Tdarr — Distributed Transcode Automation
Description
Server/node architecture for distributed library transcoding over FFmpeg and HandBrake with folder watching, health checks, plugin pipelines and CPU/GPU worker classes.
Resource details
- Category path
- Media Tools›Batch Processing & Automation
- Classification
- Not yet classified
Tdarr — Distributed Transcode Automation is cataloged in Media Tools and Batch Processing & Automation.
URL
Related resources
- aws-batch-with-FFmpegReference architecture running FFmpeg containers (ARM64/x86-64/NVIDIA/Xilinx) on AWS Batch with Spot compute environments and SDK/REST job…
- python-video-converterPython library wrapping ffmpeg/ffprobe with a declarative Converter().convert() API for scripting format/audio/video conversion pipelines.
- mkv-webBrowser-based MKV remuxer using ffmpeg.wasm and web workers, remuxes to MP4/WebM entirely client-side with no server or extension.
- vast-vmapJavaScript library parsing IAB VAST 2.0/3.0 and VMAP 1.0 ad schemas with explicit tracking model (caller invokes track() rather than DOM wa…
- ffcvtCLI ffmpeg wrapper with zero-config recommended encode settings, supports recursive batch mode over directories.
- Evaluating Video Quality Metrics for Neural and Traditional Codecs (4K/UHD-1)Academic comparison of VMAF, AVQBits, and FasterVQA no-reference quality metrics on 4K content.
- Bitrate Ladder Construction via Transfer Learning and Spatio-Temporal FeaturesML-based bitrate ladder optimization achieving 94.1% complexity reduction at only 1.71% BD-rate cost using transfer learning.
- VMAF-torchPyTorch reimplementation of VMAF metric, GPU/gradient-friendly for use in learned codec optimization loops.
- CompressedVQA: Full/No-Reference VQA for Compressed UGC VideoICME 2021 grand challenge-winning full-reference and no-reference video quality models for compressed user-generated content.
- 2BiVQA: Double Bi-LSTM No-Reference Video Quality AssessmentNo-reference VQA model for UGC video using double Bi-LSTM, companion code to arXiv:2208.14774.