Transcribe Video Testimonials
Fetches video testimonials via HTTP and uses OpenAI to automatically convert the audio into text.
What it does
You get your video testimonials converted into searchable text automatically by fetching the recordings via HTTP and using OpenAI to transcribe the audio. This workflow turns spoken feedback into written content without manual effort.
Step by step
- Configure an HTTP GET node to fetch the video file from the provided URL.
- Extract the audio stream or convert the video container to a supported format like MP3 or WAV before sending it to the API.
- Send the processed audio payload to the OpenAI Transcription endpoint with the correct model parameter.
Where the LLM does the work
- Determine the optimal preprocessing steps for handling various video codecs and sample rates that might cause transcription errors.
- Draft clear system prompts or post-processing instructions if the raw transcript needs formatting into structured testimonial quotes.
- Select appropriate language codes or auto-detection settings to ensure accurate transcription of non-English segments.
Watch out for
Large video files can exceed API size limits so you must compress or trim the audio before uploading it.
Background noise in testimonials reduces accuracy so add a noise reduction step if the source quality is poor.
OpenAI may reject unsupported file formats so convert the input to WAV or MP3 explicitly before the request.
API costs scale with duration so monitor the length of each video to avoid unexpected billing spikes.
Tools that fit
HTTP Node API Fetching video recording via GET request
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Transcribe Video Testimonials: Fetches video testimonials via HTTP and uses OpenAI to automatically convert the audio into text. ## Specification - What it does: You get your video testimonials converted into searchable text automatically by fetching the recordings via HTTP and using OpenAI to transcribe the audio. This workflow turns spoken feedback into written content without manual effort. - Trigger: Run manually (Manual · on demand) - Autonomy: Fully hands-off - Expected setup effort: under an hour - Tools/services involved: - HTTP Node: Fetching video recording via GET request - OpenAI: Transcribing the audio recording ## Known pitfalls, handle each one explicitly in your implementation 1. Large video files can exceed API size limits so you must compress or trim the audio before uploading it. 2. Background noise in testimonials reduces accuracy so add a noise reduction step if the source quality is poor. 3. OpenAI may reject unsupported file formats so convert the input to WAV or MP3 explicitly before the request. 4. API costs scale with duration so monitor the length of each video to avoid unexpected billing spikes. ## Reference implementation https://www.youtube.com/watch?v=U15D29BtfDI (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/transcribe-video-testimonials/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for marketers who'd rather brief an agent than start from a blank doc.