# Building in Dify: from recording to minutes **Guide 5 for the AI Agents workshop** LeX Consultancy B.V. · 20 September 2026 You build a workflow that turns an audio recording of a meeting into text and writes minutes from it: a summary, decisions, action points with owner and date, and what was deferred. At the end you have a web address where a colleague uploads an mp3 and gets the minutes and the full transcript back within two minutes. Compared with guides 1 and 3 two things are new: a **file as input** instead of text, and a **tool node** (Speech To Text) instead of only LLM nodes. The rest you know. This guide follows the cloud version of Dify as it looked on 20 September 2026. Dify changes fast: if your screen differs, follow what you see, not what is written here. The screenshots show the Dutch build of this app, tested with a Dutch recording; the screens and clicks are the same. **How to read this guide** | Form | Meaning | | --- | --- | | 1. 2. 3. | What you do: click, choose, open | | **Bold** | A button, menu or field as it appears on your screen | | Table *What you type* | Values you put in a form field | | Grey box *Type in* | Text you type or paste literally | | Orange box | A pitfall you would otherwise discover yourself | | Green box *Your choice* | A place where you can replace our example with your own material, style or prompt | --- ## The design card for this app Before we started clicking, this card was filled in (the blank card is in the Design card appendix). Fill in the same five boxes for your own app; the loose version of this filled-in card is in 00-design-card/examples. | 1. Who is it for | 2. What goes in | | --- | --- | | The minute-taker of a department meeting whose minutes are always two weeks late. Has a 45-minute mp3 and fifteen minutes of time. | One audio file (mp3, m4a or wav, at most 5 MB) through a file field. For the prototype only a fictional recording read out by a computer voice. | | 3. What comes out | 4. What it must stick to | | --- | --- | | Minutes with five headings (Meeting, Summary, Decisions, Action points with owner and date, Deferred) and below them the full transcript. Uncertain names with a question mark. | No real recordings in Dify cloud (privacy frame); delete audio after transcription; someone reads the minutes. Tool: Speech To Text with your own OpenAI key. | **5. How you know it works** | Test | What goes in | What must come out | | --- | --- | --- | | 1 | The fictional three-minute recording | Minutes with five headings, three action points with an owner | | 2 | The same recording at 128 kbps (too big) | An error about the file size; shrink to 32 kbps | | 3 (the hard one) | A photo instead of audio | The input field refuses the file; only Audio is enabled | ## Read this first: what is and is not allowed A voice is personal data. So these agreements apply, and they are not up for discussion: | What | Agreement | | --- | --- | | Recordings of real people in Dify cloud | **No.** Dify cloud is normally not covered by your school's data processing agreement. This guide works with a fictional recording read out by a computer. Use that, or make one yourself (appendix A). | | Real use | Only on a platform your school has approved for personal data (a data processing agreement, processing in your jurisdiction). Dify can run on such a platform too, but that is a separate set-up outside this guide. | | Recording a meeting | Explicit consent from all participants beforehand. Structural recording: inform the staff council or works council. | | Retention | Delete the audio as soon as the transcript exists. Dify keeps uploaded files and results; see "Housekeeping" at the end. | | Students | A scenario with speaking tests or student conversations is only allowed after a data protection impact assessment and after informing parents. This guide is only about staff meetings. | | Accessibility | If the tool is used for students: for all students, not only for students with a disability. | In short: what you build here is a working prototype to learn how it works. The step to real use is a separate decision with a separate environment. --- ## Preparation - A Dify account with your own OpenAI key, set to **Usage priority: API key** (Integrations → Model Provider → OpenAI → Config). Without your own key the Speech To Text tool does not work: Dify's free credits only cover text models. - A test recording. Two options: run `make_recording.py` in the folder of this guide with your OpenAI key (it reads the English script `department-meeting-script.txt` with a computer voice and produces `department-meeting-small.mp3`), or use `sample-meeting-dutch-small.mp3`, the Dutch recording from the screenshots; the tool transcribes Dutch just as well and the minutes come out in the language of your prompt. - Cost per run through your own key: transcription about 2 cents, minutes less than 1 cent. --- ## Step 1. Create the workflow 1. Go to **Studio**, click **Create** and choose **Create from Blank**. 2. Click **Workflow**. 3. Fill in the name. | Field on screen | What you type | | --- | --- | | App Name & Icon | `Transcription tool` |  4. Click **Create**. > A sandbox account may have five apps, and agents in the Agents menu count. If you see "Upgrade to create more apps", first delete an old app via the three dots on its card in Studio. ## Step 2. Create an input field for the audio file 1. Click **User Input** in the right-hand panel. 2. Click the plus sign next to **INPUT FIELD**.  3. Open **Field Type**. You see the kinds of fields.  4. Choose **Single File**. The window gets longer: file types appear.  5. Fill in the fields. | Field on screen | What you type or choose | | --- | --- | | Variable Name | `recording` | | Label Name | `Recording of the meeting (mp3, m4a or wav)` | | Support File Types | switch **Image** off, switch **Audio** on | | Upload File Types | **Local Upload** | > Image is on by default. Switch it off, otherwise someone can upload a photo later and the workflow gets stuck at the transcription step.  6. Click **Save**.  ## Step 3. Add the transcription step This is the new building block: not an LLM node but a **tool**. Dify has a built-in Audio tool that turns speech into text through the speech model of your provider. 1. Under **NEXT STEP** click **SELECT NEXT STEP**. 2. At the top of the list click the **Tools** tab.  3. Under **All tools** click **Audio**. Two options unfold.  4. Choose **Speech To Text**.  5. Under **Audio File** click **Set variable** and choose the variable **recording** under USER INPUT.  6. Under **Model** click the drop-down. You see the speech models available through your key.  7. Choose **gpt-4o-transcribe-diarize**. "Diarize" means the model separates speakers; that helps with a meeting. If you want cheaper, choose **whisper-1**.  ## Step 4. Add the minutes step 1. Click **SELECT NEXT STEP** and under **Nodes** choose the **LLM** node.  2. Click the model name **gpt-5** and choose **gpt-5-mini**. 3. Click in the **SYSTEM** field and type the instruction. ``` Start your answer with the line # MINUTES followed by an empty line. You write the minutes of a meeting at a school, based on the transcript below. The transcript comes from automatic speech recognition and may contain errors in names and numbers; write down what is most likely and put a question mark in brackets after anything you are not sure of. Write in English, businesslike and brief. Use exactly this structure with these headings: ## Meeting One line: which meeting, date, those present (if the transcript shows it). ## Summary At most five sentences about what was discussed. ## Decisions Numbered list. Only what was really decided, no intentions. ## Action points Table with columns: What, Who, When. If no date is given, write "not mentioned". ## Deferred Items deliberately moved to a later meeting, with the reason. Leave out side tracks and informal remarks. Invent nothing that is not in the transcript. Transcript: ``` 4. Right after "Transcript:" type a forward slash `/`. At the top of the list is now **SPEECH TO TEXT** with the variable **text**.  5. Choose **text** under SPEECH TO TEXT. 6. Click the name **LLM** at the top of the panel, replace it with `Minutes` and press Enter.  > **Your choice: the structure of the minutes.** The headings in the prompt are the structure a department meeting often has. A staff council meeting may want "Advice and consent", a team day wants "Agreements" and "Parking lot", a student review wants a block per student (but that is the scenario that needs an impact assessment first). Change only the headings and their explanation; keep the rules above them (speech recognition errors, invent nothing). Want two versions, for example full minutes and a five-line message for the team? Add a second LLM node with the same variable **text** as input. > The rule "put a question mark in brackets after anything you are not sure of" is not decoration. In our test the speech recognition heard "Karin" once as "Karen" and once as "Karin", and "Department meeting" once came out garbled. The minutes neatly added "(Karen?)". A person still has to check that; the tool makes visible where. ## Step 5. Decide what comes out 1. Click **SELECT NEXT STEP** and choose **Output**.  2. Click the plus sign next to **OUTPUT VARIABLE** and create two variables. | Variable name (type) | Source (choose) | | --- | --- | | `minutes` | Minutes / text | | `transcript` | Speech To Text / text |   3. Close the panel and press Ctrl+1. You see the whole workflow: four nodes.  ## Step 6. Test with the recording 1. Click **Test Run** at the top right.  2. Click **Local upload** and choose your small test recording (`department-meeting-small.mp3` or `sample-meeting-dutch-small.mp3`). > Use the file with "small" in its name. A recording at 128 kbps is about 2.8 MB for three minutes and that is too big: the tool sends audio as text (base64) to the provider, which doubles the size, and the limit is 5 MB. You then get the error below.  Rule of thumb: **3 MB per recording**. Half an hour of meeting only fits if you shrink the recording first: mono, 16 kHz, 32 kbps gives about 250 kB per minute. Any audio program can do that, or ffmpeg: ``` ffmpeg -i recording.m4a -ac 1 -ar 16000 -b:a 32k recording-small.mp3 ```  3. Click **Start Run**. Transcribing three minutes of audio takes about a minute.   4. Read the result under **RESULT**.   Check against the script (`department-meeting-script.txt`) whether it is right. In our test: two decisions found, five action points with the right owners and dates, the reading scheme under "Deferred" with the reason, and the coffee machine neatly left out. ### Recognising speakers: what the model does and does not do The model is called gpt-4o-transcribe-**diarize**, and "diarisation" is telling speakers apart. To see what that gives you in Dify, we ran the same script read by three different computer voices (`make_recording.py --three`). 5. Run the workflow once more with a three-voice recording.  6. Afterwards look in **Logs** at the run, tab **TRACING**, and expand **SPEECH TO TEXT**. Under OUTPUT is what the tool returns: one field `text`, with a line break wherever another voice starts.  That is the honest answer: the model **splits** the text at speaker changes, but Dify's Speech To Text tool passes on **no labels** (Speaker 1, Speaker 2). Who said what is inferred by the Minutes node from the content, and that works well here because the opening names the people ("Present are Karen, Bram and Fatima") and because people address each other in a meeting. In a meeting without such an opening the action points end up as "not mentioned". Two more things stood out. The transcription of the three voices contains more errors than that of the single voice: the computer voices read with a slight accent and the speech model hears it. Real speakers do better. And the run time was the same, a good minute for three minutes of audio. > **Your choice: real speaker labels.** If you want "Karen:" and "Bram:" in the transcript, there are two routes. The easy one: put in the prompt of the Minutes node "The lines in the transcript are speaker changes. Put the speaker's name before each line if it is clear from the content, otherwise Speaker 1, 2, 3." The precise one: replace the Speech To Text tool with an **HTTP Request** node to OpenAI's speech API with `response_format=diarized_json`; that gives a speaker number and a time per sentence. That needs a key in the node (through an environment variable, never in the text) and is a next guide. ## Step 7. Publish 1. Click **Publish** at the top right and then **Publish**.  2. Open the menu again and click **Open webapp**.  3. Click **Local upload**, choose the small recording and click **Execute**. > After the upload the Execute button moves down a little. Check where it is before you click.     At the top right of the table there is a copy button and a download button. With those a colleague puts the action points straight into an email or a spreadsheet. The Dutch example from this guide: . --- ## Checking that it worked - The workflow has four nodes: User Input, Speech To Text, Minutes, Output - The input field accepts only audio (Image not ticked) - Test Run with the small recording gives minutes with five headings and below them the transcript - The webapp gives the same result, with MINUTES as heading - Uncertain names have a question mark in brackets ## When things go wrong | What you see | What is going on | | --- | --- | | `ServerlessPayloadTooLarge ... max_request_bytes=5242880` | The audio file is too big. Shrink to 32 kbps mono (step 6) | | Speech To Text: no model in the list | No OpenAI key of your own, or Usage priority is set to AI credits. Integrations → Model Provider → OpenAI → Config | | The workflow gets stuck at Speech To Text with a photo | Image is still on in the input field (step 2) | | The minutes mention people who were not in the meeting | The speech recognition misheard a name. Look in the transcript at the bottom; the right name is often there with a question mark | | Minutes and transcript run into each other | The line "Start your answer with the line # MINUTES" is missing from the instruction | | "Upgrade to create more apps" | Sandbox limit of five apps. Delete an old app via the three dots on its card | | An mp3 link from a test run gives `403 forbidden` | Download links for Dify files are valid for five minutes. Download straight away, or run the test again | | The transcript has no speaker names, only line breaks | That is how the Speech To Text tool works: diarize splits but does not label in Dify. See Recognising speakers at step 6 | | A message about temperature | The model does not support that parameter. Ignore it | --- ## Make it yours The recording is invented and the structure of the minutes is our choice. This chapter helps you rebuild the tool for your meetings, within the privacy frame at the start of this guide. From easy to hard. | Change | Where | Difficulty | What you get for it | | --- | --- | --- | --- | | Other headings in the minutes | Step 4, prompt | easy | Minutes in the shape you already use | | Language and tone (formal, informal, another language) | Step 4, prompt | easy | Minutes you can share without rewriting | | A different speech model (whisper-1 is cheaper) | Step 3, settings of the tool | easy | Lower cost; you lose the line breaks at speaker changes | | Speaker names in the transcript | Step 6, prompt of Minutes, or an HTTP Request node (see the green box under Recognising speakers) | medium | A transcript with "Karen:" and "Bram:" in front | | A second output: a short message for those who were absent | Extra LLM node after step 4, extra output variable in step 5 | medium | Two products from one recording | | Action points as a separate list you can copy to a task list | Extra LLM node with only the table | medium | Less retyping after the meeting | | An input field "type of meeting" that chooses the structure | Step 2 plus an IF/ELSE node or a prompt with conditions | medium | One tool for several kinds of meeting | | Recordings longer than 5 MB | Replace Speech To Text with an HTTP Request node to the speech API | hard | An hour of meeting in one go; see Housekeeping | | Running on your school's approved platform | Export DSL, import, reconnect the models | hard | The route that is compulsory for real recordings | **Three assignments** 1. **Your structure.** Take the last minutes someone in your team wrote by hand. Put its headings in the prompt of step 4, with one sentence of explanation per heading. Run the fictional recording again and compare with the handwritten minutes: what is missing, what is too much? 2. **Make your own test recording.** Write a fictional ten-minute script with your team about a real topic (without real names), and turn it into a recording via appendix A or `make_recording.py`. That way you test the tool on your jargon without a real voice ever entering Dify. 3. **Prepare the move.** Export the workflow (Export DSL) and write on half a page what is needed to run it on your school's platform: which model, which key, who the owner is, how long audio is kept. That half page is exactly what a data protection review asks of you. **Checklist before you use your own material** - [ ] No real recording goes into Dify cloud; only fictional or synthetic audio - [ ] For real recordings: the tool runs on your school's approved platform, and the participants have consented - [ ] Audio is deleted after transcription and it is recorded who checks that - [ ] The scenario with students (student reviews, conversations with students) waits for the impact assessment - [ ] Someone reads the minutes before they go round; the question marks in the minutes are there for that - [ ] There is one owner of the tool who maintains the prompt and republishes --- ## Housekeeping **What Dify keeps.** Every upload and every result is in the app's **Logs**, with the audio file attached. For the prototype with the fictional recording that is no problem. As soon as a real recording would ever go in, this is the reason Dify cloud is not allowed: you cannot delete the audio without wiping the whole log, and a retention policy (audio gone after transcription) cannot be honoured. For real use the tool belongs on your school's platform, with a retention period that is written down. **Costs.** Through your own key: gpt-4o-transcribe-diarize costs about 0.6 cents per minute of audio, whisper-1 half that. A 45-minute meeting comes to 20 to 30 cents including the minutes. Dify credits are not touched as long as OpenAI is set to API key. **Longer recordings.** The tool's 5 MB limit is hard. An hour of meeting at 32 kbps is 14 MB and so does not fit. Two routes: cut the recording into fifteen-minute pieces and run them separately, or replace the Speech To Text tool with an HTTP Request node that goes straight to the provider's speech API (limit 25 MB). The latter is a next guide. **From prototype to your school's platform.** The workflow can be saved as a file via **Export DSL** (three dots on the card in Studio) and imported into another Dify account. The models have to be reconnected there to the keys of that account. --- ## Appendix A. Making a test recording without real voices For a prototype you need a recording without personal data. The easiest route is to have a fictional script read out by the same Audio tool, but the other way round: **Text To Speech**. That is how the recording for this guide was made (the script `make_recording.py` does the same from the command line). 1. Make a workflow with a **User Input** with one field: type **Paragraph**, variable `text`. 2. Choose as next step **Tools** → **Audio** → **Text To Speech**.  3. Under **Text** click in the field, type `/` and choose **text**.  4. Under **Model** choose a speech model; **tts-1** is the cheapest.  5. Under **Voice of tts-1** choose a voice, for example **Alloy**.  6. Add an **Output** node with variable `audio` → Text To Speech / **files**.  7. Click **Test Run**, paste the script (`department-meeting-script.txt`) and click **Start Run**. After half a minute an mp3 is in the result.  8. Click the file name straight away and save the file. The link works for five minutes. Cost: about 4 cents for three minutes of speech. The result is one voice reading all the speakers; for a test of transcription and minutes that is enough. A sandbox account has room for five apps, so delete this helper workflow afterwards, or rebuild it into the Transcription tool as done in this guide.