Best AI tools for< Transpose Chords >

Lamucal is an AI-powered music application that provides users with accurate chords, beats, lyrics, and tabs for any song. It features AI-generated rhythm patterns and precise lyric synchronization, making it an invaluable tool for musicians and music enthusiasts alike. With Lamucal, users can easily find and play their favorite songs, explore new music, and improve their musical skills.

Lamucal is an AI-powered platform that provides tabs and chords for any song. It offers real-time chords, lyrics, tabs, and melody for any song, making it a valuable tool for musicians and music enthusiasts. Users can upload songs or search for any song to access chords and other musical elements. With a user-friendly interface and a wide range of features, Lamucal aims to enhance the music learning and playing experience for its users.

Web Transpose is an AI-powered web scraping and web crawling API that allows users to transform any website into structured data. By utilizing artificial intelligence, Web Transpose can instantly build web scrapers for any website, enabling users to extract valuable information efficiently and accurately. The tool is designed for production use, offering low latency and effective proxy handling. Web Transpose learns the structure of the target website, reducing latency and preventing hallucinations commonly associated with traditional web scraping methods. Users can query any website like an API and build products quickly using the scraped data.

Harvy is an AI-driven automation tool designed to streamline work diary data entry and compliance reporting for heavy vehicle operators. By automating tasks such as scanning logbook sheets, detecting breaches, and generating compliance reports, Harvy simplifies complex processes, reduces human error, and enhances operational efficiency. The platform offers significant time and cost savings, promotes regulatory compliance, and provides valuable insights to improve safety and fatigue management. With user-friendly features and a proactive approach to compliance, Harvy is a valuable tool for transport operations seeking to optimize their processes and ensure regulatory adherence.

Sentibotics is an AI-powered platform that offers smart autonomous robots and solutions for various applications, such as warehouse logistics and disinfection systems. The platform provides self-reliant systems with customization possibilities, rental options, and the ability to develop own solutions through API control. Sentibotics aims to minimize user intervention, reduce operation costs, and make robots truly work for the customer.

Waymo is a self-driving car company that develops autonomous vehicles and ride-hailing services. The company's mission is to make it safe and easy for people and things to get where they're going. Waymo's self-driving cars have driven over 20 billion miles on public roads and are considered to be the most experienced autonomous vehicles in the world. Waymo's ride-hailing service, Waymo One, is the world's first fully autonomous ride-hailing service. Waymo One is currently available in Phoenix, San Francisco, Los Angeles, and Austin.

Zoox is a company developing autonomous electric vehicles. The company's mission is to make transportation safer, more enjoyable, and more sustainable. Zoox's vehicles are designed from the ground up to be fully autonomous, with no steering wheel or pedals. They are also all-electric, which helps to reduce pollution and congestion. Zoox is currently testing its vehicles in San Francisco and plans to launch a commercial service in the near future.

Cruise is a self-driving car company that is developing autonomous vehicles for ride-hailing and delivery services. The company's mission is to save lives, enhance access, and improve communities through driverless technology. Cruise is currently testing its vehicles in Phoenix, Arizona, and plans to launch commercial services in the near future.

Kardinal is an AI-powered Last-Mile Delivery Optimization Platform dedicated to providing carrier and shipper clients with tailored logistics intelligence solutions. The platform offers route optimization, delivery slot planning, transport procurement optimization, territory sectorization, parcel picking & sorting, and PUDO location services. Kardinal utilizes AI algorithms and machine learning to continuously optimize operations, adapt to real-time conditions, and enhance future optimizations. The platform helps businesses address critical questions from strategic to operational levels, enabling them to optimize continuously and in real-time while modeling reality without compromise.

Dot Group is an AI-powered data advisory and solutions platform that specializes in effective data management. They offer services to help businesses maximize the potential of their data estate, turning complex challenges into profitable opportunities using AI technologies. With a focus on data strategy, data engineering, and data transport, Dot Group provides innovative solutions to drive better profitability for their clients.

Fantasai is an AI tool designed for Table-top RPG Games enthusiasts to create premium HD handouts, cards, scenes, and maps for their campaigns. It offers high-definition generative art, detailed handouts, and easy map-making capabilities to enhance the immersive experience of players. Users can bring their characters to life, design equipment, items, transport, landmarks, and maps with unprecedented control over the creative process. Fantasai empowers players to visualize their world and focus on their game by providing a platform to freely explore community creations.

AixStock is a website that provides free AI-generated stock images for personal and commercial use. The images are high-quality and cover a wide range of categories, including animals, architecture, backgrounds and textures, business and finance, crafts and DIY, cultures and traditions, decor, e-commerce, education, elements and seasons, fantasy and mythology, fashion and beauty, foods and drinks, gardening and floristry, health and wellness, history, holidays, kids, micro and macro worlds, nature, people, science, space and sci-fi, sport, technology, transport, travel, and uncategorized.

UseCasesFor.ai is an AI application that offers a collection of over 250 use cases for artificial intelligence across various industries and disciplines. It provides insights into how different types of AI, such as computer vision, generative AI, machine learning, and natural language processing, are utilized in fields like agriculture, automotive, e-commerce, education, energy, entertainment, finance, healthcare, human resources, insurance, IT, law enforcement, legal, logistics, manufacturing, marketing, product development, public services, property, retail, science, sport, telecommunications, transport, tourism, and wildlife. The platform also allows users to sign up to receive a PDF containing all the use cases and stay updated with the latest AI trends and news.

Hub IT is a comprehensive IT solutions and services provider offering a wide range of services including website development, mobile app development, cloud services, special software solutions, AI technologies, cyber security, SEO, creative content, data entry, business coaching, ads management, and back-office solutions. The company aims to empower businesses and individuals through cutting-edge technology and innovative digital marketing solutions, ensuring optimal efficiency and success in the digital world. With a focus on industry-specific solutions, Hub IT serves clients in various sectors such as automotive, EdTech, energy and utilities, fintech, healthcare, social media, insurance, government, hospitality, logistics, retail, real estate, technology, telecom, tourism, travel, transport, cargo, and video games.

ChordSeqAI Web App is a user-friendly interface for composing chord progressions using deep learning models. The app allows users to interact with suggestions, customize signatures, specify durations, select models and styles, transpose, import, export, and utilize chord variants. It supports keyboard shortcuts, automatic local saving, and playback features. The app is designed for desktop use and offers features for both beginners and advanced users in music composition.

Aiotone is a repository containing audio synthesis and MIDI processing tools in AsyncIO. It includes a work-in-progress polyphonic 4-operator FM synthesizer, tools for performing on Moog Mother 32 synthesizers, sequencing Novation Circuit and Novation Circuit Mono Station, and self-generating sequences for Moog Mother 32 synthesizers and Moog Subharmonicon. The tools are designed for real-time audio processing and MIDI control, with features like polyphony, modulation, and sequencing. The repository provides examples and tutorials for using the tools in music production and live performances.

RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly compute the hidden state for the "RNN" mode. So it's combining the best of RNN and transformer - **great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding** (using the final hidden state).

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

Ivy is an open-source machine learning framework that enables you to: * 🔄 **Convert code into any framework** : Use and build on top of any model, library, or device by converting any code from one framework to another using `ivy.transpile`. * ⚒️ **Write framework-agnostic code** : Write your code once in `ivy` and then choose the most appropriate ML framework as the backend to leverage all the benefits and tools. Join our growing community 🌍 to connect with people using Ivy. **Let's** unify.ai **together 🦾**

NNtrainer is a software framework for training neural network models on devices with limited resources. It enables on-device fine-tuning of neural networks using user data for personalization. NNtrainer supports various machine learning algorithms and provides examples for tasks such as few-shot learning, ResNet, VGG, and product rating. It is optimized for embedded devices and utilizes CBLAS and CUBLAS for accelerated calculations. NNtrainer is open source and released under the Apache License version 2.0.

BitBLAS is a library for mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in GPTQ, the $W_{INT2}A_{FP16}$ in BitDistiller, the $W_{INT2}A_{INT8}$ in BitNet-b1.58. BitBLAS is based on techniques from our accepted submission at OSDI'24.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Ivy is an open-source machine learning framework that enables users to convert code between different ML frameworks and write framework-agnostic code. It allows users to transpile code from one framework to another, making it easy to use building blocks from different frameworks in a single project. Ivy also serves as a flexible framework that breaks free from framework limitations, allowing users to publish code that is interoperable with various frameworks and future frameworks. Users can define trainable modules and layers using Ivy's stateful API, making it easy to build and train models across different backends.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

PyCM is a Python library for multi-class confusion matrices, providing support for input data vectors and direct matrices. It is a comprehensive tool for post-classification model evaluation, offering a wide range of metrics for predictive models and accurate evaluation of various classifiers. PyCM is designed for data scientists who require diverse metrics for their models.

TokenFormer is a fully attention-based neural network architecture that leverages tokenized model parameters to enhance architectural flexibility. It aims to maximize the flexibility of neural networks by unifying token-token and token-parameter interactions through the attention mechanism. The architecture allows for incremental model scaling and has shown promising results in language modeling and visual modeling tasks. The codebase is clean, concise, easily readable, state-of-the-art, and relies on minimal dependencies.

Janus is a series of unified multimodal understanding and generation models, including Janus-Pro, Janus, and JanusFlow. Janus-Pro is an advanced version that improves both multimodal understanding and visual generation significantly. Janus decouples visual encoding for unified multimodal understanding and generation, surpassing previous models. JanusFlow harmonizes autoregression and rectified flow for unified multimodal understanding and generation, achieving comparable or superior performance to specialized models. The models are available for download and usage, supporting a broad range of research in academic and commercial communities.

LLMVoX is a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming Text-to-Speech (TTS) system designed to convert text outputs from Large Language Models into high-fidelity streaming speech with low latency. It achieves significantly lower Word Error Rate compared to speech-enabled LLMs while operating at comparable latency and speech quality. Key features include being lightweight & fast with only 30M parameters, LLM-agnostic for easy integration with existing models, multi-queue streaming for continuous speech generation, and multilingual support for easy adaptation to new languages.

This project focuses on implementing Sparse Autoencoders (SAEs) for mechanistic interpretability in Large Language Models (LLMs) like Llama 3.2-3B. The SAEs aim to untangle superimposed representations in LLMs into separate, interpretable features for each neuron activation. The project provides an end-to-end pipeline for capturing training data, training the SAEs, analyzing learned features, and verifying results experimentally. It includes comprehensive logging, visualization, and checkpointing of SAE training, interpretability analysis tools, and a pure PyTorch implementation of Llama 3.1/3.2 chat and text completion. The project is designed for scalability, efficiency, and maintainability.

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

Friendly transpiler for music coding.

Public transport authorities collect data on travel patterns, fares, and sometimes personal details of passengers, necessitating strong privacy measures.

The Vacation Planning Assistant is a digital tool for easy and efficient travel planning. It offers personalized itineraries, hotel and transport booking, travel alerts, a user-friendly interface, budget management, and 24/7 support. It learns from user feedback for future trips.

Coconut API Guide

An expert on EMT roles, emergency services, and training, with a medical tone.

Expert on oil industry, extraction methods, and environmental impact.