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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 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.
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…
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
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
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
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