Use case

Podcasts & Audio Pipelines

Run FFmpeg for audio workloads. Process multi-hour podcast episodes, normalise loudness, extract audio from video, convert between formats. No Lambda timeout, no server management.

Try free Read docs

Pain points

Commands

Links

Outcome

Your audio pipeline shouldn't have a 15-minute ceiling. Process a 4-hour episode in one job.

Podcast hosting platforms, audio processing tools, transcription services, and any app that processes spoken-word audio or extracts audio from video content.

step 01Loudness normalisation (podcast standard)

step 02Extract audio from video podcast

step 03Multi-format audio export

Problem

Video infra should not own your roadmap

Lambda's 15-minute ceiling meets 3-hour podcasts

Most podcast episodes are 30-90 minutes. Interviews run 2-3 hours. AWS Lambda kills your job at 15 minutes. Chunking audio is lossy. You need a platform that handles long-form audio natively.

Broadcast loudness standards are not optional

Apple Podcasts, Spotify, and YouTube all have loudness standards. Getting LUFS normalisation right with FFmpeg's loudnorm filter requires proper parameters and testing — not a one-line command.

Audio extraction from video at scale

Video podcasts, webinar recordings, and conference talks all need audio extracted. When you're processing hundreds, doing it manually is impossible.

Why us

Raw FFmpeg power without the worker fleet

6-hour runtimes — process a full-length audiobook in one job

Dedicated 16 vCPU hardware — fast FFmpeg audio filters, no throttling

Real-time logs — watch the loudnorm analysis pass in real time

Command chaining — extract audio, normalise, and generate multiple formats in one call

AI auto-diagnosis — catches filter misconfigurations before they waste processing time

Usage-based pricing — process 100 hours this month, 10 the next, pay accordingly

Commands

Common FFmpeg commands for Podcasts & Audio Pipelines

ffmpeg

Loudness normalisation (podcast standard)

Normalise audio to -16 LUFS with -1.5 dBTP true peak for podcast hosting platforms.

-i {{input}} -af "loudnorm=I=-16:TP=-1.5:LRA=11:print_format=summary" -c:a aac -b:a 128k {{normalised.m4a}}

ffmpeg

Extract audio from video podcast

Strip the audio track from a video podcast and save as high-quality MP3.

-i {{video}} -vn -c:a libmp3lame -b:a 192k {{audio.mp3}}

ffmpeg

Multi-format audio export

Generate MP3, AAC, and Opus versions in one request for broad platform compatibility.

-i {{input}} \
  -vn -c:a libmp3lame -b:a 192k {{podcast.mp3}} \
  -vn -c:a aac -b:a 128k {{podcast.m4a}} \
  -vn -c:a libopus -b:a 96k {{podcast.opus}}

It's actually not bad.

Get an API key in 30 seconds. Your first 2 GB are free. No credit card required.

Try Free

Related use cases

Agencies & Freelancers Video SaaS Platforms Content Creators & UGC Platforms

Integrations for Podcasts & Audio Pipelines

Make n8n Pipedream

Use case

Podcasts & Audio Pipelines

Run FFmpeg for audio workloads. Process multi-hour podcast episodes, normalise loudness, extract audio from video, convert between formats. No Lambda timeout, no server management.

Try free Read docs

Pain points

Commands

Links

Outcome

Your audio pipeline shouldn't have a 15-minute ceiling. Process a 4-hour episode in one job.

Podcast hosting platforms, audio processing tools, transcription services, and any app that processes spoken-word audio or extracts audio from video content.

step 01Loudness normalisation (podcast standard)

step 02Extract audio from video podcast

step 03Multi-format audio export

Problem

Video infra should not own your roadmap

Lambda's 15-minute ceiling meets 3-hour podcasts

Most podcast episodes are 30-90 minutes. Interviews run 2-3 hours. AWS Lambda kills your job at 15 minutes. Chunking audio is lossy. You need a platform that handles long-form audio natively.

Broadcast loudness standards are not optional

Apple Podcasts, Spotify, and YouTube all have loudness standards. Getting LUFS normalisation right with FFmpeg's loudnorm filter requires proper parameters and testing — not a one-line command.

Audio extraction from video at scale

Video podcasts, webinar recordings, and conference talks all need audio extracted. When you're processing hundreds, doing it manually is impossible.

Why us

Raw FFmpeg power without the worker fleet

6-hour runtimes — process a full-length audiobook in one job

Dedicated 16 vCPU hardware — fast FFmpeg audio filters, no throttling

Real-time logs — watch the loudnorm analysis pass in real time

Command chaining — extract audio, normalise, and generate multiple formats in one call

AI auto-diagnosis — catches filter misconfigurations before they waste processing time

Usage-based pricing — process 100 hours this month, 10 the next, pay accordingly

Commands

Common FFmpeg commands for Podcasts & Audio Pipelines

ffmpeg

Loudness normalisation (podcast standard)

Normalise audio to -16 LUFS with -1.5 dBTP true peak for podcast hosting platforms.

-i {{input}} -af "loudnorm=I=-16:TP=-1.5:LRA=11:print_format=summary" -c:a aac -b:a 128k {{normalised.m4a}}

ffmpeg

Extract audio from video podcast

Strip the audio track from a video podcast and save as high-quality MP3.

-i {{video}} -vn -c:a libmp3lame -b:a 192k {{audio.mp3}}

ffmpeg

Multi-format audio export

Generate MP3, AAC, and Opus versions in one request for broad platform compatibility.

-i {{input}} \
  -vn -c:a libmp3lame -b:a 192k {{podcast.mp3}} \
  -vn -c:a aac -b:a 128k {{podcast.m4a}} \
  -vn -c:a libopus -b:a 96k {{podcast.opus}}

It's actually not bad.

Get an API key in 30 seconds. Your first 2 GB are free. No credit card required.

Try Free

Related use cases

Agencies & Freelancers Video SaaS Platforms Content Creators & UGC Platforms

Integrations for Podcasts & Audio Pipelines

Make n8n Pipedream