Cabal Clippers Army

Module B / Code recipe / 10-20 min

Extracting audio with FFmpeg, then transcribing

Turn MP4, MOV, or MKV into clean audio before transcription.

TL;DR

Use this lesson to turn MP4, MOV, or MKV into clean audio before transcription. Treat it as practical guidance, not a rigid rulebook.

Why it matters

Captions make clips understandable without sound, searchable after publishing, and reviewable by editors before export. The goal is to help you make a stronger clip without taking away your creative freedom.

What you will learn

Understand the caption or transcript decision behind this workflow.
Produce a usable transcript, caption file, or burned-in caption pass for one clip.
Catch the caption mistakes that most often hurt readability, accuracy, or platform fit.

Prerequisites

  • An audio file, video file, URL, or exported clip
  • A target output format such as SRT, VTT, burned-in MP4, or transcript text

What you need

FFmpeg installed locally.
A local source video file.
A small test input before processing a full source.
Permission to use the source media.

Core concept

Caption work is part accuracy and part design. The workflow only works if viewers can read the result quickly on a phone.

Example

Scenario

Auto-captions are mostly correct, but the clip contains names, numbers, jargon, or fast speech.

Move

Apply the workflow to a short section first and proofread the result at phone size.

Result

The caption pass becomes readable and accurate enough that sound-off viewers can follow the clip.

How to do it

  1. 1Use FFmpeg to extract a clean mono audio file before sending it to transcription.
  2. 2Check the audio file by playing the first few seconds and a later section before uploading.
  3. 3Transcribe the extracted audio and keep word timings if you will create captions.
  4. 4Use the transcript to find cut points or caption lines, then return to the original video for final export.
  5. 5Save the exact command that worked so future clips use the same reliable path.

Expected output

A caption or transcript artifact that is proofread, timed, readable on a phone, and matched to the target platform.

Practice task

Produce a clean caption pass

  1. 1Take a 20-30 second section of a real clip.
  2. 2Apply the caption or transcript workflow from this lesson.
  3. 3Proofread it with sound on, then watch it again with sound off at phone size.

Check your work

Names, numbers, jargon, acronyms, and claims are correct.
Caption lines are short, timed well, and readable on a phone.
The final export uses the caption method the target platform will actually show.

Common mistakes and fixes

Do not finish Extracting audio with FFmpeg, then transcribing without checking the exact words against the audio.
Do not let long caption blocks fill the screen on mobile.
Do not ignore names, numbers, acronyms, tickers, and niche terms.
Do not assume every platform will show sidecar caption files.
Do not export before checking caption placement at phone size.

Troubleshooting

If FFmpeg cannot find the file, drag the file into the terminal or quote paths with spaces.
If output audio is silent, confirm the source has an audio stream and remove overly aggressive flags.
If subtitles do not burn in, check that the SRT path is correct and escape special characters in the filename.

Related resources

Caption QA checklistOpen resourceSRT/VTT/ASS format referenceOpen resourceCustom vocabulary templateOpen resource

Reference snippets

OpenAI speech-to-text request

curl --request POST \
  --url https://api.openai.com/v1/audio/transcriptions \
  --header "Authorization: Bearer $OPENAI_API_KEY" \
  --header "Content-Type: multipart/form-data" \
  --form file=@audio.mp3 \
  --form model=gpt-4o-transcribe

Python speech-to-text request

from openai import OpenAI

client = OpenAI()
with open("audio.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="gpt-4o-transcribe",
        file=audio_file,
    )

print(transcript.text)

Whisper word timestamps

curl https://api.openai.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@audio.mp3" \
  -F model="whisper-1" \
  -F response_format="verbose_json" \
  -F "timestamp_granularities[]=word"

Extract audio before transcription

ffmpeg -i input.mp4 -vn -ac 1 -ar 16000 audio.wav

Download audio from a URL for local review

yt-dlp -x --audio-format mp3 "https://example.com/video"