Media and video AI pipelines built for production quality
Causal Labs builds transcription, diarization and AI generation pipelines for audio and video — Whisper, speaker diarization, FAL.ai, ElevenLabs and FFmpeg — at production quality and scale. Delivery has included a Whisper-based transcription app with speaker diarization, plus AI video/voice generation pipelines.
Causal Labs builds transcription, diarization and AI generation pipelines for audio and video — Whisper, speaker diarization, FAL.ai, ElevenLabs and FFmpeg — at production quality and scale. Delivery has included a Whisper-based transcription app with speaker diarization, plus AI video/voice generation pipelines.
Why transcription accuracy alone isn't enough for production media pipelines
A raw Whisper transcript without speaker diarization is a wall of text nobody can use for meeting notes, call analysis, or content indexing — you need to know who said what, not just what was said. Production media AI pipelines need the accuracy of a good model plus the structure (speakers, timestamps, segments) that makes the output actually usable downstream.
How we approach a media/video AI engagement
Requirements gathering
We identify the downstream use of the output — search, analytics, generation — which shapes the pipeline's structure.
Build
Transcription, diarization and/or generation pipeline built around FFmpeg for media handling and the appropriate model APIs.
QA & review
Accuracy and latency benchmarked against representative media samples, including overlapping speech and background noise.
What's included
Transcription pipelines
Whisper-based transcription tuned for accuracy on your actual audio conditions.
Speaker diarization
Who-said-what segmentation, not just a flat transcript.
AI voice & video generation
ElevenLabs and FAL.ai integration for generation use cases, where the project calls for it.
Media processing infrastructure
FFmpeg-based handling for format conversion, segmentation and preprocessing at scale.
- Production transcription/diarization pipeline
- Media processing infrastructure
- Accuracy benchmark on representative samples
- Generation pipeline (where applicable)
Core transcription model, tuned per use case for accuracy and latency.
Speaker segmentation layered on top of raw transcription output.
Used for AI generation workloads requiring managed GPU infrastructure.
Voice generation and cloning where the project requires synthesized audio.
Handles media format conversion and preprocessing across the pipeline.
Shipped a Whisper-based transcription app with speaker diarization plus AI video/voice generation pipelines.
typical delivery window
Questions about this service
Does the transcription pipeline handle multiple speakers and overlapping speech?
Speaker diarization is built specifically for the multi-speaker case; overlapping speech is a genuinely harder accuracy problem and we'll set expectations against your actual audio samples during scoping rather than promise perfect separation.
Sitting on hours of audio or video that needs to become searchable, structured data?
Tell us about your media and its use case downstream.
Start a conversation