ASR-LLM-TTS is a repository that provides detailed tutorials for setting up the environment, including installing anaconda, ffmpeg, creating virtual environments, and installing necessary libraries such as pytorch, torchaudio, edge-tts, funasr, and more. It also introduces features like voiceprint recognition, custom wake words, and conversation history memory. The repository combines CosyVoice for speech synthesis, SenceVoice for speech recognition, and QWen2.5 for dialogue understanding. It offers multiple speech synthesis methods including CoosyVoice, pyttsx3, and edgeTTS, with scripts for interactive inference provided. The repository aims to enable real-time speech interaction and multi-modal interactions involving audio and video.

This repository is a modification based on the pixi-live2d-display project. It provides a platform for TTS (Text-to-Speech) functionality and a large model voice chat page. Users can install node.js, run the provided commands, and access the specified URLs to utilize the features.

ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

OpenedAI Speech is a free, private text-to-speech server compatible with the OpenAI audio/speech API. It offers custom voice cloning and supports various models like tts-1 and tts-1-hd. Users can map their own piper voices and create custom cloned voices. The server provides multilingual support with XTTS voices and allows fixing incorrect sounds with regex. Recent changes include bug fixes, improved error handling, and updates for multilingual support. Installation can be done via Docker or manual setup, with usage instructions provided. Custom voices can be created using Piper or Coqui XTTS v2, with guidelines for preparing audio files. The tool is suitable for tasks like generating speech from text, creating custom voices, and multilingual text-to-speech applications.

Ichigo is a local real-time voice AI tool that uses an early fusion technique to extend a text-based LLM to have native 'listening' ability. It is an open research experiment with improved multiturn capabilities and the ability to refuse processing inaudible queries. The tool is designed for open data, open weight, on-device Siri-like functionality, inspired by Meta's Chameleon paper. Ichigo offers a web UI demo and Gradio web UI for users to interact with the tool. It has achieved enhanced MMLU scores, stronger context handling, advanced noise management, and improved multi-turn capabilities for a robust user experience.

The 'llm-past-tense' repository contains code related to the research paper 'Does Refusal Training in LLMs Generalize to the Past Tense?' by Maksym Andriushchenko and Nicolas Flammarion. It explores the generalization of refusal training in large language models (LLMs) to the past tense. The code includes experiments and examples for running different models and requests related to the study. Users can cite the work if found useful in their research, and the codebase is released under the MIT License.

Build fast and accurate GenAI applications with GraphRAG SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows. The SDK simplifies the development process by automating ontology creation, knowledge graph agent creation, and query handling, enabling users to interact and query their knowledge graphs effectively. It supports multi-agent systems and orchestrates agents specialized in different domains. The SDK is optimized for FalkorDB, ensuring high performance and scalability for large-scale applications. By leveraging knowledge graphs, it enables semantic relationships and ontology-driven queries that go beyond standard vector similarity, enhancing retrieval-augmented generation capabilities.

Phi-3-MLX is a versatile AI framework that leverages both the Phi-3-Vision multimodal model and the Phi-3-Mini-128K language model optimized for Apple Silicon using the MLX framework. It provides an easy-to-use interface for a wide range of AI tasks, from advanced text generation to visual question answering and code execution. The project features support for batched generation, flexible agent system, custom toolchains, model quantization, LoRA fine-tuning capabilities, and API integration for extended functionality.

Fira is a memory-efficient training framework for Large Language Models (LLMs) that enables full-rank training under low-rank constraint. It introduces a method for training with full-rank gradients of full-rank weights, achieved with just two lines of equations. The framework includes pre-training and fine-tuning functionalities, packaged as a Python library for easy use. Fira utilizes Adam optimizer by default and provides options for weight decay. It supports pre-training LLaMA models on the C4 dataset and fine-tuning LLaMA-7B models on commonsense reasoning tasks.

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models. Users can add documents to the tree, answer questions based on indexed documents, save and load the tree, and extend RAPTOR with custom summarization, question-answering, and embedding models. The tool is designed to be flexible and customizable for various NLP tasks.

This repository contains the code and dataset for LDB, a novel debugging framework that enables Large Language Models (LLMs) to refine their generated programs by tracking the values of intermediate variables throughout the runtime execution. LDB segments programs into basic blocks, allowing LLMs to concentrate on simpler code units, verify correctness block by block, and pinpoint errors efficiently. The tool provides APIs for debugging and generating code with debugging messages, mimicking how human developers debug programs.

LoRAX is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency. It features dynamic adapter loading, heterogeneous continuous batching, adapter exchange scheduling, optimized inference, and is ready for production with prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. LoRAX supports a number of Large Language Models as the base model including Llama, Mistral, and Qwen, and any of the linear layers in the model can be adapted via LoRA and loaded in LoRAX.

KVCache-Factory is a unified framework for KV Cache compression of diverse models. It supports multi-GPUs inference with big LLMs and various attention implementations. The tool enables KV cache compression without Flash Attention v2, multi-GPU inference, and specific models like Mistral. It also provides functions for KV cache budget allocation and batch inference. The visualization tools help in understanding the attention patterns of models.

SUPIR is an AI-based image processing and upscaling tool that leverages cutting-edge technology to enhance image quality and resolution. The tool provides users with the ability to upscale images with high generalization and quality, as well as specific settings for light degradation scenarios. It offers a range of models and checkpoints for different use cases, along with detailed instructions for installation and usage. SUPIR also includes features for color fixing, linear CFG adjustments, and various prompts for image enhancement. The tool is designed for non-commercial use only and comes with a contact email for inquiries and permission requests for commercial use.

LLMLean integrates LLMs and Lean for tactic suggestions, proof completion, and more. Users can utilize LLMLean on problems from Mathematics in Lean by installing LLM on their laptop or using LLM from the Open AI API or Together.ai API. The tool provides tactics like `llmstep` for next-tactic suggestions and `llmqed` for completing proofs. For optimal performance, especially with `llmqed` tactic, it is recommended to use the Open AI API.

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

ASR-LLM-TTS is a repository that provides detailed tutorials for setting up the environment, including installing anaconda, ffmpeg, creating virtual environments, and installing necessary libraries such as pytorch, torchaudio, edge-tts, funasr, and more. It also introduces features like voiceprint recognition, custom wake words, and conversation history memory. The repository combines CosyVoice for speech synthesis, SenceVoice for speech recognition, and QWen2.5 for dialogue understanding. It offers multiple speech synthesis methods including CoosyVoice, pyttsx3, and edgeTTS, with scripts for interactive inference provided. The repository aims to enable real-time speech interaction and multi-modal interactions involving audio and video.

Bolna is an open-source platform for building voice-driven conversational applications using large language models (LLMs). It provides a comprehensive set of tools and integrations to handle various aspects of voice-based interactions, including telephony, transcription, LLM-based conversation handling, and text-to-speech synthesis. Bolna simplifies the process of creating voice agents that can perform tasks such as initiating phone calls, transcribing conversations, generating LLM-powered responses, and synthesizing speech. It supports multiple providers for each component, allowing users to customize their setup based on their specific needs. Bolna is designed to be easy to use, with a straightforward local setup process and well-documented APIs. It is also extensible, enabling users to integrate with other telephony providers or add custom functionality.

The LiveKit Agent Framework is designed for building real-time, programmable participants that run on servers. Easily tap into LiveKit WebRTC sessions and process or generate audio, video, and data streams. The framework includes plugins for common workflows, such as voice activity detection and speech-to-text. Agents integrates seamlessly with LiveKit server, offloading job queuing and scheduling responsibilities to it. This eliminates the need for additional queuing infrastructure. Agent code developed on your local machine can scale to support thousands of concurrent sessions when deployed to a server in production.

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

This repository is a curated list of awesome large AI models in audio signal processing, focusing on the application of large language models to audio tasks. It includes survey papers, popular large audio models, automatic speech recognition, neural speech synthesis, speech translation, other speech applications, large audio models in music, and audio datasets. The repository aims to provide a comprehensive overview of recent advancements and challenges in applying large language models to audio signal processing, showcasing the efficacy of transformer-based architectures in various audio tasks.

Nexa SDK is a comprehensive toolkit supporting ONNX and GGML models for text generation, image generation, vision-language models (VLM), and text-to-speech (TTS) capabilities. It offers an OpenAI-compatible API server with JSON schema mode and streaming support, along with a user-friendly Streamlit UI. Users can run Nexa SDK on any device with Python environment, with GPU acceleration supported. The toolkit provides model support, conversion engine, inference engine for various tasks, and differentiating features from other tools.

AudioLLMs is a curated collection of research papers focusing on developing, implementing, and evaluating language models for audio data. The repository aims to provide researchers and practitioners with a comprehensive resource to explore the latest advancements in AudioLLMs. It includes models for speech interaction, speech recognition, speech translation, audio generation, and more. Additionally, it covers methodologies like multitask audioLLMs and segment-level Q-Former, as well as evaluation benchmarks like AudioBench and AIR-Bench. Adversarial attacks such as VoiceJailbreak are also discussed.

FireRedTTS is a foundation text-to-speech framework designed for industry-level generative speech applications. It offers a rich-punctuation model with expanded punctuation coverage and enhanced audio production consistency. The tool provides pre-trained checkpoints, inference code, and an interactive demo space. Users can clone the repository, create a conda environment, download required model files, and utilize the tool for synthesizing speech in various languages. FireRedTTS aims to enhance stability and provide controllable human-like speech generation capabilities.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.