> ## Documentation Index > Fetch the complete documentation index at: https://docs.cyberwave.com/llms.txt > Use this file to discover all available pages before exploring further. # Log DJI Mini 3 observations by voice > Fly a DJI Mini 3 manually and report what you see, hands-free. A workflow turns the pilot's spoken observations into structured alerts on the drone twin — no autonomous flight, no code. **Early tutorial (stub).** The flow and node configuration are complete enough to build and test end-to-end. Screenshots and a shareable template link will be added as the template is published. **This workflow never flies the drone.** The pilot flies manually on the RC; the workflow only listens and raises alerts. We deliberately keep voice out of the flight-control loop — a spoken command should never move an aircraft. By the end of this tutorial you'll have a DJI Mini 3 whose pilot can call out what they see — *"pallet left by the north gate,"* *"person near the fence"* — and have each observation logged as a **structured alert** on the drone twin, hands-free, while both hands stay on the sticks. Built entirely in the visual Workflow editor, **no code.** This is the **voice-reporting sibling** of the [autonomous site sweep](/tutorials/dji-mini-3-site-sweep) (a VLM watches the feed and alerts on its own). Here a human watches and narrates; the workflow just captures it. ## The idea Speech is a fast, hands-free way to file an incident while flying. The workflow transcribes what the pilot says, a model decides whether it's actionable and classifies it, and only real observations become alerts — idle chatter is ignored. ```mermaid theme={null} flowchart LR A["Audio Track
mic"] --> B["Audio Assistant"] B --> C["Call Model
speech-to-text"] C --> D["Call Model
classify observation"] D --> E["JSON Parser"] E --> F["Conditional
actionable?"] F --> G["Send Alert
DJI Mini 3 twin"] ``` No loop, no controller, no drone command — the aircraft is never touched. ## Prerequisites * A **DJI Mini 3** twin, paired and streaming (see [DJI Mini 3 Site Sweep · Phase 1](/tutorials/dji-mini-3-site-sweep)). The pilot flies it manually on the RC as normal. * A **microphone** twin that streams audio (a headset mic on the pilot is ideal). *** ## Step 1: Create the workflow Create a workflow (*DJI Voice Alerts*) and add the **microphone** and **DJI Mini 3** twins. Wire nodes left to right; set inputs on the `#` (fixed) or `` (expression) tabs using `{node-name.output}`. *** ## Step 2: Capture the voice — Audio Track → Audio Assistant | Node | Field | Value | | --------------- | ------------- | --------------------- | | Audio Track | Twin | your microphone twin | | Audio Track | Buffer preset | `speech-to-text` | | Audio Assistant | `audio` | `{audio-track.audio}` | | Audio Assistant | Modality | `voice_assistant` | Speak and open **Executions** — a run fires and Audio Assistant shows `is_speaking: true` with a captured speech segment. *** ## Step 3: Transcribe — Call Model (speech-to-text) | Field | Mode | Value | | ------- | ----- | ----------------------------------------------------- | | `audio` | `` | `{audio-assistant.audio}` | | Model | — | a speech-to-text model (e.g. Faster Whisper Small EN) | Output: `result` = the transcript. *** ## Step 4: Classify the observation — Call Model (LLM) Add a second **Call Model** node (an LLM). Set **Prompt** to `` and paste the classifier below; the last line inlines the transcript. It decides whether the utterance is a real observation worth alerting on, and structures it. ``` You are an incident classifier for a drone site inspection. The pilot narrates what they see while flying. Turn a single spoken utterance into a JSON verdict. No chat, no markdown, no code fences. # Decide - should_alert: true only if the utterance describes a real, actionable observation (equipment left out, a hazard, an intrusion, damage, a person where they shouldn't be). false for idle chatter, self-talk, or flight banter ("okay", "battery's fine", "nice view"). - title: a short label (max ~6 words), e.g. "Pallet left by north gate". - severity: one of "info", "warning", "critical". - summary: one plain sentence describing the observation. # Output — exactly one JSON object, nothing else { "should_alert": true, "title": "Pallet by north gate", "severity": "warning", "summary": "A pallet was left out near the north gate." } # Rules - If should_alert is false, still return valid JSON with empty title/summary and severity "info". - NEVER output prose outside the JSON object. # Examples Utterance: "there's a ladder leaning on the fence by bay three" {"should_alert":true,"title":"Ladder on fence, bay 3","severity":"warning","summary":"A ladder is leaning on the fence near bay three."} Utterance: "okay looking good over here" {"should_alert":false,"title":"","severity":"info","summary":""} # The pilot's observation "{call-model.result}" ``` Output: `result` = the JSON verdict. *** ## Step 5: Read the verdict — JSON Parser | Field | Mode | Value | | --------------- | ----- | ----------------------- | | `json_data` | `` | `{call-model-2.result}` | | LLM fix enabled | — | on | *** ## Step 6: Alert only on real observations — Conditional | Field | Mode | Value | | ------------- | ----- | -------------------------------------- | | `left_value` | `` | `{json-parser.json_data.should_alert}` | | operator | — | `equal` | | `right_value` | `#` | `true` | Wire the **true** port → Send Alert, so idle chatter never raises an alert. *** ## Step 7: Raise the alert — Send Alert Wire **Conditional (true) → Send Alert** on the DJI Mini 3 twin, mapping the classifier's fields. | Field | Mode | Value | | -------- | ------ | ---------------------------------- | | Twin | picker | DJI Mini 3 | | Name | `` | `{json-parser.json_data.title}` | | Severity | `` | `{json-parser.json_data.severity}` | | Body | `` | `{json-parser.json_data.summary}` | | Category | `#` | `business` | The alert lands in the twin's **Alerts** panel with the pilot's observation, tied to the moment and the aircraft. *** ## Step 8: Test You can test this fully **without flying** — it's just voice in, alerts out. 1. Activate the workflow (Simulate or Live; the drone is never commanded either way). 2. Speak a real observation: *"there's a toolbox left out by the loading dock."* 3. Check **Executions** and the **Alerts** panel: | Node | Expect | | ----------------------- | ---------------------------------------------------- | | Call Model (STT) | `result` = your words | | Call Model (classifier) | `should_alert: true` with a title, severity, summary | | Conditional | `condition_met: true` | | Send Alert | a `warning` alert appears on the twin | 4. Speak idle chatter: *"okay, battery looks fine."* → `should_alert: false`, the Conditional blocks it, **no alert** fires. Tune the classifier's `should_alert` rules for your site so routine flight banter never trips an alert, and genuine hazards always do. ## Why voice stays out of flight The pilot's job is to fly and watch; the workflow's job is to remember and report. Keeping voice on the **reporting** side means a misheard word can, at worst, create a spurious alert — never move the aircraft. The RC remains the only thing that flies the drone. ## Next steps Let a VLM watch the feed and alert on its own — no pilot narration. Chain an alert trigger to email or another integration. Pairing, telemetry, and the full command set. Every node used here, with inputs and outputs.