification before critical details fade.
Hard to describe while hands are busy
Reviewing code? Debugging? Drawing architecture diagrams? Your hands are occupied but you need to log the task. Voice transcription keeps you in flow.
Context switching kills momentum
Stop what you are doing to open a note app, type, then return. Every switch breaks concentration. Voice stays in the same workspace.
Key Capabilities
Multiple Language Support
OpenAI transcription supports multiple languages. Respects per-project settings configured in your workspace.
Per-Project Configuration
Set project defaults for language, temperature, and transcription model. Integrates with text_improvement and task_refinement prompts.
Terminal Dictation (Stage 5)
Dictate commands directly to your terminal session (Stage 5). Keep your eyes on code while controlling the terminal with your voice.
Accuracy Benchmarks
Accuracy Benchmarks
What is Word Error Rate (WER)?
WER = (Substitutions + Deletions + Insertions) / Reference words. Lower is better.
- Substitution: a word is transcribed incorrectly
- Deletion: a word is omitted
- Insertion: an extra word is added
In technical workflows, small WER differences can flip flags, units, or constraints—creating ambiguous tickets and rework. High accuracy preserves intent and enables precise, implementation-ready specifications.
gpt-4o-transcribe shows the lowest WER in this benchmark. Even a 1–2% absolute WER reduction can remove multiple mistakes per paragraph.
About these models
- OpenAI gpt-4o-transcribe — advanced multilingual speech model optimized for accuracy and latency.
- Google Speech-to-Text v2 — cloud speech recognition by Google.
- AWS Transcribe — managed speech recognition by Amazon Web Services.
- Whisper large-v2 — open-source large-model baseline for comparison.
Bottom line: Fewer errors mean fewer ambiguous tickets and less rework. gpt-4o-transcribe helps teams capture precise, implementation-ready specifications on the first try.
Illustrative Example: Capturing Specifications
Illustrative Example: Capturing Specifications
OpenAI gpt-4o-transcribe
Create a Postgres read-replica in us-east-1 with 2 vCPU, 8 GB RAM, and enable logical replication; set wal_level=logical and max_wal_senders=10.
Competitor Model
Create a Postgres replica in us-east with 2 CPUs, 8GB RAM, and enable replication; set wal level logical and max senders equals ten.
Errors — Substitutions: 9, Deletions: 0, Insertions: 8. Even a few errors can invert flags or units.
Impact: Mishearing "read-replica" as "replica", dropping region suffix "-1", or changing "wal_level=logical" can lead to incorrect deployments or data flows.
Voice Transcription Across Stages
Stage 1: Dictating Tasks
Dictate tasks while thinking aloud. Raw voice input captures complete requirements - mental models, constraints, context - without the cognitive load of typing.
Stage 5: Terminal Voice Control
Dictate terminal commands while reviewing code, monitoring logs, or analyzing diffs. Keep your eyes on what matters while controlling execution.
Integration with Text Enhancement Prompts
Voice respects per-project language and temperature settings. Transcripts feed into text_improvement for grammar polish and task_refinement for completeness expansion.
Real Use Cases
Capture ideas hands-free (Stage 1)
You are deep in a debugging session. You spot three related issues that need fixing. Speak them into the voice recorder without leaving your terminal.
Ideas logged instantly. Return to debugging without breaking flow. Polish transcripts with text_improvement.
Dictate while reviewing code (Stage 1)
Code review reveals a refactoring opportunity. Your hands are on the diff, eyes on the screen. Voice captures the task description.
Task created with full context, zero typing, no context switch. Ready for text_improvement polish.
Faster task entry for repetitive work
You have 10 similar bugs to log after QA testing. Typing each one takes 2 minutes. Voice transcription takes 20 seconds.
10x faster task entry. QA feedback processed in minutes instead of hours. Refine with task_refinement before file discovery.
Terminal commands without looking away (Stage 5)
Monitoring build output when you need a complex docker command. Dictate it while watching the logs - terminal inserts it correctly.
Commands entered correctly while eyes stay on logs. Stage 5 terminal control without context switching.
Frequently Asked Questions
Everything you need to know about PlanToCode
Refine Voice Transcripts Before File Discovery
Voice transcription is Stage 1 input in Intelligence-Driven Development. After capturing raw thoughts, text_improvement cleans grammar and task_refinement expands completeness - preparing specs for Stage 2 file discovery.
Text Enhancement (Stage 1)
Polish grammar, improve clarity, and enhance readability while preserving your original intent. Makes voice transcripts professional.
Task Refinement (Stage 1 → 2)
Expand descriptions with implied requirements, edge cases, and technical considerations. Prepares specs for FileFinderWorkflow.
Related Features
Discover more powerful capabilities that work together
Voice to Terminal Commands
Speak naturally, execute precisely. No more typing complex commands.
Learn moreMulti-Model Planning Synthesis
Get the best insights from GPT-5.2, Claude, and Gemini combined
Learn moreStart Capturing Specifications with Voice
Stage 1 specification capture with voice, then refine with AI. Stage 5 terminal control while reviewing code. Voice transcription bridges thinking and execution across the workflow.