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...It replaces hand-engineered codecs with learned models that capture speech characteristics more efficiently and reconstruct waveforms with a neural vocoder. The system targets mobile-class hardware, balancing latency and quality so it can run in real-time on phones. Its architecture is resilient to packet loss and jitter through framing strategies and error concealment, helping conversations remain understandable under adverse conditions. The codebase includes encoder and decoder components, along with tools for data preparation and evaluation. ...
IPTV/Video cloud
Features:
Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian)
GPU/CPU Encode/Decode/Post Processing
Stream statistics
CCTV
Adaptive hls streams
Load balancing
Temporary urls
HLS push
EPG scanning
Subtitles to text conversions
AD insertion
Logo overlay
Video effects
Relays
Timeshifts
Catchups
Playlists
Restream/Transcode from online streaming services like Youtube, Twitch
Mozaic
Many Outputs
Physical Inputs
Streaming Protocols
File Formats
Presets
Vods/Series server-side support
Pay per view channels
Channels on demand
HTTP Live Streaming (HLS) server-side support
Public API, client server communication via JSON RPC
Protocol gzip compression
Deep learning video analysis
Supported deep learning frameworks:
Tensorflow
NCSDK
Caffe
ML Hardware:
Implements Lazy Wavelet Transform with Lifting in several variants: 1-D, 2-D, 3-D, integer precision, etc. Sample video compressor, integrated with FFmpeg. OpenCL version is created.
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