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google/lyra

0
View on GitHub↗
3,964 stars·367 forks·C++·Apache-2.0·13 views

Lyra

Lyra is a voice compression framework and low-bitrate speech codec designed to transmit high-quality audio over bandwidth-constrained networks. It utilizes an adaptive bitrate audio codec to balance audio quality and network bandwidth during active sessions.

The project employs generative audio compression, using neural networks to synthesize speech signals from minimal data and reconstruct missing audio details. This allows for high-quality voice audio reconstruction from highly compressed byte streams.

The system covers bandwidth-optimized voice over IP and real-time voice communication, focusing on low-bitrate speech compression to maintain call stability. Its capabilities include dynamic audio bitrate adjustment and voice audio processing to prevent lag and signal dropout in unstable network environments.

Features

  • Adaptive Bitrate Codecs - Provides a voice compression technology that adjusts the data rate during active sessions to balance quality and bandwidth.
  • Audio Sample Reconstruction - Uses generative models to reconstruct high-quality speech waveforms from model samples.
  • Neural Audio Compression - Converts speech signals into a low-bitrate byte stream using neural tokenizers and generative models.
  • Neural Vocoders - Translates compressed latent features back into audible speech by predicting the underlying waveform sample by sample.
  • Neural Audio Reconstruction - Recreates high-quality speech signals from compressed byte streams using deep learning models.
  • Latent Space Compression - Maps high-dimensional speech signals into a compact vector space for low-bitrate transmission.
  • Generative Waveform Synthesis - Uses neural networks to synthesize high-quality speech waveforms from low-dimensional latent representations.
  • Voice Compression Frameworks - Provides a complete set of tools for encoding and decoding speech into a low-bitrate byte stream.
  • Low Bitrate Audio Coding - Reduces the size of voice audio data for transmission over networks with very limited bandwidth.
  • Latent Bitrate Quantization - Controls the precision of compressed latent vectors to balance audio fidelity against available network bandwidth.
  • Audio Feature Extraction - Extracts essential acoustic characteristics from voice signals to maintain intelligibility while discarding redundant data.
  • VoIP Bandwidth Optimizations - Manages voice over IP calls by adjusting audio bitrates to prevent lag and signal dropout.
  • Real-Time Voice and Video Communication - Streams spoken audio between devices while maintaining clarity despite slow or unstable internet connections.
  • Media & Communication - Speech compression codec.

Star history

Star history chart for google/lyraStar history chart for google/lyra

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does google/lyra do?

Lyra is a voice compression framework and low-bitrate speech codec designed to transmit high-quality audio over bandwidth-constrained networks. It utilizes an adaptive bitrate audio codec to balance audio quality and network bandwidth during active sessions.

What are the main features of google/lyra?

The main features of google/lyra are: Adaptive Bitrate Codecs, Audio Sample Reconstruction, Neural Audio Compression, Neural Vocoders, Neural Audio Reconstruction, Latent Space Compression, Generative Waveform Synthesis, Voice Compression Frameworks.

Which projects share features with google/lyra?

Projects with overlapping indexed features include: facebookresearch/encodec — EnCodec is a neural audio codec and compression tool designed to transform raw audio waveforms into discrete codes and… text-to-audio/audiolcm — AudioLCM is a deep learning framework designed for text-to-audio synthesis. It functions as a generative engine that… facebookresearch/audiocraft — Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and… mumble-voip/mumble — Mumble is a low-latency voice over IP system designed for real-time communication. It provides a self-hosted,… mattermost/mattermost-server — Mattermost Server is an open source collaboration suite and self-hosted team communication platform. It functions as… doubiiu/tooncrafter — ToonCrafter is a model that combines latent diffusion, reference-based colorization, and sketch-guided control for…

Projects sharing features with Lyra

These projects share indexed features with Lyra. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • facebookresearch/encodecfacebookresearch avatar

    facebookresearch/encodec

    3,893View on GitHub↗

    EnCodec is a neural audio codec and compression tool designed to transform raw audio waveforms into discrete codes and reconstruct them back into sound. It functions as a system for neural audio representation, converting continuous audio signals into sequences of integer indices for use in generative AI tasks. The project utilizes a residual vector quantizer, which employs multiple layers of codebooks to represent audio signals with high precision at low bitrates. This approach allows the system to compress audio to discrete codes and perform low bitrate audio coding for efficient transmissi

    Python
    View on GitHub↗3,893
  • text-to-audio/audiolcmText-to-Audio avatar

    Text-to-Audio/AudioLCM

    1,162View on GitHub↗

    AudioLCM is a deep learning framework designed for text-to-audio synthesis. It functions as a generative engine that converts written descriptions into high-fidelity audio clips by processing text prompts through latent consistency models. The project distinguishes itself by utilizing latent consistency distillation to enable rapid audio generation. By mapping diffusion trajectories to a single-step consistency function, the system achieves efficient sound synthesis while maintaining the output quality typically associated with iterative diffusion processes. The framework provides a comprehe

    Python
    View on GitHub↗1,162
  • facebookresearch/audiocraftfacebookresearch avatar

    facebookresearch/audiocraft

    23,379View on GitHub↗

    Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It al

    Jupyter Notebook
    View on GitHub↗23,379
  • mumble-voip/mumblemumble-voip avatar

    mumble-voip/mumble

    7,756View on GitHub↗

    Mumble is a low-latency voice over IP system designed for real-time communication. It provides a self-hosted, encrypted voice server that secures data transmission using public-private key authentication and digital certificates. The platform distinguishes itself through a request-response protocol for remote configuration and integration with external automation scripts. It also features a graphical overlay that renders active speaker information and chat status on top of other running applications, as well as positional audio to simulate the 3D spatial location of speakers in a virtual envi

    C++audioclientcmake
    View on GitHub↗7,756
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