Search Results for "which" - Page 8
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Showing 369 open source projects for "which"
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WhisperJAV
A subtitle generator for Japanese Adult Videos.
A subtitle generator for Japanese Adult Videos. Transformer-based ASR architectures like Whisper suffer significant performance degradation when applied to the spontaneous and noisy domain of JAV. This degradation is driven by specific acoustic and temporal characteristics that defy the statistical distributions of standard training data. -
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Coqui TTS
A deep learning toolkit for Text-to-Speech, battle-tested in research
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. TTS comes with pre-trained models, tools for measuring dataset quality and is already used in 20+ languages for products and research projects. High-performance Deep Learning models for Text2Speech tasks. Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). Speaker Encoder to compute speaker embeddings... -
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AIStarter
AlStarter-Your platform for AI project management
...Out-of the box The biggest highlight is out-of-the-box , just one click to complete the environment testing , deployment , program installation and optimization . Regardless of which operating system you are using, you can easily zero configuration to start using a variety of powerful AI open source projects. Packing and Sharing AIStarter excels in intelligent AI project management, offering users seamless one-click download, installation, and usage. Additionally, users have the flexibility to package projects themselves, enabling easy sharing and collecting of favorite projects. -
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...It would help us best, if you can also add a hyperlink to our site http://www.bitfarm-archiv.com. By this you help the software to gain a better presence in the web which helps distribute it. This, however, will allow us to acquire more enterprise customers which gives us more resources, e.g. for further development of the GPL version.
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MMEditing
MMEditing is a low-level vision toolbox based on PyTorch
...With elaborate designs of the new framework and careful implementations, hope MMEditing could provide a better experience. When installing PyTorch in Step 2, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations. -
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Astrape
Optical-packet node transceiver frequency allocation
...Then, configuration of the local transceiver laser frequencies of the controlled pluggable devices takes place, for facilitating the connectivity in-between the ROADM network. Also, the agent records and reports back telemetry data (feedback) which is used by the PacketCTL's resource-allocating mechanism to improve efficiency within the network topology. -
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Mice MX OS speech to text Voice Control
Mice speech to text with MX Cinnamon OS ISO
Note about this image This image contains a system based on Linux MX, which was created to improve accessibility within the Linux environment. The distribution uses the Cinnamon desktop interface, which is configured to be operated using voice commands and outputs. The user interface and the control of your own devices and home automation systems can be customized and extended. The voice control program MiceStTM.py was developed to enable easy adaptation to other languages. ... -
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Style Aligned
Official code for Style Aligned Image Generation via Shared Attention
...This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as objects or attributes change. The result is more consistent edits across a set, which is crucial for workflows like product variations, character sheets, or brand-coherent art. The repository provides reproducible scripts, reference prompts, and guidance for tuning strengths so users can dial in subtle retouches or bolder substitutions. Because it builds on widely used diffusion checkpoints, creators can integrate it without training or dataset collection. -
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OpenAssistant
Chat-based assistant that understands tasks
...Open Assistant is a project organized by LAION and individuals around the world interested in bringing this technology to everyone. The code and models are licensed under the Apache 2.0 license. Open Assistant will be free to use and modify. There will be versions which will be runnable on consumer hardware. You do not need to run the project locally unless you are contributing to the development process. The website link above will take you to the public website where you can use the data collection app. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. ... -
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TextGen
textgen, Text Generation models
...This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with synonyms, random word insertion, deletion, replacement, etc. method, generating new text and implementing text augmentation This project realizes the back translation function based on Baidu translation API, first translate Chinese sentences into English, and then translate English into new Chinese. This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the... -
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Detectron
FAIR's research platform for object detection research
...The framework emphasized a clean configuration system, strong baselines, and a “model zoo” so researchers could compare results under consistent settings. It includes training and evaluation pipelines that handle multi-GPU setups, standard datasets, and common augmentations, which helped standardize experimental practice in detection research. Visualization utilities and diagnostic scripts make it straightforward to inspect predictions, proposals, and losses while training. Although the project has since been superseded by Detectron2, the original Detectron remains a historically important, reproducible reference that still informs many productions. -
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Stable Diffusion in Docker
Run the Stable Diffusion releases in a Docker container
Run the Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint. Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a suitable GPU you can set the options --device cpu and --onnx instead. Since it uses the model, you will need to create a user access token in your Huggingface account. ... -
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funNLP
Resources, corpora, and tools for Chinese natural language processing
...The project is highly community-oriented, frequently updated with contributions and new resources, and it’s widely used in both academic and applied NLP research. Its value lies in providing not just tools but also curated, domain-specific data, which can be hard to find elsewhere. -
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Nougat
Implementation of Nougat Neural Optical Understanding
...It combines object-centric modules with transformer-based reasoning to propose, refine, and render scenes in a generative pipeline. The architecture allows you to specify or prompt a layout (which objects should be where) and then the model fills in appearance, context, lighting, and relations coherently. The design supports interactive editing: you could adjust object positions or types and have the model adapt generation accordingly. Because it integrates structured layout reasoning, Nougat tends to produce more compositional and controllable results than purely unconstrained generative models. -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. ... -
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which significantly improves the model's ability to understand and execute instructions. -
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VATSG
Video automatic transcribe and translated subtitle generator
It generates srt format subtitle from videofile which can be any source language that whisper support , and then make translated subtitle file of your target language which deepl support. This is the subtitle generator(VATSG) which use [moviepy](https://github.com/Zulko/moviepy) to generate mp3 and then use [faster-whisper](https://github.com/guillaumekln/faster-whisper) to get text recognition and then use deepl-api to generate your target language subtitle file(srt format) If you are a general user who want to view any video file and mp3 file to your language, It will provide way. ... -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
...The modular design of MMOCR enables users to define their own optimizers, data preprocessors, and model components such as backbones, necks and heads as well as losses. Please refer to Getting Started for how to construct a customized model. The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints. -
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
...In a nutshell, Flash is the production-grade research framework you always dreamed of but didn't have time to build. All data loading in Flash is performed via a from_* classmethod on a DataModule. Which DataModule to use and which from_* methods are available depends on the task you want to perform. For example, for image segmentation where your data is stored in folders, you would use the from_folders method of the SemanticSegmentationData class. Our tasks come loaded with pre-trained backbones and (where applicable) heads. You can view the available backbones to use with your task using available_backbones. ... -
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Repo of Tree of Thoughts (ToT)
Implementation of "Tree of Thoughts
...This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem-solving. ToT allows LMs to perform deliberate decision-making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
...Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
...There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case.
