CaseStudies

Complete Multimodal Pipeline

Status: 📋 Planned
Version: v0.2.0+
Last Updated: November 15, 2025

Overview

End-to-end audio and video understanding with generation capabilities for full multimodal workflows, enabling agents to process and create rich media content seamlessly.

Description

Goal

Enable MassGen agents to work with all media types (text, images, audio, video) in both understanding and generation modes, creating complete pipelines from raw input to polished multimedia output.

Key Features

Audio Capabilities
- Understanding: Speech-to-text, speaker identification, emotion detection, music analysis
- Generation: Text-to-speech, voice cloning, music generation, audio effects
- Formats: MP3, WAV, FLAC, OGG, streaming audio
Video Capabilities
- Understanding: Scene detection, object tracking, action recognition, transcript extraction
- Generation: Video synthesis, editing, effects, transitions
- Formats: MP4, AVI, MOV, WebM, streaming video
Cross-Modal Integration
- Text ↔ Audio (read/speak)
- Text ↔ Video (describe/generate)
- Audio ↔ Video (soundtrack/narration)
- Image ↔ Video (frame extraction/animation)
End-to-End Pipelines
- Podcast Production: Script → TTS → Music → Mix → Export
- Video Creation: Storyboard → Generate scenes → Add voiceover → Edit → Publish
- Content Repurposing: Blog post → Script → Video → Social clips
- Accessibility: Video → Transcript → Audio description → Subtitles
Generation Models
- Text-to-Speech: ElevenLabs, Azure TTS, Google TTS
- Text-to-Video: Runway, Pika, Stable Video Diffusion
- Text-to-Music: MusicGen, Jukebox
- Video Editing: FFmpeg, MoviePy integration

Example Workflows

Podcast Production:

Research topic → Write script → Generate speech → Add music → Export MP3

Educational Video:

Lesson plan → Create slides → Generate narration → Add animations → Compile video

Content Marketing:

Blog post → Extract key points → Create video script → Generate video → Export for social media

Testing Guidelines

Test Scenarios

Audio Understanding Test
- Input: 5-minute podcast episode with multiple speakers
- Test: Transcribe, identify speakers, detect emotions
- Expected: >95% transcription accuracy, correct speaker labels
- Validation: Compare to ground truth transcript
Audio Generation Test
- Input: Text script with emotional cues
- Test: Generate speech with appropriate emotion and prosody
- Expected: Natural-sounding speech, correct emotional tone
- Validation: Human evaluation for naturalness
Video Understanding Test
- Input: 2-minute video clip
- Test: Extract scenes, describe content, generate transcript
- Expected: Accurate scene boundaries, detailed descriptions
- Validation: Compare to human annotations
Video Generation Test
- Input: Text description: “A cat playing with a ball in a sunny garden”
- Test: Generate 10-second video clip
- Expected: Video matches description, smooth motion
- Validation: Human evaluation for quality and relevance
End-to-End Pipeline Test
- Input: Blog post about AI trends
- Test: Convert to 2-minute explainer video with narration
- Expected: Complete video with visuals, voiceover, text overlays
- Validation: Video is coherent, informative, production-ready
Cross-Modal Test
- Input: Video without audio
- Test: Generate audio description for accessibility
- Expected: Detailed narration of visual content
- Validation: Blind user testing for completeness

Quality Metrics

Audio Quality: SNR, clarity, naturalness (MOS score)
Video Quality: Resolution, frame rate, visual coherence
Sync Quality: Audio-video alignment accuracy
Generation Fidelity: Adherence to text prompts
Processing Speed: Real-time factor (RTF) for generation

Performance Testing

Test with various input lengths (10s, 1min, 10min, 1hr)
Measure memory usage for large video processing
Test concurrent multimodal workflows
Benchmark generation speed vs. cloud services

Validation Criteria

✅ Audio transcription accuracy >95%
✅ Video scene detection precision >90%
✅ Generated speech naturalness MOS >4.0/5.0
✅ Generated video quality suitable for social media
✅ End-to-end pipeline completes without manual intervention
✅ Support files up to 1 hour in length

Implementation Notes

Architecture

Input (text/audio/video)
    ↓
Multimodal Understanding
    ↓
Agent Processing & Decision
    ↓
Multimodal Generation
    ↓
Output (text/audio/video)

Technology Stack

Understanding:

Audio: Whisper, wav2vec, pyAudioAnalysis
Video: OpenCV, PySceneDetect, YOLO

Generation:

Audio: ElevenLabs API, Azure TTS, Bark
Video: Runway API, Stable Diffusion Video, FFmpeg
Music: MusicGen, AudioCraft

Processing:

FFmpeg for format conversion and editing
MoviePy for Python-native video editing
pydub for audio manipulation

Configuration Example

multimodal:
  audio:
    transcription: whisper-large-v3
    tts: elevenlabs
    music_gen: musicgen-large
  
  video:
    understanding: gemini-2.0-flash-exp
    generation: runway-gen3
    editing: ffmpeg
  
  pipelines:
    podcast_production:
      - research
      - script_writing
      - tts_generation
      - music_generation
      - audio_mixing

Multimodal Video Analysis (v0.1.3) - Video understanding foundation
MassGen Video Recording and Editing (Planned) - Video production
Computer Use Tools (v0.1.9) - Visual understanding basics

References

See ROADMAP.md for detailed long-term vision and development timeline.