Rapid specification capture with voice
Speak your requirements and ideas naturally. This is the first step in your specification workflow: capture ideas quickly with voice, then refine them manually with AI-powered prompts. The fastest way to capture initial specifications before refinement.
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.
Per-Project Configuration
Set project defaults. Your team shares sensible defaults.
Terminal Dictation
Dictate commands directly to your terminal session.
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.
Real Use Cases
Capture ideas hands-free
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.
Dictate while reviewing code
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.
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.
Terminal commands without memorizing syntax
Need a complex git command with flags you always forget. Dictate it naturally, let transcription handle the syntax.
Commands entered correctly, faster than looking up documentation.
Frequently Asked Questions
Everything you need to know about PlanToCode
Refine Your Captured Specifications
Voice transcription is the first step in our Specification Capture workflow. Once you've captured your requirements, use AI-powered prompts to transform rough transcripts into clear, implementation-ready specifications.
Text Enhancement
Polish grammar, improve clarity, and enhance readability while preserving your original intent.
Task Refinement
Expand descriptions with implied requirements, edge cases, and technical considerations.
Start Capturing Specifications with Voice
From voice to refined specifications, seamlessly. Capture requirements hands-free, then refine with AI prompts. This is how corporate teams should capture and clarify requirements.